High School Students' Perceptions of Their Yearlong CASE Experience

Jonathan J. Velez, Oregon State UniversityMisty D. Lambert, Oregon State University

Kristopher M. Elliott, Oregon State University

AbstractThe purpose of this study was to begin examining the impact of the Curriculum for Agricultural Science Education (CASE). Under development since 2008, the curriculum is intended to integrate core academics and Science, Technology, Engineering, and Math (STEM) into agricultural education programs. This longitudinal descriptive correlational study (N = 173) sought to examine the perceptions of students enrolled in a CASE course specific to the constructs of critical thinking, task value, autonomy, science self-efficacy, and cognitive engagement. Results revealed no significant differences in construct means between points of assessment. Correlation of the constructs of interest with student characteristics revealed small correlations between gender, English Language Learner status, and activity in the FFA with task value, autonomy, science self-efficacy and cognitive engagement. Conclusions and recommendations are discussed in light of both the findings and the exploratory nature of this study.

Introduction

Agricultural Education, vibrant and active in the United States since the early 1900s, has encountered many calls for change specific to the curricula and nature of agricultural education coursework. In 1988, Agricultural Education was spurred to respond to the A Nation at Risk (1983) publication and the response came in the form of a book entitled Understanding Agriculture: New Directions for Education. The Green Book, as it is sometimes referred to by agriculture educators, produced several findings and recommendations by the National Research Council to ensure a bright future for Agricultural Education. Two significant recommendations of the committee were their calls for new career opportunities in the agricultural industry beyond production agriculture, and major revisions in the current curricula with more emphasis on agricultural sciences, agribusiness, marketing, and food production (National Research Council, 1988).

More recently, the No Child Left Behind Act and the 2006 Perkins Act have placed an increased focus on the integration of academics into the curriculum of Career and Technical Education programs. In 2006, the Association for Career and Technical Education (ACTE) outlined several main themes in the 2006 Perkins Act. The final theme encouraged academic and technical integration within CTE programs. This emphasis, while present in prior authorizations of the bill, was strengthened in the 2006 version, and demands that all CTE programs, including agriculture, increase their level of academic rigor, in addition to expanding their cooperation with core content teachers. The act goes on to address professional development as one potential means to attain increased academic integration within the CTE arena (109th U.S. Congress, 2006).

In Agricultural Education in 2008, The National Council for Agricultural Education spearheaded the development of national agriculture content standards. The new standards included a crosswalk to the national core content standards. The identification of the two sets of standards provided teachers a framework for teaching core content in their agriculture classrooms. During the same time, the National Council for Agricultural Education led the development of the Curriculum for Agricultural Sciences Education, or CASE (CASE, 2011).

The CASE philosophy is to “empower the student by providing students an active role in their learning rather than learning being a product of teacher led instruction” (CASE, 2011p. 4) and the curriculum is intended to be an inquiry-based, scientific approach to teaching in school-based secondary agriculture programs than is aligned with Science, Technology, Engineering and Math (STEM) (CASE, 2011). In addition to curriculum, the CASE support system is intended to provide professional development, assessment and certification (CASE, 2011).

The CASE curriculum purports to be a “system of instructional support for the classroom teacher like no other resource in agricultural education today” (CASE 2011, p. 1). As agricultural education continues to wrestle with the integration of academics and seeks to identify effective methods thereof, research examining CASE is warranted. The 2011-2015 National Research Agenda calls for research that examines the “design, development, and assessment of the meaningful learning environments which produce positive learner outcomes” (Doerfert, 2011, p. 9). Specifically, priority four seeks research which can promote “meaningful, engaged learning in all environments” (Doerfert, 2011, p. 9), and priority five states that “Agricultural education has the obligation to show that its curriculum can be used to meet the academic challenges of today’s school system while preparing students for a career in the agriculture industry,” (p. 26). The current study seeks to add to the knowledge base regarding the relatively new CASE curriculum.

Theoretical Foundation

The theoretical foundation for this research was based on the concept and theories surrounding student interest. Some interest models focus solely on academic interest (Model of Domain Learning, Alexander, 1997, 2004) and some break up interest into multiple phases (Four-Phase Model of Interest Development, Hidi & Renninger, 2006). One particularly applicable model, upon which the present research was grounded, is the Person-object Theory of Interest (POI) espoused by Krapp and Fink (1992). The POI model focuses on both the affective and cognitive aspects of interest. Both elements were deemed to be of importance to the present research and thus the researchers considered both cognitive and affective elements when determining research constructs.

At its core, the POI separates interest into the person and the context. The person contributes individual interest, theorized as a dispositional trait, and that interest is operationally defined as actualized individual interest. The context portion is just as it sounds, a reference to the context stimulating the interest. If interest is therefore contextual, it is defined as situational interest (Krapp & Fink, 1992; Krapp, 2002). Individual interest, in this study, was theorized to pertain more to critical thinking and task value while situational interest was theorized to connect to autonomy, science self-efficacy and cognitive engagement.

The POI has also been coupled with the three basic psychological needs identified in the Self-determination theory of Deci and Ryan (2000) and Ryan and Deci (2000). Deci and Ryan advocated for the importance of competence, autonomy, and social-relatedness in the motivation of students. Krapp (2002) suggested that in order to stimulate and maintain interest, all three psychological needs would need to be met. In deference to the work of Deci, Ryan, and Krapp, one of the areas of consideration for this research was the examination of student perceptions of autonomy.

This research, grounded in the POI and Self-determination Theories, sought to examine some of the cognitive and affective elements of interest generation. Since this research examines a new curriculum (CASE) and there is very little record of prior research, the authors purposefully selected constructs that were broad and inclusive—constructs perceived to capture levels of student interest and autonomy. The constructs assessed in this study were operationally defined as critical thinking, task value, autonomy, science self-efficacy, and cognitive engagement. The authors recognize that each of these constructs, in and of itself, are distinctive and worthy of individual analysis. In fact, many have and are currently being researched in an intensive manner. However, in the current research, in an effort to cast a wide net and begin a broad examination of CASE, the authors chose to initially examine all five constructs.

Potentially, future studies may dive further into the individual constructs once there is more research available concerning the CASE curriculum. While research has been conducted on the integration of science into Agricultural Education (Connors & Elliot, 1993, 1994; Miller, 2000; Myers & Thompson, 2009; Thompson & Balschweid, 1998, 1999), there is very little scholarly research specific to the CASE curriculum.

Conceptual Framework

Critical Thinking

The term critical thinking has several definitions, but for the purpose of this research, the authors have adopted the Scriven and Paul (2004) definition of critical thinking as cited and modified by Peirce (2005) as,

sound thinking within a discipline that is needed and relied upon by practitioners in that discipline—thinking that is accurate, relevant, reasonable, and rigorous, whether it be analyzing, synthesizing, generalizing, applying concepts, interpreting, analyzing, evaluating, supporting arguments and hypotheses, solving problems, or making decisions (p. 81).

Critical thinking has been linked with good grades (Williams, Oliver, Allin, Winn, & Booher, 2003), management of interpersonal relationships (Kegan, 1994), leadership (Heifitz, 1994) and the ability to manage complex problems (Kolb, 1984).

Task Value

The task value construct was intended to assess the motivational value students place in what they are learning. Eccles et al. (1983) and Wigfield and Eccles (2000) identified task value as a critical determinant for student task engagement. Students who possess a greater degree of task value will be more likely to engage and persist in a given task (Pintrich, 1994). Task value therefore, provides the impetus and is the catalyst for attempting a task. The CASE curriculum encourages students to guide their learning in a self-directed manner, thus, student task value would presumably be a meaningful construct.

Autonomy

Autonomy is a measure of the students’ sense of the classroom climate. Typically a classroom can either be controlling or autonomy supportive. The designers of the CASE curriculum espouse it as a student-direct curriculum which allows students to pursue answers through inquiry learning. An autonomy supportive environment has been shown to increase perceived confidence and enhance intrinsic motivation (Deci, Vallerand, Pelletier, & Ryan, 1991). Students who interact with non-autonomy supportive teachers (controlling) tend to assume passive, cognitively disconnected, extrinsically motivated classroom roles (Reeve, 2002).

Science Self-efficacy

Self-efficacy is commonly defined as judgments about one’s ability to organize and execute specific courses of action (Bandura, 1997). It is a motivational construct that has been directly linked with academic achievement — both short-term and long-term (Pajares, 1996; Schunk, Pintrich, & Meece, 2008). Bandura (1997) believed that self-efficacy would influence the choices individuals make in terms of goals, effort, and persistence. All of which can shape students’ intent to enter a science-related career field. Self-efficacy has also shown to be positively correlated with student goals and persistence (Bandura & Locke, 2003; Britner & Pajares, 2006; Eccles, 1994).

Cognitive Engagement

The cognitive engagement construct was a composite construct originating from a subset of the Motivating Tasks statements identified by Green, Miller, Crowson, Duke, and Akey (2004). Green et al. (2004) examined how classroom structures (tasks, autonomy support, mastery and evaluation) influenced student self-efficacy, achievement goals and instrumentality. The authors operationally defined the construct to represent the level of cognitive engagement and processing of the participants. Students who engage in a more meaningful manner (elaboration) will be more likely to form a more meaningful mental representation (Weinstein & Mayer, 1986).

Purpose and Research Objectives

The overall purpose of this longitudinal assessment was to examine student perceptions of critical thinking, autonomy, task value, science self-efficacy, and student cognitive engagement

during a yearlong CASE course. In addition, selected demographic information was collected for the purposes of further understanding the students enrolled in the selected CASE courses. The research objectives are as follows:

1.) Identify the demographic characteristics of students enrolled in CASE courses.2.) Identify the means of the constructs of interest for all the first, second, and third points of

assessment during the yearlong CASE experience.3.) Examine the relationships between constructs and respondent characteristics.

Methods and Procedures

It is important to note that this study is part of a larger, comprehensive study on the CASE program. The population for this research consisted of four selected high schools within Oregon. This purposive sample was selected based on recent CASE training of the teachers and their intent to teach CASE courses during the year of study. According to Ary, Jacobs, Razavieh and Sorensen (2006), a purposive sample is one in which, “sample elements judged to be typical, or representative, are chosen from the population” (p. 174). The researchers identified one small rural school, two large suburban schools, and one large urban school that fit the criteria of recent CASE training and intent to implement CASE for the duration of the study. As a result of the sampling method, and due to the many extraneous variables that comprise high school education, the results of this research are generalizable only to the respondents of this study.

In an effort to assess student changes over time, the researchers conducted a longitudinal, year-long multipoint assessment. Participants were assessed in September, December and May during the year they were enrolled in a CASE course. Of the 353 eligible students in this study, the first collection had a total of 276 (78.19%) respondents, the second had 268 (75.92%), and the third had 231 respondents (65.44%).

Instrumentation

The research instrument was intended to examine five distinct constructs of critical thinking, autonomy, task value, science self-efficacy, and student cognitive engagement as well as capture selected respondent characteristics. The instrument descriptions and reliabilities are described in detail and the reliabilities are presented in a range due to the nature of a longitudinal study and several points of assessment.

Critical thinking. Critical thinking was assessed using the Motivated Strategies for Learning Questionnaire

(MSLQ) (Pintrich, Smith, García, & McKeachie, 1991). This questionnaire contained five questions, scaled from 1 (strongly disagree) to 6 (strongly agree), directed at assessing student critical thinking. Examples include “I treat the course material as a starting point and try to develop my own ideas about it,” and “whenever I read or hear an assertion or conclusion in this class, I think about possible alternatives.” This instrument has been commonly used with both college and high school students and generally reports reliabilities between .70 and .84 (Duncan & McKeachie, 2005). The reliabilities for this current assessment ranged from .72-.75.

Task value.

The task value assessment was also a component of the MSLQ (Pintrich et al., 1991), and was intended to assess task value in light of interest (intrinsic) value, importance (attainment) value, and utility value. The six questions regarding task value were scaled from 1 (strongly disagree) to 6 (strongly agree). Example questions include “I think I will be able to use what I learn in this course in other courses,” and “I am very interested in the subject matter of this course.” Similar to the critical thinking assessment, this instrument has been widely used with both college and high school students and reports reliabilities between .83-.90 (Duncan & McKeachie, 2005). In the current study, the Cronbach reliability estimates ranged from .86-.89.

Autonomy.The researchers utilized the Learning Climate Questionnaire (LCQ) developed by Deci

and Ryan (1985) and revised by Deci et al. (1991). The LCQ short version consisted of six questions scaled from 1 (strongly disagree) to 6 (strongly agree). Example questions include “I feel that my instructor provides me choices and options,” and “My instructor listens to how I would like to do things.” Previous research reported reliability coefficients generally ranging above .90 (Black & Deci, 2000; Williams & Deci, 1996). In the current study, the Cronbach reliability estimates ranged from .88-.90.

Science Self-efficacy.To measure science self-efficacy, the Science Lab Self-Efficacy instrument developed by

Britner (2000) was utilized. The instrument consisted of six questions scaled from 1 (strongly disagree) to 6 (strongly agree). Example questions include “I am confident in my ability to identify sources of error that might affect the results of a science lab activity,” and “I am confident in my ability to draw correct conclusions from scientific projects.” Previous research reported reliabilities of .84 (Britner, 2000). The current research revealed Cronbach reliability estimates ranging from .86-.90.

Cognitive Engagement.The cognitive engagement instrument sought to measure the level at which the students

were cognitively engaged in the CASE material. The instrument was adapted from a subset of the Motivating Task statements identified by Greene et al. (2004). The motivating task questions were selected based on the perceived relevance to cognitive engagement. The seven questions were scaled from 1 (strongly disagree) to 6 (strongly agree). Examples include “In this class, the teacher emphasizes learning the material to gain understanding,” and “In this class, the teacher introduces material in ways that are interesting to the students.” Previous reliability estimates from the complete scale were .85 (Greene et al., 2004). The current study revealed Cronbach reliability estimates ranging from .89-.91.

Data Analysis

Correlational analysis, utilizing Hopkins (1997) magnitude of correlation descriptors, was performed on summated means in order to address relationships between the constructs of interest. Statistical significance was reported; however, based on the nonrepresentative sampling method and the influence of the size of the sample, statistical significance should be interpreted with caution. King and Minium advised to, “Be careful, a significant r does not mean that the association is important. The expression “significant correlation” means only that Ho: ρ= 0 has

been tested and rejected, and “nonsignificant correlation” means only that Ho: ρ= 0 has been tested and retained—nothing more, nothing less” (p. 290). The effect size was also reported using Hopkins descriptors.

Results

Research objective one sought to determine the characteristics of students enrolled in the CASE courses. The overall student population for these studies was 353 students. While this was the overall number of all participants, it should be noted that the length of this assessment (one year) made it difficult to retain all students. Some students transferred, dropped, or simply chose not to fill out the research instruments. In addition, for the students engaged in the longitudinal study, there were three primary points of assessment and the numbers of respondents varied. Of the entire population of 353 students, there were 173 students who completed all three points of assessment.

The 353 total respondents to these assessments indicated coming from four different schools: Two larger schools with CASE enrollments of 125 and 136 students and two smaller schools with 69 and 23 students. Of the 353 students engaged in this assessment, 315 students indicated gender, with 155 (43.78 %) females and 160 (45.19 %) males. There were 70 freshmen (19.77 %), 70 sophomores (19.77 %), 95 juniors (26.83 %), and 80 seniors (22.59 %) enrolled in CASE courses. Of these students, 47 (13.27 %) were on an Individualized Education Plan (IEP), 15 (4.23 %) were on a 504 plan, 45 (12.71 %) were English Language Learner (ELL) students, and 13 (3.67 %) were Talented and Gifted (TAG) students. There were 108 (30.50 %) students who were actively involved in FFA, 230 (64.97 %) who were receiving science credit, and 26 (7.34 %) who were receiving College Now credit.

The population consisted of three different CASE courses: Introduction to Agriculture, Food, and Natural Resources (AFNR), Principles of Agricultural Science - Animal, and Principles of Agricultural Science - Plant. The AFNR courses had 87 student participants, the Animal course had 59 students, and the Plant course had 207 students. Science credit was received by 124 students from the Plant course and 59 students from the Animal course. No students from the AFNR course received science credit.

The intent of the second research objective was to examine the construct means for the five constructs of interest. Figure 1 depicts the construct means for autonomy, task value, and critical thinking for all three points of assessment. The overall means are only reflective of those students who completed all three points of assessment. Figure 2 depicts the construct means for science efficacy and cognitive engagement. Student perceptions in all five construct areas remained relatively unchanged throughout the exposure to the CASE curriculum. All constructs evidenced a slight decrease ranging from science efficacy (.11) to autonomy (.26). The slight change did not yield any statistically significant differences in the mean scores.

Assessment 1 Assessment 2 Assessment 31

1.6

2.2

2.8

3.4

4

4.6

5.2

Student Perceptions of Autonomy, Task Value, and Critical Thinking

AutonomyTask ValueCritical Thinking

Figure 1. Student Perceptions of Autonomy, Task Value and Critical Thinking (N = 173)

Assessment 1 Assessment 2 Assessment 31

1.6

2.2

2.8

3.4

4

4.6

5.2

Student Perceptions of Science Efficacy and Cognitive Engagement

Cognitive EngagementScience Efficacy

Figure 2. Student Perceptions of Science Efficacy and Cognitive Engagement (N = 173)

The third research question was intended to examine the relationships between the constructs of interest and student characteristics. The characteristics included student gender, student grade, Individual Education Plan (IEP), 504 Plan, English Language Learner (ELL), and Talented and Gifted (TAG) status. In addition, the researchers also examined FFA participation levels and whether or not the students were receiving science credit. Table 4 examines the relationships between grade level and the constructs of interest. Hopkins (1997) correlation coefficients descriptors were utilized. Hopkins labeled his indicators as: .00-.10 = trivial, .10-.30 =small, .30-.50 =moderate, .50-.70 = large, .70-.90 = very large, and .90-1.00 = nearly perfect. Results indicated a positive relationship between autonomy and grade level with a small effect size. Task value, critical thinking, science efficacy, and cognitive engagement all yield trivial effect sizes.

Table 4Spearman’s rho correlations between grade level and the constructs of interest (N = 173)Ordinal Variable Interval Variable Value

Grade LevelX Autonomy .12b

X Task Value -.01a

X Critical Thinking -.06a

X Science Efficacy .02a

X Cognitive Engagement .02a

Note. All correlations and effect sizes are less than r =.20 (<.04). Grade level was coded 1 = Freshman, 2 = Sophomore, 3 = Junior, 4 = Seniora =trivial, b =small

Table 5 examines the point-biserial correlations between the dichotomous nominal variables and the constructs of interest. Individualized Education Plans (IEP’s) evidence one negative statistically significant association with science efficacy (rpb = -.19, M = 4.50, SD = .63) indicating that students with IEP’s had less science efficacy. TAG correlations revealed two significant correlations between critical thinking (rpb = .15, M = 4.21, SD = 0.60) and cognitive engagement (rpb = .17, M = 4.72, SD = 0.64). Thus, students who identified as TAG evidenced a stronger relationship to both critical thinking and cognitive engagement.

Table 5Point-biserial correlations between dichotomous nominal and interval variables (N = 173)

Autonomy Task ValueCritical

ThinkingScience Efficacy

Cognitive Engagement

Gender rpb -.25* -.21* -.08 -.15* -.22*Sig. .00 .00 .25 .04 .00

IEP rpb -.12 -.10 -.04 -.19* -.09Sig. .18 .21 .61 .01 .22

504 rpb .08 -.00 -.04 .00 .07Sig. .30 .96 .63 .98 .35

ELL rpb -.21* -.24* -.10 -.19* -.26*Sig. .00 .00 .18 .01 .00

TAG rpb .15 .12 .15* .08 .17*Sig. .05 .10 .05 .29 .02

Active in FFA

rpb .25* .21* .15 .24* .21*Sig. .00 .00 .05 .00 .00

Science Credit

rpb .16* .00 -.06 .02 .02Sig. .04 .93 .47 .82 .79

Note. All effect size descriptors for statistically significant correlations fall within the small (.10-.30) designation. All dichotomous variables were coded 0 = no, 1 = yes. Gender was coded 0 = females, 1 = males.* Correlation is significant at the 0.05 level (2-tailed).

Gender, ELL status, and activity level in the FFA all show statistically significant correlations in four constructs—autonomy, task value, science efficacy, and cognitive engagement. As a result, to further describe the relationships, tables 6-8 detail the mean scores in the four constructs for gender, ELL, and active FFA participation.

Table 6Gender Comparisons by Constructs

GenderMales (n = 87) Females (n = 86)

Construct M SD M SDCritical Thinking 4.09 0.65 4.24 0.55Autonomy 4.29 0.67 4.64 0.58Task Value 4.21 0.69 4.63 0.72Science Efficacy 4.31 0.66 4.64 0.58Cognitive Engagement 4.43 0.68 4.80 0.56

Table 7ELL comparisons by construct

English Language Learners (ELL) ELL (n = 10) Non-ELL (n = 163)

Construct M SD M SDCritical Thinking 3.96 0.97 4.23 0.57Autonomy 4.08 0.79 4.66 0.62Task Value 3.79 1.07 4.52 0.67Science Efficacy 4.02 0.90 4.53 0.60Cognitive Engagement 4.06 0.99 4.76 0.59

Table 8FFA participation levels by construct

Construct

Active in FFA Active (n = 83) Inactive (n = 90)

M SD M SDCritical Thinking 4.30 0.52 4.12 0.66Autonomy 4.80 0.59 4.47 0.67Task Value 4.63 0.68 4.33 0.72Science Efficacy 4.66 0.59 4.36 0.64Cognitive Engagement 4.86 0.55 4.59 0.68

Conclusions, Implications, and Recommendations

The participants in this study included a relatively heterogeneous mix of grade levels. Most of the participants were enrolled in the CASE Plant Sciences course (n = 207) and 124 of those students were receiving science credit. The respondents were evenly mixed with 155 females and 160 males in the sample.

The second research objective sought to examine the mean scores for all three points of assessment. Results indicated there was no statistically significant mean difference between the first, second, and third assessment points on any of the constructs of interest. From a practical sense, the students only varied slightly between points of assessment with summated mean scores dipping from first through third points of assessment. In analyzing the results, it is clear that CASE implementation, and the potential effects on student variables, is very much context specific. The school, teachers, materials, students, class size, and a host of other variables potentially interact throughout the year to impact the autonomy, critical thinking, task value, science efficacy and cognitive engagement of the students. With this in mind, the research separated the mean scores for the four high schools to see if there was any difference in the mean scores. Two of the schools showed slight gains in all the constructs of interest and two of the schools showed decreases in all the constructs of interest. Once the scores were averaged together any potential variations by school were hidden.

The researchers recommend further research which is able to control for some of the confounding variables. In particular, as more teachers move to adopt and implement CASE in the classroom, more potential sites of assessment will be available. This will allow for options in selection method and the potential to control some of the confounding variables.

The third objective was to examine the relationships between the constructs and the respondent characteristics. Results indicated trivial to small correlations between grade level and the constructs of interest. Results of the correlations between the constructs of interest and the other dichotomous variables yielded similar trivial to small correlations. While statistical significance among correlations are to be taken with caution (King & Minium, 2008), three characteristics yielded statistically significant results in four of the five construct areas. Gender, ELL status, and whether or not the student was active in the FFA all showed small correlations with autonomy, task value, science efficacy, and cognitive engagement. As might be expected, the construct of critical thinking showed only one statistically significant correlation and that was with students in the Talented and Gifted (TAG) category.

An analysis of the mean scores by gender, ELL, and FFA activity level provided further clarity as to the correlations. Students who were active in FFA (n = 108) showed slightly higher mean scores in autonomy, task value, science efficacy, and cognitive engagement. FFA purports to be a co-curricular (Talbert, Vaughn, & Croom, 2006) program and thus it is positive to see that students active in FFA perceive themselves to be more “engaged” in their CASE coursework. Relating to the Person-object Theory of Interest (POI), the FFA means were related more with situational variables of interest. The only individual interest variable that was related to FFA involvement was that of task value. Further research should examine with detail the student perceptions of academic engagement from those who are active in FFA and those who are not. Involvement in FFA may be an important situational interest component to engaging students in CASE and non-CASE courses. In addition, the result also point towards the

involvement of the Self-determination theory and the social-relatedness components of student motivation. Further research should consider the role FFA involvement may play in overall student motivation.

Mean scores by gender revealed higher mean scores for females than males. Across the board, females perceived themselves as higher in autonomy, critical thinking, task value, science efficacy and cognitive engagement as compared to their male counterparts. It is important to note that the results may be influenced by the females’ levels of perceptivity. Further research should examine the differences between males and females with the goal to identify potential areas for intervention or key points in the developmental process. Based on the POI model, the male and female students had the potential to be influenced by both individual and situational contexts. The higher female scores include the researcher identified constructs relating to individual interest. This has implications for females in that, “individual interest is used as a predictor of academic achievement” (Krapp, 2002, p. 407). If key points of interest are identified and connected with the CASE curriculum, instructional interventions may be able to address and perhaps enhance the mean scores of both male and female students.

Students who were ELL also evidenced lower mean scores compared to their non-ELL counterparts with the largest mean differences in task value and cognitive engagement. These results are not surprising when considering that ELL students may struggle in their understanding and processing of the English language. While the CASE curriculum is reading-heavy, it is also very hands-on and the pairing of these two elements may influence the mean scores. Further research should examine the task value and cognitive engagement of ELL students in other classrooms and determine if the CASE curriculum is impacting their task value or cognitive engagement.

It is important to recognize that this research was not intended to examine the CASE curriculum directly or assess academic gains. It is simply a longitudinal assessment of student perceptions while engaged in a CASE course. Since CASE itself is new, and very little research exists on the curriculum, the researchers attempted to begin a broad examination of CASE and highlight potential areas for further research. As researchers move forward to begin examining CASE, they must keep in mind, identify, and control for the extraneous variables associated with conducting social science research in an active high school classroom.

References

109th United States Congress. (2006). Carl D. Perkins Career and Technical Education Improvement Act of 2006. Retrieved from http://www.acteonline.org/perkins.aspx

Alexander, P. A. (1997). Mapping the multidimensional nature of domain learning: The interplay of cognitive, motivational, and strategic forces. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in motivation and achievement (Vol. 10, pp. 213–250). Greenwich, CT: JAI.

Alexander, P. A. (2004). A model of domain learning: Reinterpreting expertise as a multidimensional, multistage process. In D. Y. Dai & R. J. Sternberg (Eds.), Motivation, emotion, and cognition: Integrative perspectives on intellectual functioning and development (pp. 273–298). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Ary, D., Jacobs, L. C., Razavieh, A., & Sorensen, C. (2006). Introduction to research in education. Belmont, CA: Thompson Wadsworth.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Bandura, A., & Locke, E. A. (2003). Negative self-efficacy and goal effects revisited. Journal of Applied Psychology, 88, 87–99. doi:10.1037/0021-9010.88.1.87

Black, A. E., & Deci, E. L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84, 740-756. doi:10.1002/1098-237X(200011)84:6<740::AID-SCE4>3.0.CO;2-3

Britner, S. L. (2000). Teaching multiple intelligences in a seventh grade science classroom. In R. Sheppard (Ed.), Perspectives from the Classroom (pp. 41-44). Atlanta, GA: Georgia Middle School Association.

Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43, 485–499. doi:10.1002/tea.20131

Curriculum for Agricultural Science Education. (2011). Understanding the CASE model. Retrieved from http://www.case4learning.org/about-case/vision.html

Connors, J. J., & Elliot, J. F. (1993). The influence of agriscience and natural resources curriculum on students’ science achievement scores. Retrieved November 27, 2011 from http://www.eric.ed.gov/PDFS/ED383861.pdf

Connors, J. J., & Elliot, J. F. (1994) Teacher perceptions of agriscience and natural resources curriculum. Journal of Agricultural Education, 35(4), 15-19. doi:10.5032/jae.1994.04015

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self determination of behavior. Psychological Inquiry, 11, 227–268.doi:10.1207/S15327965PLI1104_01

Deci, E., Vallerand, R., Pelletier, L., & Ryan, R. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26, 325-346.doi:10.1080/00461520.1991.9653137

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Duncan, T. G., & McKeachie, W. J. (2005). The making of the Motivated Strategies for Learning Questionnaire. Educational Psychologist, 40(2), 117-128. doi:10.1207/s15326985ep4002_6

Eccles, J. S. (1994). Understanding women’s educational and occupational choices: Applying the Eccles et al. model of achievement-related choices. Psychology of Women Quarterly, 18, 585–609. doi:10.1111/j.1471-6402.1994.tb01049.x

Eccles J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). San Francisco, CA: W. H. Freeman.

Greene, B. A., Miller, R. B., Crowson, H. M., Duke, B. L., & Akey, K. L. (2004). Predicting high school students’ cognitive engagement and achievement: Contributions of classroom perceptions and motivation. Contemporary Educational Psychology, 29 (4), 462-482. doi:10.1016/j.cedpsych.2004.01.006

Heifitz, R. (1994). Leadership without easy answers. Boston: Harvard Business School Press.

Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111-127. doi: 10.1207/s15326985ep4102_4

Hopkins, W. G. (1997). New view of statistics. Retrieved November 23, 2011 from http://www.sportsci.org/resource/stats/effectmag.html

Kegan, R. (1994). In over our heads: The mental demands of modern life. Cambridge, MA: Harvard University Press.

King, B. M., & Minium, E. W. (2008). Statistical reasoning in the behavioral sciences (5th ed.). Hoboken, NJ: John Wiley & Sons.

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall.

Krapp, A. (2002). An educational-psychological theory of interest and its relation to self-determination theory. In E. Deci & R. Ryan (Eds.), The handbook of self-determination research (pp. 405–427). Rochester, NY: University of Rochester Press.

Krapp, A., & Fink, B. (1992). The development and function of interests during the critical transition from home to preschool. In K. A. Renninger, S. Hidi, & A. Krapp (Eds.), The role of interest in learning and development (pp. 397–429). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Miller, G., & American Association for Agricultural Education. (2000). 21st century research for agricultural education. Proceedings of the National Agricultural Education Research Conference, 27, 240-253. Retrieved from ERIC http://www.eric.ed.gov/PDFS/ED449351.pdf

Myers, B. E., & Thompson, G. W. (2009). Integrating academics into agriculture programs: A Delphi study to determine perceptions of the national agriscience teacher ambassador academy participants. Journal of Agricultural Education, 50(2), 77-88. doi:10.5032/jae.2009.02075

National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. Washington, DC: U.S. Department of Education.

National Research Council. (1988). Understanding agriculture: New directions for education. Washington, D.C.: National Academy Press.

Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66, 543-578. doi:10.3102/00346543066004543

Peirce, W. P. (2005). The year of critical thinking at Prince George's Community College: An integrated professional development program. New Directions for Community Colleges, 2005(130), 79–85. doi: 10.1002/cc.198

Pintrich, P. R. (1994). Continuities and discontinuities: Future directions for research in educational psychology. Educational Psychologist, 29, 37-148. doi: 10.1207/s15326985ep2903_3

Pintrich, P. R., Smith, D. A. F., García, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor: University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning.

Reeve, J. (2002). Self-determination theory applied to educational settings. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination research (pp. 183–203). Rochester, NY: University of Rochester Press.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78. doi:10.1037/0003-066X.55.1.68

Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd ed.). Upper Saddle River, NJ: Merrill/Prentice-Hall.

Talbert, B. A., Vaughn, R., & Croom, D. B. (2006). Foundations of agricultural education. Caitlyn, IL: Professional Educators Publications.

Thompson, G. W., & Balschweid, M. A. (1998, December). Agriculture teachers’ perceptions of integrating science in Oregon agricultural science and technology programs. Paper presented at the National Agricultural Education Research Meeting, New Orleans, LA. Retrieved from ERIC http://www.eric.ed.gov/PDFS/ED429169.pdf

Thompson, G. W., & Balschweid, M. M. (1999). Attitudes of Oregon agricultural science and technology teachers toward integrating science. Journal of Agricultural Education, 40(3), 21-29.

Weinstein, C. E., & Mayer, R. E. (1986). The teaching of learning strategies. In M. Wittrock (Ed.), Handbook of research on teaching (pp. 315-327). New York, NY: Macmillan.

Wigfield, A., & Eccles, J. S. (2002). The development of competence beliefs, expectancies for success, and achievement values from childhood through adolescence. In A. Wigfield & J. S. Eccles (Eds.), The development of achievement motivation (pp. 91–120). New York: Academic.

Williams, G. C., & Deci, E. L. (1996). Internalization of biopsychosocial values by medical students: A test of self-determination theory. Journal of Personality and Social Psychology, 70, 767-779. doi:10.1037/0022-3514.70.4.767

Williams, R. L., Oliver, R., Allin, J. L., Winn, B., & Booher, C. S. (2003). Psychological critical thinking as a course predictor and outcome variable. Teaching of Psychology, 30, 220-223. doi:10.1207/S15328023TOP3003_04

Teachers’ Perceptions of CASE

Misty D. Lambert, Oregon State UniversityJonathan J. Velez, Oregon State University

Kristopher M. Elliott, Oregon State University

Abstract

The purpose of this multiple case study was to explore how the implementation of the Curriculum for Agricultural Science Education (CASE) was impacting five teachers at four high schools in Oregon. Through the use of weekly journals, semi-structured interviews, and a focus group, researchers attempted to gain insight into how the teachers saw this new curriculum impacting their programs, their students and themselves. Five themes emerged from the study: a) Some teachers adapted more easily to the student-centeredness of the curriculum; b) teachers enjoyed having content available, but none of them made it all the way through the material; c) the teacher’s personality influenced the implementation of the curriculum; d) Teachers saw attending the institute as vital to their implementation of CASE; and, e) Implementing CASE allowed the teachers to refocus.

Introduction and Literature Review

In 1983, the National Commission on Excellence in Education issued a report known as A Nation at Risk, which argued that the American education system was in trouble. The response to this report from many states was to require more science and math for high school students. This focus on core subjects and increasing test scores in areas like math and science has led to decreased Career and Technical Education (CTE) enrollments at the secondary level (Camp & Heath-Camp, 2007) as students have less time to fit CTE courses into their schedules (Martin, Fritzsche, & Ball, 2006).

Actively involved in CTE reform efforts, the National Council for Agricultural Education established eight initiatives to facilitate the development of quality CTE programs. The third initiative, which called for a sequence of courses to enhance the delivery model of Agricultural Education, has led to the development of the Curriculum for Agricultural Science Education, also known as CASE (CASE, 2011). From their own documents, CASE purports to be the complete package of resources and claims to remove a lot of teacher stress by shifting the focus from preparation to instruction (CASE, 2011).

CASE was developed largely following the Project Lead the Way (PLTW) model. PLTW has approached reform through the integration of Science, Technology, Engineering and Math (STEM), project based learning, and rigorous academic content. PLTW currently impacts over 400,000 high school and middle school students in all 50 states. While the PLTWcurriculum is free, teachers are required to attend professional development activities and purchase equipment and technology to implement the program (PLTW, 2011) which tends to be the biggest barrier to implementation (Shields, 2007).

Like PLTW, CASE also requires 80 hours of professional development for each CASE course a teacher wants to offer in his or her program. CASE provides rigor in the agriculture curriculum through the alignment of national agriculture, science, math, and English language arts standards, while delivering curriculum using the same activity-, project-, and problem-based instructional framework that is the foundation of PLTW (CASE, 2011c). While some research has been conducted on the integration of science into Agricultural Education (Connors & Elliot, 1993, 1994; Miller, 2000; Myers & Thompson, 2009; Thompson & Balschweid, 1999), there is very little scholarly research specific to the new CASE curriculum.

This study attempts to address the National Research Agenda’s Priority Area 5: Efficient and effective agricultural education programs (Doerfert, 2011). The National Research Agenda states “Agricultural education has the obligation to show that its curriculum can be used to meet the academic challenges of today’s school system while preparing students for a career in the agricultural industry” (Doerfert, 2011, p. 26). Much like the assessment of literacy curriculum performed by Bellah and Dyer (2009), this paper is part of a larger study “which attempts to assess attitudes, concerns, usage levels, and innovative adaptations” of CASE teachers (p. 3).

Conceptual Framework

The Concerns-Based Adoption Model (CBAM) (Hall & Hord, 2001) is a conceptual framework which describes, explains, and predicts probable teacher concerns and behaviors throughout a change process. Here, the CBAM being applied to the change process of implementing the CASE curriculum within a high school agriculture program. Of particular interest in this study are the stages of concern, the levels of use, and the innovation configuration components of the model.

Figure 1. Concerns Based Adoption Model by Hall & Hord (2001)

Hall and Hord (2001) defined seven Stages of Concern that a person may experience when implementing change. Stage 0 is Awareness where the individual is concerned or involved with the innovation. Stage 1 is Informational and concerns the participant gaining more information about the innovation. Stage 2 is Personal and involves concerns about how the innovation relates to the individual. Stage 3 is Management and involves concerns about the mechanics of using or integrating the innovation. Stage 4 is Consequence and looks at concerns about the effect of the innovation on students. Stage 5 is Collaboration and concerns

User System Culture

Innovation users and non-users

Intervening

Innovation Configuration

Levels of Use

Stages of Concern

Probing

Change Facilitator

Team

Resource System

coordinating efforts in using the innovation with others. Finally, stage 6 is Refocusing and involves the exploration of other ways to utilize the innovation or improve upon the innovation.

Hall and Hord (2001) also defined eight Levels of Use. These levels focus on the behaviors in teachers that are or are not taking place in relation to implementation of a new curriculum (Willis, 1992). Applying Newhouse’s (2001) explanation, these levels are the phases through which a teacher would pass as they implement a curriculum and gain confidence in its use. These begin with Level 0 or Nonuse when the teacher has little or no knowledge of the curriculum. Level I Orientation is the point at which the implementer begins acquiring information about the curriculum. Level II is Preparation and involves preparing to use the curriculum. Level III is Mechanical and reflects a user focused on the mechanical day-to-day aspects of using the curriculum. Level IVA is Routine and has been reached when the implementer is comfortable with the curriculum with little preparation and they are not planning to change how the innovation is used. Level IVB Refinement is reached when the implementer is working to improve their personal use of the innovation. Level V Integration and has the teacher working with colleagues in a collaborative effort to use the curriculum. Lastly, level VI is Renewal and has the teacher re-evaluating the innovation and seeking to make major modifications. Hall and Hord (2001) indicated “in nearly all cases the innovation as operationalized by different users will vary along a continuum from being very close to what the developer had in mind to a distant zone where what is being done is nearly unrecognizable” (p. 39).

Purpose and Objectives

The purpose of this multiple case study was to understand the teachers’ experience during their first year of implementing the CASE curriculum within their Agricultural Education program. Specifically, the researchers sought to understand how the teachers saw CASE impacting (a) their total Ag Ed program, (b) their Ag Ed students, and (c) themselves as a teacher.

Methods and Procedures

Qualitative methods were chosen to investigate this problem because these methods allow the researcher to understand how people make sense of their world (Merriam, 2009). This type of research is more concerned with meaning than frequency (VanMaanen, 1979). Feagin, Orum, and Sjoberg (1991) argue case study is an essential investigation tool to allow for better understanding than is possible with quantitative measures. The current study used a multiple case study lens. Stake (2006) explained that in “multicase study research, the single case is of interest because it belongs to a particular collection of cases” (pp. 5-6).

The size of qualitative studies is usually quite small, averaging between one and twenty participants (Creswell, 1998). Using criterion sampling, five teachers were selected as the focus of this study. Criterion-based selection techniques involve determining participants based upon the goals of the study (Creswell, 1998). The participants were selected because they each met the selection criteria as teachers implementing CASE for the first year. They were all teachers located within the Educational Consortium funding the research, and for reporting purposes

pseudonyms were used. Qualitative researchers make use of non-probabilistic sampling procedures to focus the study from its inception, identifying cases demonstrating the specific characteristics of interest (Patton, 2002). Permission was granted through the individual and the Institutional Review Board. It is important to note that Oregon was connected to CASE in a number of ways that other states may not have been. Because of the tight knit Oregon Agricultural Education community, there were teachers in this study who have done early field experiences with CASE authors or had them as professors while at Oregon State University. One of the teachers in the study was a former teaching partner of a CASE curriculum developer.

Data were collected through two semi-structured interviews, a focus group and a school year of weekly journals. Questions were planned ahead based upon the central questions being investigated and aimed to capture participant experiences with the CASE curriculum as well as how it was impacting their students and their program. Both semi-structured interviews lasted approximately 30 minutes per teacher while the focus group lasted just over an hour. Weekly journal prompts were sent every Thursday morning, for the duration of the school year, with teachers responding by email.

Bracketing the experiences and biases of the researchers which could have potentially influenced the interpretation of the results helped ensure the objectivity and confirmability. The researchers in this study are former high school teachers and are all presently involved in teacher education. One researcher taught in North Carolina, one in California, while a third taught in Oregon. These experiences influenced how the researchers interacted with and received responses from the agriculture teachers, but every attempt was made to minimize this influence by triangulating data and being aware of these possible influences.

The data was compiled into a single file for each teacher containing all of their journal entries, both interviews, and the focus group. The coding process began with a review and re-read of the data for an individual teacher. An attempt was made at open-coding looking for significant comments and reflections that helped a reader understand the individual as clearly as possible while remembering the goal of the study. The data for each teacher was compiled and analyzed for overlapping information. Each researcher wrote two to three of the case summaries and the other researchers read behind them to examine the report for comprehensiveness.

Qualitative researchers use measures of validation formed from the credibility, transferability, dependability, and confirmability achieved through the methods (Lincoln & Guba, 1985). Credibility relates to the level of confidence in the researcher, design, and findings, to accurately represent and interpret the data (Ary, Jacobs, & Sorensen, 2010). Credibility of the data was established through the use of reference materials, peer debriefing, and member checks. First, interviews were audio recorded, and transcribed word for word. According to Kvale (1996), transcripts are translations of the lived interview experience into the text format and are interpreted differently as a result. Therefore, transcripts were submitted to participants to allow them to check for the accuracy of statements. Throughout the data collection, individual coding, and group coding process, the lead researcher consulted an outside peer in order to debrief the process as well, and further ensure through an outside perspective that the results could hold true (or be considered credible). To establish transferability, participants were purposively selected for the study based upon their level of experience with the

phenomenon. Thick descriptions were also utilized to further support the transferability of the results. Finally, to ensure the dependability and confirmability of the results, the raw interview protocol, records of the audio transcripts, raw individual and group codes, and researcher reflections have been maintained, so that future researchers could feasibly conduct the study with other participants.

Qualitative research, by purpose and design, focuses on a smaller number of participants in greater depth. While potentially transferable to other settings, the findings from this study are limited to the context of the five teachers in Oregon who participated. In addition, this is one component of a larger study. It is important to remember that qualitative research is not intended to be generalized, and the findings should not be interpreted beyond the scope of the participants in this study.

Case Summaries

Doug teaches in a suburban school in a multi-teacher agriculture department and has taught for 12 years. He attended the CASE institutes for both Principles of Agricultural Science -Plant and Animal, during the same summer, and holds a Bachelor’s degree in Agricultural Sciences. Doug has interacted extensively with CASE personnel and is active in utilizing available resources, including contacting the CASE developers directly, with questions and recommendations.

Doug is an overall positive supporter of CASE and knows several of the curriculum writers and stated “I am a supporter. I am always going to be. I believe in the people that are running it.” At the beginning of the research year, Doug indicated his perspective on CASE and stated “I like it. I appreciate it a lot. And my students are appreciating it too and that’s the important thing for me.” He describes CASE as “what ag teachers would teach if they had the time to teach just one class.”

Doug indicated that the institute was “outstanding for the most part.” He encouraged participation in the institute and stated “Without hesitation go.  Be prepared to be challenged about your teaching and go with an open mind to get better at your craft… embrace it as a tool to make you and your program better.” He believed the biggest challenge upon completion of the institute was “buying $30,000 worth of stuff.” However, the cost was also reflective of the materials needed to teach all three courses –Principles of Plant, Animal, and Introduction to AFNR. Doug indicated that the initial cost was one of his biggest areas of concern.

Doug indicated that one of his concerns with the curriculum was that he was “not going to get through the whole course.” However, once he began teaching and modified the pacing, he indicated that he was “okay with it [pacing].” He explained that “next year I know it will be much faster and I have already found places where we can pick up the pace.” At the conclusion of the year, Doug indicated that he made it “60%” of the way through the curriculum.

Specifically, Doug appreciated the heavy science components of CASE and the method of delivery. He stated “it is more inquiry based, problem based.” He went on to indicate “it is not anything different than what I have ever taught before. But it is put together into a logical

sequence and a packet that makes sense, not only to me, but to our kids.” He also felt that the CASE curriculum could work in any program, with any type of student “if the ag teacher is willing to make it work.” He believed that “CASE is a powerful tool for anybody, but I don’t think it is the savior for ag programs.”

Doug felt that the CASE curriculum had encouraged positive changes in both the students and his teaching. He felt that he had already seen changes in how the students processed information and stated “they are really going to start looking for the concepts.” He saw CASE as effective with students who do not typically excel in regular academic courses and he highlighted some of the positive benefits to IEP students. Doug felt, overall, students were finding the CASE material “more accessible, interesting, and fun.”

Regarding his teaching, Doug stated “I believe it has made me better. I see it starting to spill over into my other courses as well.” One consistent theme with Doug was how the CASE curriculum caused him to reevaluate his teaching. He stated that CASE has “really forced me to step back and look at ‘what is teaching’ and ‘what is learning’ and ‘what is that process’ and redefine my definition of teaching, to an extent.” He went on to say “I think it’s made me a more effective teacher, a more efficient teacher.”

Annie teaches with Doug and has taught for three years. She attended the CASE Institute to certify in the Introduction to Agriculture, Food and Natural Resources and holds a masters degree. She taught one class period of Introduction to AFNR to mostly freshmen and a few upperclassmen, but all were first time agriculture students. Annie was piloting the AFNR course for CASE. As such, she was asked by the CASE staff to “follow it by the book” and admits that the first week of school she “freaked out” and “got really nervous about it.” Annie added “I think if I was a little bit more loosey-goosey about it, it probably would have gone a little bit better. It would have been a little bit more me and less the curriculum.” She did indicate she thought her CASE class was “more work…in prepping and grading,” but added that all of the struggles lie in her “personally not having enough prep time to go through each lesson and prepare all of the lab equipment for each lesson…two days in advance.”

At the beginning of the year, she was not enjoying the process of teaching with CASE and was somewhat disillusioned with the day-to-day management. She stated that “it runs me out.” She felt that working with CASE had not been a positive experience “but I don’t think it is necessarily CASE, I think it is the situation.” She indicated that by ‘situation’ she meant the piloting of the course. During a mid-year interview, her feelings had improved, but not gone away. Annie stated “I am little bit more comfortable with it than the last time we spoke but…everyday is a challenge for me.” She also stated “I don’t feel like it is mine so I don’t feel like…I don’t feel comfortable.”

Annie was frustrated with the consistent grading components of the CASE curriculum and stated “by the end of the week, I have 90 freaking packets sitting on my desk.” Her frustration was also apparent in other ways. For example, when asked if she was cutting units or doing them exactly as written, she said “I have only cut one lesson that they said we should do and it just required so much material and so much crap and it was another kind of soft skill communication, and I was just like we are done with that crap.” While Annie was frustrated with

elements of the CASE curriculum, she did indicate that there were elements she enjoyed. Specifically, she liked that that program was “very organized and structured” and that she “didn’t have to think of the activities.” She appreciated the structure and thought that the students “basically know the format, which is so helpful.”

Annie was thankful for the institute and felt that it was key in her familiarity with the CASE curriculum. She said “I learned so much. . . I thought that there was no better way to prepare me to teach it than by going to that.” She did mention several times she struggled with the curriculum organization and having to look in several places to find the objectives, activity, materials, and other requirements. She stated “I would say between five to seven places that you need to actually look to make sure that you are getting it all.” To get all the items she needed for the activities, Annie indicated that she was “constantly, at least once a week, tapping my personal bank account as well as our FFA chapter account.” She recognized that she was purchasing consumable materials and that, in all likelihood, would not get reimbursed. She was challenged by the amount of printing and materials necessary to implement CASE. Referring to the paperwork, she stated “I mean, it’s insane. The amount of stuff you need to print is crazy because there’s a worksheet, literally, a little packet . . . there is something that they get every single class period.”

At the end of the year, Annie, reflective and looking back, recognized that there were opportunities to adjust the curriculum. For her first year, she “followed the CASE curriculum by the book” and indicated that the next year she would “restructure my units and switch them all around.” She felt that her lack of flexibility, mainly due to the pressure of piloting the course is “why she struggled.” Annie acknowledged “I wasn’t moving and playing with it. I was trying to teach the curriculum exactly how it was presented straight through.”

Jane teaches at a large, urban school. She is in a single teacher department and has taught for 8 years. She is certified in Principles of Agricultural Science – Plant. She was traditionally certified with a bachelor’s degree. She has experience working in several different sized high schools and has a background in horticulture. Jane taught three horticulture classes and utilized the CASE Plant Science curriculum. She did have English Language learners (ELL) making up approximately 20% of her plant science enrollment. Jane’s average class size was around 32 students and classes were 90 minutes in length. In addition, it is important to recognize that Jane’s experience with CASE was underfunded. She was funded to attend the institute, however, she taught through year one (the research year) without the materials CASE recommends for implementing the curriculum.

On her first reflections and during her first interview, Jane mentioned that the high numbers of ELL students presented a problem with the reading-heavy CASE curriculum. She developed strategies to meet the needs of students and as the year progressed, she expressed gratefulness for the reading load and recognized an improvement in reading fluency. Jane’s strategies included decreasing the lesson pacing and spending time reading and re-reading the important lesson content. Jane continued to struggle with the heavy reading components in CASE. In some of her later reflections, Jane stated “my students are really struggling ... they don't have the reading levels and basic skills and we are having to go back and re-teach things.”

Jane recognized that the CASE curriculum forced the students to actively participate. Regarding the ELL students, Jane felt that in the past, they would just do what their friends were doing and just sit and wait. She stated “well, they can’t do that now.” Overall, she indicated that it took a while for the students to make the shift in thinking. Jane recognized that CASE works differently for different students. She stated CASE “definitely works for the upper level kids that are kind of the general good kids and good students that do as they are told and follow directions.” She went on to say “it doesn’t work very well for kids that have attention issues. It doesn’t work with kids with low IEP and reading issues, writing issues.” Jane referenced the pacing of the curriculum and indicated that she picked from each CASE unit and didn’t necessarily cover all the material. Jane stated “we’re going to shorten up and focus on maybe six weeks at a time and then one unit and maybe the next unit, and then the next.”

When asked about her overall reflections on the CASE curriculum, Jane stated “I really still like it. I think it is a really good program. It just doesn’t address everybody, obviously, but not all of them are going to.” On a personal note, Jane said that she still felt “a lot more prepared academically. I can just read over the lesson and I am ready to go.” She attributed her success to attending the CASE Institute. In describing the institute to a new teacher, she stated “It is the best teacher training I have ever been to. You will come away with something you will use every day and not one of those binders that sit on the shelf... I wish I would have had this in college.”

Jane shared some similar comments to other participants regarding the impact of the CASE curriculum on her personal teaching habits. She stated “I feel like I don’t have to scramble at midnight to try and figure out how I am going to teach this.” She said “I can look it up and go through. . . some great activity for them to do because it is already there.” Jane also offered some advice for other teachers as they prepare to implement CASE. She encouraged other teachers to “look through a calendar. . . map out what you really want to do. . . because you can’t go from start to finish. You’re going to have to modify things.”

Heather teaches in a small, rural school in a single teacher agriculture department and has taught for 4 years. She is certified to teach Principles of Agricultural Science – Plant, and holds a master’s degree in Agricultural Education. Heather is active in her school and teaches agriculture courses that offer students science credit. She is currently the only CTE program offered in the school. Heather teaches in a four day school week, with seven period days, and her class sizes are between 12-26 students. Her students typically share some demographic similarities. The school population contains 16% special education and only 3% English language learner students.

She started out the first six weeks of the school year with the CASE curriculum, but no CASE lab materials. At the beginning of the year, she “honestly thought that I wasn’t going to be able to implement it because of resources . . . my school never had that kind of budget.” Heather was able to find some “outside funding” and she went “from not being able to teach it to, all of a sudden before two days of school starting, to being able to start teaching it.” Even after she secured funding, the materials took a long time to arrive. Consequently she suggested that “purchases be made more than a month in advance of school starting.”

Heather liked the CASE institute, but felt like it was a “whirlwind.” In hindsight, she wished that she could have taken better notes and perhaps videoed several short clips to help her better remember. She described CASE as “rigorous and relevant classes. They are hands-on student driven and they are tied to academic core standards.” Heather really felt that the addition of the CASE curriculum was improving the academics of her students. Heather liked the hands-on aspects of CASE and appreciated the fact that the students were forced to take an active role in their learning. She did have some logistical challenges with getting supplies for the CASE lessons, due to the lack of stores in her rural area. She said “I don’t live near any stores, so it’s not easy to just run down and go get some things.” Similar to Annie, Heather stressed that she had to “take a lot out of my own personal finances, and then wait to get reimbursed for it.” Heather wanted to caution new teachers about prep time and stated “I feel that my CASE course has the greatest amount of prep time than any other class of mine.”

Heather felt comfortable changing the pacing and some of the CASE content. Specifically, she did not teach the material in the pacing suggested by CASE. Rather, she stepped away from CASE and inserted the lessons she needed to maintain the school greenhouse and other program activities. She stated “I’m definitely way behind where I should be” on the CASE schedule. Heather indicated her biggest adjustment was in the time allotted for lessons and activities. She observed that while she had a “great group of students” they “seem to require a lot of extra direction no matter how thoroughly I review the lab at the beginning of class.”  She also noted differences across groups of students indicating that “the upperclassmen do well with the self-directed learning, but my class is predominantly sophomores who are in the process of learning personal management and self-guided direction.” Overall, Heather struggled with some of the aspects of CASE, but at the end she stated “I don’t like the program. I love it.” She stated that “CASE provides teachers with a convenient resource of rigorous, science-based curriculum, so as a teacher I feel that the quality of my course curriculum has multiplied tenfold.”

Claire is in a multi-teacher department in a suburban area of [Sate]. She has taught for 5 years. She is certified in three areas of CASE (Plant, Animal and AFNR). At the completion of the study, she was selected to serve as a lead teacher for CASE trainings. She holds a master’s degree and a traditional teaching license. She was teaching 2 periods of AFNR, one period of animal science and one period of plant science. Overall, she teaches in a school with a seven period day on traditional 45-50 minute periods and her class sizes range from 25-38 students. She has only one repeating class and, thus, has 5 different class preps per day.

Claire’s perspective on CASE was “that it is nothing revolutionary,” but she appreciated that CASE “brought in the science skills that I certainly was lacking.” She noted that her program before CASE was “pretty traditional in what we taught” and “production-oriented which isn’t what we need to be teaching kids” and that CASE “provided that opportunity for me to take it to a different level.” She also lauded CASE for being “a model where all the stuff is pretty hands on which is what we preach about in ag education all the time.” She appreciated the organized structure and the fact that she knows “what I am going to do the next day because all that busy work is gone.” Claire mentioned several drawbacks of the CASE curriculum including the initial startup costs, the prep time for both materials and labs, and the grading load which she called “a really unfair amount of grading.” Regarding the applicability of the CASE curriculum,

Claire stated “CASE is designed for everyone… as long as you are willing to admit that you don’t have it all figured out.”

She appreciated the applicability of the CASE Institutes. She felt that the institutes allowed her to “collaborate with teachers from all over the country in different types of programs and with different types of agriculture and to see what they did in their program and that in itself was a really tremendous learning experience.” She also believed “we would see a decrease in teacher burn-out if we provided new teachers with the experience and guidance to more effectively deliver classroom material.” Claire’s personal reasons for attending three CASE institutes in one summer was to “provide myself with resources to allow me to spend more time on teaching, FFA and my family, rather than always trying to prepare or just to stay afloat.” She believed that the structure of the curriculum was beneficial for her and her students and that it would draw a different type of student to the program. In relation to the draw her program would have for students, Claire said “I think it is going to change the type of kids that we get out there in some of those areas as it becomes more science-based and inquiry-based. We are going to draw on a different kind of kid.”

Similar to Heather, Jane, and Doug, Claire didn’t have any issues modifying CASE. She noted that “how you run your classroom is ultimately your decision. There is no CASE police out there.” She felt that the CASE curriculum had actually given her some additional freedom in her classes by providing her “more time to make it fun instead of trying to just get the bare minimum.” While Claire believed that CASE had the ability to work in any program, she did recognize that she had to adjust some of the lessons, both content and pacing. She felt that “there is no way I can get through it, so I just have to pick and choose what is important in terms of science and articulation, and community college articulation.” Claire felt that the CASE curriculum overall was not completely changing her curriculum “it’s just enhancing it.” As a result of the implementation of CASE, Claire stated that “instead of being a production agriculture class, I’m teaching an agricultural science class, which is what industry says we need.” Claire talked about how the CASE curriculum provided her some balance between “core” content and FFA content.

Going through the training and implementing CASE caused Claire to rethink her philosophy. Evidence of reflection was apparent in statements like “I think that I spent a lot of time this summer thinking about what is important. Is it important to win a banner? Is it important that you are getting experience or learning those things?” She was also reflective about times she gave a month of class time to prepare speeches or learn for other FFA events stating “I’m not proud, but for certain things, I spent too much time.” For her, the most important thing was that her students were “well-rounded and exposed to a variety of different things.”

Claire felt that as far as rigor, CASE hit about in the middle and “unfortunately, for some it is over their heads and for some it is too low.” She stated that using CASE has “kept my lessons more meaningful.” Instead of spending two weeks making wreaths and centerpieces, she said she “spent two days.” When describing the curriculum, Claire used words and phrases like routine, reliable, consistency, “know what to expect”, “clear path to follow.” She appreciated the structure of the curriculum yet also emphasized the importance of materials. If she could change one aspect of the curriculum, she “would make funding available to purchase ALL of the

recommended supplies for teachers interested in adopting the curriculum.” She stated she was able to purchase “most of the equipment and supplies through Perkins and other grants.” Claire did struggle with implementing the curriculum within larger class sizes. She had purchased materials for 20 students and very quickly realized that students would need to share and work in groups. She also referenced the sizable amount of copies required. Claire said “I feel like I have made more copies in the first week than I did all last year.”

Conclusions, Implications, & Recommendations

This study lends itself to the exploration of CASE implementation by five different teachers. Each teacher, context, and materials vary from program to program. While each teacher, situation, and case is different, there were several overlapping themes identified in the results. The five strongest themes that emerged were:

Some teachers adapted more easily to the student-centeredness of the curriculum. Teachers enjoyed having content available, but none of them made it all the way through

the material. The teacher’s personality influenced the implementation of the curriculum. Teachers saw attending the institute as vital to their implementation of CASE. Implementing CASE allowed the teachers to refocus.

The use of the Concerns Based Adoption Model (CBAM) (Hall & Hord, 2001) encourages the analysis of findings for clues to the stages of concern, levels of use, and implementation configurations. Placement of individuals into stages, levels, and configurations can be done through the analysis of interviews, reflections, and focus groups. Within the stages of concern, Annie was in a management stage and was highly focused on the mechanics of the implementation. Heather and Jane were both in the consequences stage of concern. They were confident in implementation and reflected on how the curriculum was influencing their students. Doug and Claire were not only comfortable with the curriculum, they had both developed active strategies for improving CASE; therefore, they were in the refocusing stage of concern.

The participants in this study also variety by levels of use of CASE. This could be expected with the implementation of a new curriculum and the vastly different school contexts, locations and support structures (both administrative and fiscal). Jane and Annie were both in the mechanical level of use and primarily occupied with day-to-day aspects of the curriculum. Heather was able to implement the curriculum yet didn’t significantly alter the curriculum, thus placing her in the routine category. Doug and Claire were both in the refinement category as they more fully engaged with the curriculum and were able to make changes in both their grading and pacing. However, the researchers also felt that Claire exhibited signs of also being at the integration level. She articulated her ability to work within the curriculum while consulting with colleagues and addressing the needs of community members.

When looking at the participant data, it was apparent that each adopted a different implementation configuration. Annie implemented the material in a straightforward manner with little variation while Heather and Jane used the CASE curriculum in a supplemental manner. Doug and Claire both fully implemented the curriculum and evidenced a configuration closest to the full adoption intend by CASE (CASE, 2011). As a result of the emergent themes, and the

data collection as a whole, the authors recommend that teachers interested in participating in CASE consider their own personality in the classroom and whether they are willing to make the shift from a teacher to student-centered environment. Teachers who struggle with allowing students to work on their own and who prefer the structure of specific answers, as opposed to contextual, applied answers, may find the CASE curriculum difficult to implement.

Based upon the participants’ experiences with CASE, the researchers recommend that teachers try to line up the funding to purchase the CASE-related scientific equipment prior to the summer institute. The participants in this study had varying levels of equipment support, yet all indicated that without some equipment, the institute and training are not nearly as effective. This finding is consistent with previous research showing resources and funding were barriers to implementation within Project Lead the Way schools (Shields, 2007). The participants also indicated the need to be flexible with the implementation of the curriculum and willingness to insert your own voice, make modifications, change PowerPoints, supplement materials, and adjust the curriculum to meet the needs of your program and local community. None of the participants made it all the way through the yearlong curriculum and each one recognized the importance of modification and tailoring the curriculum to meet their local needs.

Implementation of the CASE curriculum allowed the participants a chance to refocus and reflect on their development as teachers. Several of the teachers referenced the fact that CASE would have been extremely beneficial during their first few years of teaching and recommended that other teachers consider how the implementation of CASE can create the additional time necessary to focus on several different classes (preps). As a group, participants recommended the CASE curriculum and voiced appreciation for the fact that it allowed them to refocus some of their creative and curriculum development energies in a different direction. Two of the participants referenced the high burnout rate in agricultural education and felt that having access to a curriculum such as CASE may lessen teacher attrition. Based on the voice of the participants, the authors recommend that practicing agricultural education teachers consider attending the CASE institute and engaging with the CASE curriculum. Further research is recommended to distinguish both the cognitive and affective impacts of the CASE institute.

The participants indicated that teacher educators should encourage participation in CASE as a means to promote professional development and allow teachers the opportunity to refocus. The teachers in this study reevaluated their teaching and their classroom focus as a result of interacting with the CASE curriculum. In this study, CASE was a tool which promoted self-reflection and program evaluation among the practicing teachers, of which teacher educators have long lauded the importance (Baird, Fensham, Gunstone, & White, 1991; Calderhead & Gates, 1993; Patterson, 1993). Teacher educators should consider the refocusing benefits of the CASE curriculum when discussing curriculum merits with their students.

The authors recommend that teacher educators consider exposing students to components of the CASE curriculum during their undergraduate or graduate experience. However, the participants of this study also questioned the readiness of pre-service teachers to understand and implement the curriculum as first year teachers. Further research should examine the readiness of pre-service teachers to actively engage with the CASE curriculum and implement it in the classroom during their first year. Furthermore, the authors recommend that CASE investigate

ways to help teachers customize the curriculum to fit the needs of their program, either through the training they provide through their summer institutes or in the materials they provide. It may be beneficial for CASE to address some of the challenges a teacher may face with fully implementing the program and continue to look for ways that teachers can share some of their best practices with their peers. As CASE continues to grow, further research is recommended which examines and groups teachers by similar schools and contexts. This will allow for greater applicability and aid Agricultural Education as we seek to address industry and employability needs in the 21st century.

References

Ary, D., Jacobs, L. C., & Sorensen, C. (2010). Introduction to research in education (8th ed.). Belmont, CA: Thomson Wadsworth.

Baird, J. R., Fensham, P. J., Gunstone, R. F., & White, R. T. (1991). The importance of reflection in improving science teaching and learning. Journal of Research in Science Teaching, 28(2), 163-182. doi:10.1002/tea.3660280207

Bellah, K. A., & Dyer, J. E. (2009). Attitudes and stages of concern of elementary teachers toward agriculture as a context for teaching across grade level content area standards. Journal of Agricultural Education, 50(2), 12-25. doi:10.5032/jae.2009.02012

Calderhead, J., & Gates, P. (1993). Conceptualizing reflection in teacher development. Newark, DE: Psychology Press

Camp, W. G., & Heath-Camp, B. (2007). The status of CTE teacher education today. Techniques: Connecting education and careers, 82(6), 16-19. Retrieved from http://eric.ed.gov/PDFS/EJ775464.pdf

Connors, J. J., & Elliot, J. F. (1993). The influence of agriscience and natural resources curriculum on students’ science achievement scores. Retrieved from http://www.eric.ed.gov/PDFS/ED383861.pdf

Connors, J. J., & Elliot, J. F. (1994). Teacher perceptions of agriscience and natural resources curriculum. Journal of Agricultural Education, 35(4), 15-19. doi:10.5032/jae.1994.04015

Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage Publications.

Curriculum for Agricultural Science Education (CASE). (2011). CASE lesson development philosophy. Retrieved from http://www.case4learning.org/about-case/vision.html

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Feagin, J. R., Orum, A. M., & Sjoberg, G. (Eds.) (1991). A case for the case study. Chapel Hill, NC: University of North Carolina Press.

Hall, G. E., & Hord, S. M. (2001). Implementing change: Patterns, principles and potholes. Boston: Allyn and Bacon.

Kvale, S. (1996). InterViews: An introduction to qualitative research interviewing. Thousand Oaks, CA: Sage.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.

Martin, M. J., Fritzsche, J. A., & Ball, A. L. (2006). A Delphi study of teachers’ and professionals’ perceptions regarding the impact of the no child left behind legislation on secondary agricultural education programs. Journal of Agricultural Education, 47(1), 101-109. doi:10.5032/jae.2006.01101

Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA: Jossey Bass.

Miller, G. (2000). 21st century research for agricultural education. Proceedings of the National Agricultural Education Research Conference, 27, 240-253. Retrieved from http://www.eric.ed.gov/PDFS/ED449351.pdf

Myers, B. E., & Thompson, G. W. (2009). Integrating academics into agriculture programs: A Delphi study to determine perceptions of the national agriscience teacher ambassador academy participants. Journal of Agricultural Education, 50(2), 77-88. doi:10.5032/jae.2009.02075

National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. Washington, DC: US Department of Education.

Newhouse, C. P. (2001). Applying the concerns-based adoption model to research on computers in classrooms. Journal of Research on Computing in Education, 33(5), 1-21. Retrieved from http://web.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=f12857fa-9bc9-4792-a2c6-aa258719b07d%40sessionmgr12&vid=33&hid=9

Patterson, L. (Ed.) (1993). Teachers are researchers: Reflection and action. Newark, DE: International Reading Association.

Patton, M. Q. (2002). Qualitative research & evaluation methods (3rd Ed.). Thousand Oaks, CA: Sage.

Project Lead the Way (PLTW). (2011). Who we are. Retrieved from http://www.pltw.org/about-us/who-we-are

Shields, C. J. (2007). Barriers to the implementation of Project Lead the Way as perceived by Indiana high school principals. Journal of Industrial Teacher Education, 44(3), 43-70. Retrieved from http://www.eric.ed.gov/PDFS/EJ830484.pdf

Stake, R. E. (2006). Multiple case study analysis. New York, NY: The Guilford Press.

Thompson, G. T., & Balschweid, M. M. (1999). Attitudes of Oregon agricultural science and technology teachers toward integrating science. Journal of Agricultural Education, 40(3), 21-29. doi:10.5032/jae.1999.03021

VanMaanen, J. (1979). Reclaiming qualitative methods for organizational research: A preface. Administrative Science Quarterly, 24(4), 520-526.

Willis, J. (1992). Technology diffusion in the soft disciplines: Using social technology to support information technology. Computers in the Schools, 9(1), 81-105.

How Does CASE Function in the Total Agricultural Education Program?

Misty D. Lambert, Oregon State UniversityJonathan J. Velez, Oregon State University

Kristopher M. Elliott, Oregon State University

Abstract

This qualitative study sought to explore the impact the new CASE curriculum was having on the total agriculture program as perceived by the Agricultural Education instructor. Five teachers across four schools participated in weekly journals, two semi-structured interviews, and one focus group. The findings for the study are supported by thick, rich quotes and indicate seven themes: a) Teachers were uncertain as to the impact CASE would have on their enrollment; b) All of the teachers saw CASE as either paperwork heavy, grading heavy, or both; c) The materials and equipment were essential to the successful implementation of CASE; d) The teachers took personal ownership of CASE and modified it; e) FFA and SAE were altered; f) Pacing was a challenge for some; and, g) CASE provided more science content and less production focus. Recommendations are made for teachers, teacher educators, and future research.

Introduction and Review of Literature

Since the passage of the Smith-Hughes Act in 1917, the industrial revolution has occurred. This revolution resulted in massive changes to the agriculture industry, including increased productivity. Consequently, the percentage of individuals involved in production agriculture has dropped from 38% in the early 1900s to 2.6% in 2000 (United States Department of Agriculture & Utah State University, 2005). In the early 1900s, Agricultural Education programs were production based. However, as the agriculture industry has changed so has the Agricultural Education classroom. “The objectives have shifted from preparing students for a career in production agriculture to preparing students for a career that requires knowledge of agriculture” (Doerfert, 2011, p. 24). While today’s competitive global market requires all citizens to become literate in science and math (Mazur, 1998), it is becoming clear American students are falling behind when it comes to science achievement when compared to other countries (National Center for Education Statistics, 2005; Provasnik et al., 2009). When compared to other countries, 15 year old students in the United States “are not able to apply scientific knowledge and skills to real world tasks as well as their peers” (Provasnik et al., 2009, p. 45).

The call to integrate science is not new. In 1988, a national report Understanding Agriculture: New Directions for Education was published and concluded there was a need to increase the scientific subject matter in the Agricultural Education curriculum (Phipps, Osborne, Dyer, & Ball, 2008). Many high schools in the United States have been facing an increased demand for the basics: mathematics, science and writing (Doerfert, 2011). Unfortunately for agriculture, these increased course demands have many times been met by sacrificing career and technical education opportunities for students (Camp & Heath-Camp, 2007; Luft, 2004; Martin, Fritzsche, & Ball, 2006).

In order to keep student enrollments up and fit into an ever changing education landscape, many programs have begun integrating science into their agriculture courses. Agriscience teachers perceived program enrollment may increase as the teachers integrated more science into their courses (Thompson, 1998). Agriculture has been called the world’s oldest science (Ricketts, Duncan, & Peake, 2006) and, because of the context, serves to help students understand scientific principles and concepts better (Thompson & Balschweid, 2000). Studies (Chiasson & Burnett, 2001; Thompson & Balschweid, 2000) have found integration of science concepts into the agricultural education curriculum was a more effective way to teach science. Secondary students who were enrolled in an advanced life science curriculum, taught in the context of animal agriculture, learned the transferable skills needed for achievement in scientific commerce and industry including the ability to function in experimental settings, the conduction of laboratory write ups, team work, and problem solving (Balschweid & Huerta, 2008). Johnson (1995) found offering science credit for agriculture courses not only increased enrollment and benefitted students, it also enhanced the program image. Some agricultural educators have attempted to incorporate more science into their courses while others have been reluctant to change traditional agriculture programs, fearing the integration of science could marginalize what makes agriculture programs unique (Whent, 1992). American agriculture is as diverse as the fifty states themselves. “For local programs to be effective, the diversity of the local agriculture industry, as well as the entire global food and agriculture system, must be reflected in the curriculum” (Doerfert, 2011, p. 26). This diversity brings a challenge: Can you develop a set of national standards or a curriculum that will allow for an individual community’s agricultural diversity while reflecting the breadth and depth of a global system?

The Curriculum for Agricultural Science Education (CASE) project was launched by the National Council for Agricultural Education (CASE, 2011) and attempted to create just such a national curriculum. The CASE curriculum emphasized the cross-walking of secondary agricultural education curriculum to both the National AFNR Career cluster content standards released by the National Council for Agricultural Education as well as to the national standards for core academics. These core standards align with the National Science Education Standards, Principles and Standards for School Mathematics and Standards for the English Language Arts (Team AGED, 2007). In the first full year of implementation, there were 87 teachers at 84 schools in 17 states implementing courses in plant and/or animal science.  The introduction course in AFNR was being field tested in 10 states by 29 teachers at 24 schools. Five schools had all three CASE courses and 11 schools were implementing two courses (Nancy Trivette, Personal Email Communication, 1/21/2011). CASE was based upon a model where FFA and SAE, as well as LifeKnowledge, are integrated into the curriculum. The CASE documentation indicates:

Of the many strengths of agricultural education, leadership, experiential learning, and character education are the three pillars. CASE does not ignore the three-circle model of agricultural education that represents the efforts of over 80 years of practice. Classroom instruction is enhanced to meet the needs of today’s agricultural students without sacrifice to FFA and SAE instruction. Both FFA and SAE are integral components of CASE as well as LifeKnowledge® connections (CASE, 2011, p. 2).

CASE uses a student-centered learning approach, including project, activity and problem-based learning strategies. When students take the lead in their own learning acquisition, teachers

become facilitators who are there to aid students through the problems they encounter (Barrows, 1988). This is a major shift from the educational model of the early 1900s. How is it working? This study attempts to address National Research Agenda Priority Area 5: Efficient and Effective Agricultural Education Programs (Doerfert, 2011). The National Research Agenda states, “Agricultural education has the obligation to show that its curriculum can be used to meet the academic challenges of today’s school system while preparing students for a career in the agricultural industry” (Doerfert, 2011, p. 26).

Conceptual Framework

The Concerns-Based Adoption Model (CBAM) (Hall & Hord, 2001) is a conceptual framework which describes, explains, and predicts probable teacher concerns and behaviors throughout the change process. In this case, it is being applied to the implementation of the CASE curriculum within a high school agricultural program. Of particular interest in this study are the stages of concern, the levels of use, and the innovation configuration.

Figure 1. Hall & Hord (2001)

Hall and Hord (2001) defined seven Stages of Concern a person may experience when implementing change. Stage 0 is ‘Awareness’ which can be defined as concern or involvement with the innovation. Stage 1 is ‘Informational’ and concerns the participant gaining more information about the innovation. Stage 2 is ‘Personal’ and involves concerns about how the innovation relates to the individual. Stage 3 is ‘Management’ and involves concerns about the mechanics of using or integrating the innovation. Stage 4 is ‘Consequence’ and looks at concerns about the effect of the innovation on students. Stage 5 is ‘Collaboration’ and concerns coordinating efforts in using the innovation with others. Finally, stage 6 is ‘Refocusing’ and involves the exploration of other ways to utilize the innovation or improve upon the innovation.

Hall and Hord (2001) also defined eight Levels of Use. These levels focus on the behaviors in teachers that are or are not taking place in relation to implementation of a new curriculum (Willis, 1992). Applying Newhouse’s (2001) explanation, these levels are the phases through which a teacher will pass as they implement the curriculum and gain confidence in its use. These begin with Level 0 or Nonuse when the teacher has little or no knowledge of the curriculum. Level I is Orientation at which point the implementer begins acquiring information about the curriculum. Level II is Preparation and involves preparing to use the curriculum. Level

User System Culture

Innovation users and non-users

Intervening

Innovation Configuration

Levels of Use

Stages of Concern

Probing

Change Facilitator

Team

Resource System

III is Mechanical and reflects a user focused on the mechanical day-to-day aspects of using the curriculum. Level IVA is Routine and is reached when the implementer is comfortable with the curriculum with little preparation and they are not planning to change how the innovation is used. Level IVB is Refinement and is reached when the implementer is working to improve their personal use of the innovation. Level V is Integration and has the teacher working with colleagues in a collaborative effort to use the curriculum. Lastly, level VI is Renewal and has the teacher reevaluating the innovation and seeking to make major modifications.

Hall and Hord (2001) also addressed Implementation Configurations in their model. The authors indicated “in nearly all cases the innovation as operationalized by different users will vary along a continuum from being very close to what the developer had in mind to a distant zone where what is being done is nearly unrecognizable” (p. 39). The researchers are attempting to uncover where teachers in this study are at within various stages of concern and levels of use, and what implementation configurations are currently in use.

Purpose and Objectives

CASE is being promoted as a curriculum that can teach core academic standards through an agricultural context. However, little is known about the greater impact the implementation of this curriculum is having on the total Agricultural Education program. The purpose of this study was to describe the implementation of the CASE curriculum and describe the concerns and behaviors of teachers using CASE.

Methods and Procedures

Qualitative methods were chosen to investigate this problem because these methods allow the researcher to understand how people make sense of their world (Merriam, 2009). This type of research is more concerned with meaning than frequency (VanMaanen, 1979). This phenomenological study fits Creswell’s (1998) definition of describing “the meaning of the lived experiences for several individuals about a concept or the phenomenon” (Creswell, 1998, p. 51).

Participants

The size of qualitative studies is usually quite small, averaging between one and twenty participants (Creswell, 1998). Criterion-based selection techniques involve determining participants based upon the goals of the study (Creswell, 1998) and were used in the selection of five teachers for the focus of the study. The participants are described in table 1. The participants were selected because they each met the selection criteria as teachers implementing CASE for the first year. They were all teachers located within the Educational Consortium funding the research. Qualitative researchers make use of non-probabilistic sampling procedures to focus the study from its inception, identifying cases demonstrating the specific characteristics of interest (Patton, 2002). Permission was granted through the individual and the Institutional Review Board and pseudonyms have been used. It is important to note Oregon is connected to CASE in a number of ways other states may not be. Because of the tight knit Oregon Agricultural Education community, there are teachers in this study who have done early field experiences with CASE

authors or had them as professors while at Oregon State University. One of the teachers in the study is a former teaching partner of a current CASE curriculum developer.

Table 1Description of study participants (n = 5)

Pseudonym School Description DegreeYears

TeachingCertified to teach which

CASE Course(s)Doug Suburban/ multi-teacher Masters 12 Plant / AnimalAnnie Teaches With Doug Masters 3 AFNRJane Large urban/ single teacher Bachelors 8 PlantHeather Small rural /single teacher Masters 4 PlantClaire Suburban multi-teacher Masters 5 AFNR/ Plant/ Animal

ProceduresData were collected through two semi-structured interviews, a focus group, and weekly

journals. Questions were planned ahead based upon the central questions being investigated and aimed to capture the participants’ experience with the CASE curriculum as well as how it was impacting their students and their program. Specific questions were also asked to understand how the teacher was being impacted. Each teacher completed two interviews which lasted approximately 30 minutes each time. The first interview occurred in September/October when the teachers first began implementing CASE and the second interview occurred mid-year (January/February). The focus group included all five teachers, occurred in early June and lasted just over an hour. Weekly journal prompts were sent every Thursday morning with teachers emailing their responses back to the researchers.

BracketingBracketing the experiences and biases of the researchers which could have potentially

influenced the interpretation of the results helped ensure the objectivity and confirmability. The researchers in this study are former high school teachers and are all presently involved in teacher education. One researcher taught in North Carolina, one in California and the third taught in Oregon. These experiences influenced how the researchers interacted with and received responses from the agriculture teachers, but every attempt was made to minimize this influence by triangulating data and being aware of these possible influences.

Data AnalysisFirst, interviews were audio recorded, and transcribed word for word by a research

assistant. According to Kvale (1996), transcripts are translations of the lived interview experience into the text format and are interpreted differently as a result. The researchers followed the hermeneutic process by re-reading all transcripts and journal entries, discussed their findings and then returned back to the data for a more indepth examination (Lincoln & Guba, 1985). As such, the interpretations are grounded in theory and previous literature as well as being informed by collegial discussions. The next step was an attempt at open-coding where each transcript was reviewed and highlighted to show all significant comments and for possible themes or connections to the goals of the study. Focused coding was the next step and confirmed the themes which were compiled and analyzed for overlapping information. The process

continued through multiple readings until the researchers had an intimate appreciation of the data (Lincoln & Guba, 1986).

TrustworthinessQualitative researchers use measures of validation formed from the credibility,

transferability, dependability, and confirmability achieved through the methods (Lincoln & Guba, 1985). Credibility relates to the level of confidence in the researcher, design, and findings, to accurately represent and interpret the data (Ary, Jacobs, & Sorensen, 2010). Credibility of the data was established through the use of reference materials, peer debriefing, and member checks. The researchers worked in concert and checked in with each other weekly to observe and process journal entries and discuss the direction of the study. Transcripts were submitted to participants to allow them to check for the accuracy of statements. To establish transferability, participants were purposively selected for the study based upon their level of experience with the phenomenon. Thick descriptions were also utilized to further support the transferability of the results. Finally, to ensure the dependability and confirmability of the results, the raw interview protocol, records of the audio transcripts, raw individual and group codes, and researcher reflections have been maintained, so future researchers could feasibly conduct the study with other participants.

Limitations of the studyQualitative research, by purpose and design, focuses on a smaller number of participants

in greater depth. While potentially transferable to other settings, the findings from this study are limited to the context of the five teachers in Oregon who participated. Qualitative research is not intended to be generalized, and the findings should not be interpreted beyond the scope of the participants in this study. This paper is part of a larger study which assessed the perceived impacts of CASE.

Results

The following seven themes emerged as to how the teachers saw CASE impacting their Agricultural Education programs. Each theme relates to the program in general and/or aspects of the Agricultural Education model.

Teachers were uncertain as to the impact CASE would have on their enrollment.

The teachers thought it was “too early to tell” if implementing CASE was going to impact their enrollment numbers. Claire said “I don’t know if it’s going to help numbers go up or if they are going to stay the same or if they are going to go down …we are going to have to wait that one through.” Claire also thought it might affect the kind of students her program was going to attract stating “we are going to draw on a different kind of kid. I think it’s going to provide options in terms of graduation requirements for kids to be successful” adding she thought their numbers might not change. “We have good class numbers and all of my classes are full. Our [FFA] membership isn’t really any different…. And in general, it hasn’t affected numbers really at all.” She added that while enrollment wasn’t an issues, she would have to wait and “see how competitive we are” in FFA events.

When asked about how the students perceived CASE, Heather said “unfortunately, I’m going to have to say that my kids enjoyed the non-CASE classes a little bit more. Just because...normally, they are taking my classes because they want a break from all of their academics.” As a result, students initially were not fully welcoming to the idea of CASE. Heather felt since some of her other agriculture classes were more laid back, the students could recognize the academic nature of the CASE classes and as a result, tended to like her other classes better.

All of the teachers saw CASE as either paperwork heavy, grading heavy, or both.

“I have realized that CASE is very paper heavy.” This sentiment was expressed early on by Jane and just a few weeks later, this was Claire’s journal entry: “I feel like I am drowning in paperwork...tons and tons of copies and things to grade.” Annie remarked about both the paperwork and the grading involved with implementing CASE: “It’s insane. The amount of stuff you need to print is crazy because there’s a worksheet, literally, a little packet, whether it’s one page or three pages, there’s something that they get every single class period.”

The teachers were worried about the cost of making all of the copies. Claire stated “my cost is like four hundred dollars a month in copies for all of these classes and so I have to find a different way” while adding later “I do feel like there’s a tremendous amount of copies involved that I am sure the school will eventually stop.” Teachers were seeing the need to modify the way they were implementing CASE’s paper load both because “it seems a little intimidating for kids” and because “in times of budget cuts and large classes” they could not “afford to provide everything down to notes pages to kids.”

Most of the teachers mentioned being overwhelmed by the grading. Some of them were still grading everything, with Doug saying “I know writing is important, but the flip side of that is, because they write, I feel like I need to read it as well…shear volume builds up, but it’s worth it.” Claire had decided to take a different strategy: “I am not necessarily grading every single thing they do. If I am not checking to make sure that they are doing it, they are not going to do it.” Claire thought it was “a really unfair amount of grading” adding “It’s not feasible for me to go through two hundred and twenty binders on the weekends.” It was obviously a sore point with Annie who stated “I have 30 kids in that class. I do not have time to grade a packet for everyday. By the end of the week, I have 90 freaking packets sitting on my desk.” Claire appeared to be looking at how to adjust and accommodate the grading stating “that’s probably the thing that I’m toying around with the most is how do I want to change this for next year.”

The materials and equipment were essential to the successful implementation of CASE.

The teachers saw the CASE materials and equipment as essential to the implementation of the curriculum with Heather stating “without the available equipment, implementation of the curriculum is severely limited.” Especially early in the study, Jane made statements like “my biggest concerns are not getting the materials I need to teach the class the way it is supposed to be taught” or “my frustration with not having the equipment is that we can’t do things the right way, that you have to constantly modify.” Jane added “I changed … some of the activities. We don’t have computers in here. Computer access is an issue sometimes and so we had to scrap

some of those [activities] and change the way we had it done.” Heather declared “from what I see, to get the best out of CASE you would need a school with a lot of financial resources or a teacher that is proactive enough to go outside to find resources.”

The teachers found themselves running to the store to gather materials. Heather, one of the most rural teachers in the study, said she spent time “just making sure I have all the pieces together. I don’t live near any stores, so it’s not easy to just run down and go get some things” while Annie was concerned about having the money for those consumable items stating “I am constantly (at least once a week) tapping my personal bank account as well as our FFA chapter account.” Many saw the cost of these materials and equipment along with the space needed to use them as a barrier preventing programs from using CASE. One teacher stated “implementing the CASE curriculum is definitely a financial commitment, especially in a time of budget cuts.” Annie stated “the other reason that CASE isn’t going to work in every program is that not everybody has a science classroom… you will be able to teach CASE in a regular classroom, but it would be so much harder.” Claire, who has a classroom with “lab stations with sinks, gas/air and a hood,” indicated that not having that would be a challenge saying “I can’t imagine trying to teach some of the labs and activities in my old room! It would take a lot more prep if you didn’t have the space or materials that are recommended.”

The teachers took personal ownership of CASE and modified it.

The teachers talked about how they changed the curriculum to fit their program’s needs. The teachers were adjusting for class sizes, class lengths, school calendars, personal preferences, equipment access, and, in some cases, they modified it just to fit their teaching style. Many teachers mentioned organizing the units so it worked better for them, with Claire indicating “I just finished parli pro and public speaking and just those little shifts that made us match our school and our FFA calendar a little bit, and it seemed to work fine.”

Class sizes were a big challenge with Claire stating “it’s recommended for a class size of twenty…so you have to sometimes be creative with your class groupings and with your supplies. It’s doable and it’s challenging when you have to do thirty-five kids in the class” but adding her administration is “very enthusiastic and has helped reduce the class sizes to better accommodate the labs. I am in the low thirties or high twenties in all my classes now.” However, she adds, “in order for this curriculum to be most effective, I think the class numbers need to be smaller than what some schools can do.” While Heather felt “CASE has hit really well where it is really my teaching style” others adjusted just so it fit their teaching personality with Claire remarking “I didn’t like their PowerPoints…It is easy to modify it and make it your own…I can pull out that information and use it as my guide to make something a little bit different.”

FFA and SAE were altered.

CASE is a classroom curriculum package, but the teachers talked a good bit about the balance of their CASE instruction with the other components of a total agricultural education program. Doug spoke in terms of what Agricultural Education calls the three circle model (classroom instruction, the student organization (FFA), and experiential learning, or Supervised Agricultural Experiences (SAE)). Referring to the FFA component of the model, Doug said “you

have to make a very conscious effort as an ag teacher to keep that third circle…as big and engaged as we would like it to be” while indicating “instruction is much stronger, but you have to focus more on that FFA circle yourself” echoing Annie who stated “CASE doesn’t even have that strong of an FFA influence.” The same appeared to be true for SAE with Doug saying while CASE does a good job at the beginning of talking about SAE “it never ever comes back in the curriculum … as a students or as a teacher, you still have to say ok, so Mondays are going to be SAE record book days.”

The teachers spoke of how FFA was working in their programs. Claire stated “I’ve spent a lot less time on FFA in class” while adding “we take very little time out of the animal science curriculum to do FFA things.” It didn’t appear to be affecting enrollment with Annie indicating “I think that the kids who are ag kids… were going to be in FFA and do FFA activities regardless of CASE.” Heather did feel she wasn’t spending the same time on FFA as she had in the past. She felt the CASE curriculum allowed for more academic rigor and stated “I don’t see it partnering up with my FFA quite as much, however, them knowing more and being able to stretch those mind muscles more… I think that it is more important that they are getting the academics.”

When speaking of FFA, what most of the teachers appeared to be talking about were the Career Development Events (CDEs) where students compete in various events designed around career skills. Jane observed “I don’t see much of the CDE’s that are at state convention embedded into CASE. It has to been done separately” while Heather lamented “normally we would do floriculture at our district event and we are nowhere ready.” Annie anticipated “FFA instruction will suffer. I know that you aren’t obviously supposed to be teaching FFA and that’s not really what we do, but I do use CDE as a teaching tool.” Annie thought CASE had “hindered our progress in our SAE experience.” She indicated the curriculum “only talked about it once at the very beginning of the year, they [students] established their project and we haven’t done anything with it since.” She felt she didn’t have the time necessary to revisit the SAE projects because she had to “move, move, move.”

During the focus group, the teachers strongly indicated it could be a barrier for teachers choosing CASE as their curriculum saying “I think a big concern is that people worry, how it is going to affect their competiveness in terms of FFA.” With CASE being a packed curriculum, Claire observed teaching the CASE way “takes banners or potentially takes banners off people’s walls and that’s what they are afraid of, not being able to teach six months of CDEs”. When teachers say CASE takes away their teaching voice, Doug thought “what they are really saying is that I don’t get to have these blue banners on the wall anymore and this is how my program is viewed from the public.” Doug still saw CASE as worthwhile because it was creating strong students, adding

it’s not going to put any more banners on the wall, but it’s going to make better, more well rounded kids who are ready to go to the next level or are ready to go to industry because they are going to be able to think, they are going to be able to read, they are going to have those skills that employers are begging for.

A few teachers talked about CASE adding to their balance. Claire said “I used to always feel like if I’m teaching really well, then I’m not doing FFA really well and if I’m doing FFA

well, then I’m not teaching really well. And so, it’s brought more balance to that.” Doug indicated it caused them to focus on the classroom, not the contest saying, “Thirty kids from a plant science class can get a whole lot of knowledge about soil or five could go to a state soil contest and learn a little bit more.”

When asked about the impact of CASE on the overall FFA program, Heather thought “the CASE curriculum drives agricultural education towards the newest shift in agricultural industries of science and technology based practices.” She added, because of this shift, when it came to fitting CASE into FFA “it’s not a matter of how CASE curriculum impacts the success of our CDE programs. Rather, it’s a matter of how will CDE's adapt to correlate with the scientific era we are entering.”

Pacing was a challenge for some.

Throughout interviews and journals, conversation seemed to turn to pacing a lot. Most of the teachers talked about being behind saying “I am behind” or “I have to keep going. I can’t stop” and “my class is definitely not moving at the prescribed pace. We are … very far behind where we should be.” CASE is designed to be a 45-50 minute lesson and many of the teachers were on a block schedule with 90 minute periods. Jane observed “you can’t fit two days worth into that 90 minutes, it doesn’t fit and… when you have 35 kids, it takes a lot longer than 20 kids.” Annie thought “the majority of the lessons have taken longer than they should” but added she was also behind “because of tests and test reviews, assemblies, budget reduction days, holidays, FFA activities, substitute teachers and other interruptions.”

Teachers felt the delay stemmed partly from the inability of the students to comprehend the material with Doug observing “there is just no way for the kids to grasp all of the concepts in the course and make it through all 9 or 10 units.” The teachers needed the extra time to teach the material the way they wished with Heather stating “I can’t do it as in-depth as I need to do it.” The teachers struggled with the thought of not making it through the curriculum but Doug stated “I want to make sure that the kids are getting the concepts rather than covering stuff and then moving on…so once I came to terms with that I am ok.”

Heather said while she was extremely behind in the curriculum, “it’s just one significant sacrifice made in order to bring my students to a new level academically.” Annie was clear “every class period, I try to push them to accomplish as much as possible, but I anticipate not even getting to the last few units, while Claire felt “It is always better to have more curriculum than you are going to need, so don’t take that as a negative.”

CASE provided more science content and less production focus.

While CASE is hands-on in a laboratory setting, Heather thought it would be a good curriculum for “a teacher who wants to promote academics in their class.” However, Annie cautioned “there will be a lot of teachers in this state and I think across this country that will definitely say that it is not enough production based curriculum.” Implementing CASE was changing Claire’s program: “Before we were pretty traditional in what we taught and production

oriented which isn’t what we need to be teaching kids and so its provided that opportunity for me to take it … in a different direction.”

Many of the teachers were offering science credit for their CASE agriculture courses. In fact, Claire went to CASE training in the first place because her school wanted her “to offer science credit and the CASE course seemed like a good route to document standards and curriculum.” Claire said students were giving her feedback that her Agriculture course “feels like a science class” and Claire agreed it should feel that way because the students were getting science credit. Claire added there was a misconception among students they “are going to come out and take an easy ag class and learn how to farm which isn’t what we were doing before anyway. I think that’s just what kids think we do out there.”

While the curriculum “is so very science,” Claire observed “instead of being a production agriculture class, I’m teaching an agricultural science class, which is what industry says we need.” In fact, Heather was grateful to CASE for saving her job, indicating in spite of budget cuts, CASE was “the reason why I’m going to be teaching biology next year and not the science teacher. They chose to keep the Ag program over the science position.”

Annie observed once she had done the CASE lab “the only comment was, ‘what does this have to do with ag?’” and indicated that was “a question I have to answer every day during the new CASE curriculum.” Surprisingly, Heather shared she “tried signing up for the Animal Science CASE and my administration was actually not supportive of it because it was adding too many academics to our electives.”

Conclusions, Implications & Recommendations

The teachers in this study were uncertain about how to best incorporate the CASE curriculum and the FFA/SAE portions of the program. Therefore many individual teachers took the liberty of modifying the curriculum to meet the local programmatic, student and community needs. All the teachers did comment on the logistical considerations of the CASE curriculum—focusing on both paperwork and grading. One very strong theme which emerged was the critical importance of the CASE materials. Some teachers had more access to materials than others and reported adequate materials are essential to successfully implementing the curriculum.

Analysis of the participants through the Concerns Based Adoption Model (CBAM) (Hall & Hord, 2001) yielded several important conclusions and implications (see table 2). According to the stages of concern portion of the model, Annie was firmly situated within the management stage. Her thoughts reflected her primary concern with the mechanics and integration of the program. Heather and Jane were both within the consequence stage of concern. Their concerns centered around how the curriculum was impacting their students and they evidenced confidence in the actual implementation of the curriculum. Finally, both Doug and Claire were in the refocusing stage, the sixth stage of concern. They were comfortable with the curriculum, able to assess how it was impacting their students, and had both taken active roles to improve the CASE curriculum. At the conclusion of the research year, both Doug and Claire were assigned lead teacher roles by CASE and, in the summer following this research, each taught a CASE institute.

Table 2Concerns Based Adoption Model by participantParticipant Stage of concern Level of useDoug Refocusing RefinementAnnie Management MechanicalJane Consequence MechanicalHeather Consequence RoutineClaire Refocusing Refinement/Integration

The CBAM also considered the CASE levels of use by the participants. Both Jane and Annie were mechanical in their level of use and were primarily occupied with the day-to-day aspects of using the curriculum. Heather was within the routine category with her level of use as she was able to implement the curriculum, but did not provide substantial research evidence she was intending to significantly change or alter the innovation. Doug provided evidence he was within the refinement category. He fully implemented the curriculum and then made alterations in grading and pacing to improve his use of the curriculum. Finally, Claire revealed aspects of both refinement and integration—refinement in that she didn’t hesitate to alter the curriculum to meet her program needs and integration as she expressed attention and concern for her colleagues and program stakeholders. She worked with her department colleagues to tailor the curriculum to meet the program needs of her school and community.

The participants evidenced several different implementation configurations. Annie implemented CASE in a direct manner with very little changes. Heather and Jane, on the other hand, implemented the material in a supplemental manner whereby they integrated CASE with their current curriculum. Claire and Doug were able to fully implement the curriculum and showed comfort and familiarity with making changes and modifications.

The participants also wrestled with, in both interviews and journals, the balance between the three circles of Agricultural Education. Participants felt they were better prepared to meet the needs of all the students in their classes and at the same time, they questioned the level of importance of participation in some FFA events. During the focus group, the participants engaged in a hearty discussion of whether the implementation of CASE would take FFA “banners off the walls.” During the discussion, despite whatever preference the individual teachers had, the consensus was the CASE curriculum provided benefits to all the students in the class on a consistent and continual basis in a manner which prepared students to enter into science-related agricultural careers. The authors recommend teacher educators consider their statewide focus in agricultural education and determine whether an emphasis on science integration is beneficial to their local teachers. Further research is needed to specifically explore the aspects of science integration within the CASE curriculum. How fully are practicing teachers implementing science content? Are students evidencing significant gains in science? Is there data to show the CASE curriculum is meeting the scientific competency needs of industry?

Results of this study emphasize the unique aspects of Agricultural Education programs and the integral Agricultural Education model. As teachers and programs consider the implementation of CASE, it is important they adapt and modify the curriculum to meet their local program needs. Further research should examine the student perspectives related to their

lived experiences with the CASE curriculum as well as examine how practicing CASE teachers are fully implementing the FFA and SAE components of the Agricultural Education model.

References

Ary, D., Jacobs, L. C., & Sorensen, C. (2010). Introduction to research in education (8th ed.). Belmont, CA: Thomson Wadsworth.

Balschweid, M., & Huerta, A. (2008). Teaching advanced life sciences in an animal context: Agricultural science teacher voices. Journal of Agricultural Education, 49(1), 17-27. doi:10.5032/jae.2008.01017

Barrows, H. S. (1988). The tutorial process. IL: Southern Illinois University School of Medicine.

Camp, W. G., & Heath-Camp, B. (2007). The status of CTE teacher education today. Techniques: Connecting education and careers, 82(6), 16-19. Retrieved from http://eric.ed.gov/PDFS/EJ775464.pdf

Chiasson, T. C., & Burnett, M. F. (2001). The influence of enrollment in agriscience courses on the science achievement of high school students. Journal of Agricultural Education, 42(1), 60-70. doi:10.5032/jae.2001.01061

Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage Publications.

Curriculum for Agricultural Science Education (CASE). (2011). Understanding the CASE model. Accessed from http://www.case4learning.org/about-case/vision.html

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Hall, G. E., & Hord, S. M. (2001). Implementing change: Patterns, principles and potholes. Boston: Allyn and Bacon.

Kvale, S. (1996). InterViews: An introduction to qualitative research interviewing. Thousand Oaks, CA: Sage.

Johnson, D. M. (1996). Science credit for agriculture: Perceived support, preferred implementation methods and teacher science coursework. Journal of Agricultural Education, 37(1), 22-30. doi:10.5032/jae.1996.01022

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.

Lincoln, Y. S., & Guba, E. G. (1986). But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Directions for Program Evaluation, 30, 73-84.

Luft, V. D. (2004). Accountability: Not a destination, but a never ending journey. Journal of Agricultural Education, 45(1), 1-9. doi:10.5032/jae.2004.01001

Martin, M. J., Fritzsche, J. A., & Ball, A. L. (2006). A Delphi study of teachers’ and professionals’ perceptions regarding the impact of the no child left behind legislation on secondary agricultural education programs. Journal of Agricultural Education, 47(1), 101-109. doi:10.5032/jae.2006.01101

Mazur, E. (1998). Moving the mountain: Impediments to change. Paper and presentation to the National Institute for Science Education Forum, Indicators of success in post-secondary SME&T education: Shapes of the future, February 23–24, 1998. In Millar, S. B. (Ed). 1998. Synthesis and proceedings of the Third Annual NISE Forum, University of Wisconsin-Madison, NISE, WCER, pp. 5-11, 91-93.

Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA: Jossey Bass.

National Center for Education Statistics (2005). NAEP state comparisons - data table. Retrieved from http://nces.ed.gov/nationsreportcard/statecomparisons/withinyear.aspx?usr Selections =1%2cSCI%2c0%2c2%2cwithin%2c0%2c0

National Research Council. (1988). Understanding agriculture: New directions for education. Washington, DC: National Research Council.

Newhouse, C. P. (2001) Applying the concerns-based adoption model to research on computers in classrooms. Journal of Research on Computing in Education, 33(5), 1-21. Retrieved from http://web.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=f12857fa-9bc9-4792-a2c6-aa258719b07d%40sessionmgr12&vid=40&hid=9

Patton, M. Q. (2002). Qualitative research & evaluation methods (3rd Ed.). Thousand Oaks, CA: Sage.

Phipps, L. J., Osborne, E. W., Dyer, J. E., & Ball, A. (2008). Handbook on agricultural education (Sixth ed.). Clifton Park, NY: Thomson Delmar Learning.

Provasnik, S., Gonzales, P., & Miller, D. (2009). U.S. performance across international assessments of student achievement: Special supplement to the condition of education 2009. NCES 2009–083: National Center for Education Statistics.

Ricketts, J. C., Duncan, D. D., & Peake, J. B. (2006). Science achievement of high school students in complete programs of agriscience education. Journal of Agricultural Education, 47(2), 48-55. doi:10.5032/jae.2006.02048

Team AGED (2007). Unmistakable potential: 2005-2006 Annual report on agricultural education. Retrieved from http://aaaeonline.org/files/07.annualreportaged.pdf

Thompson, G. (1998). Implications of integrating science in secondary agricultural education programs. Journal of Agricultural Education, 39(4), 76-85. doi:10.5032/jae.1998.04076

Thompson, G. W., & Balschweid, M. M. (2000). Integrating science into agriculture programs: Implications for addressing state standards and teacher preparation programs. Journal of Agricultural Education, 41(2), 73-80. doi:10.5032/jae.2000.02073

United States Department of Agriculture and Utah State University. (2005). Growing a nation: The story of American agriculture. Washington, DC: Author.

VanMaanen, J. (1979). Reclaiming qualitative methods for organizational research: A preface. Administrative Science Quarterly, 24(4), 520-526. Retrieved from http://www.jstor.org/stable/2392358

Whent, L. (1992). Bridging the gap between agricultural and science education. The Agricultural Education Magazine, 65(4), 6-8. Retrieved from http://www.naae.org/links/agedmagazine/archive/Volume65/v65i4.pdf

Willis, J. (1992). Technology diffusion in the soft disciplines: Using social technology to support information technology. Computers in the Schools, 9(1), 81-105.

Expressions of Social Presence in Agricultural Conversations on Twitter: Implications for Agricultural Communications

Kelly M. Pritchett, Texas A&M UniversityTheresa Pesl Murphrey, Texas A&M University

Traci L. Naile, Oklahoma State University

Abstract

Computer-mediated environments such as social media create new social climates that impact communication interactions in un-mediated environments. As computer-mediated communication (CMC) continues to encourage the development of more social communities, many communication behaviors will evolve and adapt to the unique social environment created by CMC. This study examined social variables during two different synchronous conversations on Twitter through a qualitative document analysis that coded messages into affective, interactive and cohesive categories. Categories were determined by indicators within each message such as emoticons, direct responses, and the use of individuals’ names. The researcher concluded that most social variables in the Twitter conversations of this study fall into the interactive social presence category but that affective and cohesive responses supported personal connection and structure within the conversations. It was also found that the same category of responses could function differently in each conversation. However, both conversations in this study appeared to be successful. Therefore, agricultural communicators should feel comfortable using CMC containing social presence dimensions more frequently to circulate agricultural information among populations across the globe. It was recommended that further research be conducted to examine social presence among new topics, populations, and other forms of CMC.

Introduction and Literature Review

For most Americans, some form of computer-mediated communication (CMC) supports their everyday activities (Taylor, Jowi, Schreier, & Bertelsen, 2011). Spitzberg (2006) defined CMC as “any human symbolic text-based interaction conducted or facilitated through digitally-based technologies” (p. 630). CMC offers new forms of communication, such as posts and comments that can be archived, found in searches, and distributed to the masses (Chan, 2008). These activities have created a unique social environment that challenges traditional communication behaviors (Bartter et al., 2009). In the beginning, CMC held a very matter-of-fact or un-relational connotation. More recently, many people use CMC as a means to initiate and develop relationships (Spitzberg, 2006). As innovations become more convenient and affordable, the importance of CMC is likely to increase (Spitzberg, 2006). Already, almost 78% of the population in North America is using the Internet (Internet World Stats, 2011) with 175 million registered users on Twitter.com (Twitter, 2010).

The Internet has grown from an objective research tool of the information age to a powerful catalyst for societal change where people engage in networking through chatting, messaging, and blogging (Bartter et al., 2009). These types of social media have become a primary stage for sharing information, meeting new people, and learning (Bartter et al., 2009). Popular examples of social media include Facebook, YouTube, Flickr, blogs, del.icio.us, and Twitter (Bartter et al., 2009; Kaplan & Haenlein, 2010).

Twitter is described as a “real-time information network” that allows users to publish 140-character messages called tweets (Twitter, 2011, An information network, para. 1). Tweets are known as a form of micro-blogging (Jansen & Zhang, 2009; Zhao & Rosson, 2009). Depending on a user’s preference, tweets can be accessed publicly or they can be private, meaning that tweets are viewable only to users who subscribe to another user’s Twitter feed (Honeycutt & Herring, 2009; Twitter, 2011). Twitter also allows users to categorize tweets with a hash tag, which marks topics with a “#” symbol to link tweets about the same topic (Twitter, 2011). The use of hash tags makes it easy for users to engage with others who have similar interests (Miller, 2010). Twitter platforms such as TweetChat automatically add a designated hash tag to outgoing tweets and enable users to view only the tweets about one topic in a streaming format (Ferguson & Pettit, 2009).

Some populations across agriculture have adopted the use of Twitter. In 2009, third-party applications for CMC inspired a group of farmers to develop #AgChat (#AgChat Foundation, 2011). #AgChat is a weekly moderated conversation on Twitter for “people in the business of raising food, feed, fuel, and fiber” (#AgChat Foundation, 2011, Why Agvocacy, para.1) with a mission to “empower farmers and ranchers to connect communities through social media platforms” (#AgChat Foundation, 2011, Mission, para. 1). Similarly, #GardenChat is an online conversation where people interested in gardening come together and share stories about their personal growing experiences. These communities convene online using hashtags to locate other people tweeting about similar topics. (Twubs, 2011). In the case of #AgChat, all participants follow and contribute to a stream of tweets marked with the #AgChat hashtag (#AgChat Foundation, 2011). All participants of #GardenChat follow and contribute to a stream of tweets marked with the #GardenChat hashtag (Gardenchat, 2011).

A review of previous research in agricultural education and communications revealed no research that specifically examined social cues and levels of perceived social presence in computer-mediated communications, such as Twitter. Social presence theory has been used in the past to describe differences in face-to-face communication and CMC, but further research was needed to expose how these differences relate to levels of perceived social presence and communication interactions on a Twitter-based platform related to agriculture. Specifically, this study supported two priorities of the National Research Agenda (Doerfert, 2011): “Priority 2: New Technologies, Practices and Products Adoption Decisions” (p. 8) and “Priority 4: Meaningful, Engaged Learning in All Environments” (p. 9). This study contributed to achieving these priorities by examining ways in which communicators can more effectively engage and educate multiple audiences through emerging media.

Theoretical Framework

This study was grounded in the theory of social presence. With the increasing use of computer-mediated communication and resulting communities such as #AgChat and #GardenChat, social presence has taken on greater importance (Dunlap & Lowenthal, 2009). Social presence has been used to explain the differences between CMC and face-to-face communication (Short et al., 1976). Founded on the psychological concepts of un-mediated environments, social presence was first defined by Short et al. (1976) as some level of salience (i.e. state of being there) between two people using a communication medium. According to Short et al. (1976), social presence is an important part of the process through which people develop knowledge and opinions about other people’s characteristics and beliefs. Social presence often is described using the concepts of intimacy and immediacy, or the function of physical distance, eye contact, smiling, and “the perceptual availability of persons to one another,” respectively (Argyle & Dean, 1965; Mehrabian & Diamond, 1971, p. 282).

The concept of social presence has been defined by researchers in several different ways. Since the original theory was developed, social presence also has been defined as the level of awareness of another during communication and the resulting value of that awareness (Walther, 1992) and “the degree of feeling, perception and reaction of being connected to another intellectual entity on CMC” (Tu, 2002, p. 2). Biocca, Harms, and Burgoon (2003) described social presence as a “sense of being with another” who is symbolized in the form of “text, images, video, 3D avatars … computers and robots” (p. 1). Shen and Khalifa (2007) endorsed the concept of social presence that described a user’s experience in three dimensions: awareness, affective social presence, and cognitive social presence.

Social context is interpreted by communicators through static and dynamic cues (Sproull & Keisler, 1986). Static cues are objects such as a large desk or personal belongings, while dynamic cues include nonverbal behavior such as nodding the head or frowning (Sproull & Keisler, 1986). A lack of these social cues during communication via computers can cause deindividuation, or a state in which users feel a loss of individuality (Spears & Lea, 1992; Taylor, 2011). Specific social cues and their effects have been studied by many researchers. Missing social cues in CMC can be replaced with response time; humorous or personalized message content; or paralanguage and emoticons, such as happy and sad faces (Picciano, 2002; Richardson & Swan, 2003; Rourke, Anderson, Garrison, & Archer, 2001; Taylor et al., 2011). In a study by Tu (2002), the most commonly used emoticon was “:-),” while paralanguage was commonly expressed through punctuation, abbreviations, font styles, and unique phrases. Participants indicated that emoticons and paralanguage made the conversation more comfortable (Tu, 2002). Kalman and Rafaeli (2010) also found that time-related, nonverbal, chronemic cues such as “pauses, time of day, and silence” (p. 55) affect online communication by meeting users’ expectations about response time and encouraging or discouraging the amount of friendly content expressed in a message.

Daft and Lengel (1986) concluded that mediums without nonverbal cues result in concise, matter-of-fact communication that eliminates unnecessary interactions. For this reason, they emphasized that vague or expressive information should be transmitted through more personal mediums (Daft & Lengel, 1986). Similarly, additional research indicated that as communication moves along the continuum from face-to-face to computer-mediated interactions, it will increasingly be experienced as less personal and sentimental and more matter-of-fact (Walther, 1996). In a study conducted by Born and Miller (1999), respondents were concerned about the “effectiveness of student/professor interactions” in web-based courses and cited this concern as a barrier to distance education (p. 37). In a later study by Nelson and Thompson (2005), “lack of personal contact” was identified as a potential barrier to online learning (p. 42). Moreover, studies on social presence suggested that researchers have not come to a consensus about whether social presence is a function of communication mediums, techniques used by communicators, or a combination of mediums and techniques (Richardson & Swan, 2003).

Social presence allows online users to identify with others in a group and contributes to useful knowledge contribution (Shen, 2010). By making introductions during the first few online learning sessions, teachers can foster social presence to build trust and participation among the group (Johansen, Vallee, & Spangler, 1988). Gunawardena (1995) found that students felt more social presence when instructors interacted with “introductions and salutations.” Tu (2002) found that participants felt more social presence when teachers supported a positive attitude about keyboarding skills and gave special attention to students who needed to further develop their skills. In addition, Murphrey and Dooley (2000) suggest that a “virtual presence” be provided for online learners (p. 49). Thus, it is important for online teachers and moderators to practice techniques in support of social presence (Tu, 2002).

According to Kaplan and Haenlein (2010), business administrators have been investigating CMC to discover how social networks can be leveraged to benefit their businesses. However, researchers have found that a lack of nonverbal and paraverbal cues such as tone, pitch, and inflection in CMC can result in unorganized conversations, misperceptions, and confusion (Rhoades, 2011; Taylor et al., 2011). Other researchers have found that lack of social cues in CMC resulted in a depersonalized or anonymous experience (Taylor, 2011).

Purpose

The purpose of this study was to gain a deeper understanding of social presence among users in computer-mediated environments for agricultural conversations. The study was conducted in two-parts. Part one focused on describing social variables through the examination of logged “tweets” and part two focused on perceived social presence and participant satisfaction among users during conversations in a computer-mediated environment. The purpose of this paper, as part of the larger study, was to report findings from part one focused on describing social variables through the examination of logged “tweets.”

Methods and Procedures

Mixed-methods that combined a qualitative content analysis of Twitter transcripts and online quantitative participant surveys were employed in the overall study. Part one of the study is reported here and consisted of qualitative content analysis in which individual messages were unitized into affective, interactive, and cohesive components of social presence based on the “Model and Template for Assessment of Social Presence” (Rourke et al., 2001). The definitions of affective, interactive, and cohesive tweets were determined a priori. Tweets were treated as archival data. Approval for the study was received by the Institutional Review Board.

Guidelines established by Dillman, Smyth, and Christian (2009) were followed when conducting the survey portion of the research. Demographics of the sample collected as part of the survey research is reported in order to provide context. Fifty-five of the 148 #AgChat users completed the survey, yielding a response rate of 37.16%. The #GardenChat survey was completed by 19 of 87 users, resulting in a 21.84% response rate. Additional survey results are reported in a different paper.

Seven weeks of #AgChat and #GardenChat Twitter conversation transcripts were examined. Twitter messages and participants from #AgChat and #GardenChat conversations were selected for research based on two main criteria that supported the purpose of the study: (1) these online communities use computer-mediated communication to collaborate consistently throughout the year for a guided conversation on Twitter, and (2) these online communities support agricultural communications by helping those in the business and hobby of agriculture tell agriculture’s story to the public from their perspective (#AgChat Foundation, 2011; #GardenChat, 2011).

Individual tweets from the Twitter conversations were unitized based on the “Model and Template for Assessment of Social Presence” (Rourke et al., 2001). During unitization, only the message without any indication of the sender was viewable. Each tweet was examined for affective, interactive, and cohesive components of social presence (Rourke et al., 2001) and designated as one or all three categories

depending on two researchers’ interpretation of the message. Original tweets and retweets were included in the conversation transcripts and analyzed. Two researchers agreed on unitization of individual tweets to establish dependability. To further establish dependability and to “look for data that supported alternative explanations,” the most recent conversations from before and after the fourth week were coded in the same manner to determine that social presence dimensions in week four were typical of other weeks (See Table 1) (Merriam, 2009, p. 219).

Table 1Tweets and Users of #AgChat and #GardenChat ConversationsConversation Relevant to Survey Tweets Users Tweets Users

#AgChat #GardenChatTwo Weeks Before 1,039 137 1,286 98One Week Before 980 115 998 95Week of Survey 915 148 1,452 87One Week After 841 132 765 59Two Weeks After 1,130 117 1,162 70

Findings

Profile of Respondents

Of the 33 #AgChat survey respondents, 65 % were female and 35 % were male. Of the #GardenChat survey respondents, 72 % were female and 28 % were male. The majority of #AgChat respondents were between 26 and 45 years of age. The majority of #GardenChat respondents were between 36 and 45 years of age. Most participants responding were Caucasian. Each conversation had one Latino respondent. One respondent of #AgChat was Asian/Pacific Islander, while one respondent of #GardenChat was African American. Overall, 18 states and two countries were represented by #AgChat respondents. Multiple respondents indicated that they were located in either California (n = 4), Indiana (n = 4), Iowa (n = 3), or Wisconsin (n = 3). Other respondents were either the only one or one of two people from their specified state. Eleven states and one country were represented by #GardenChat respondents. Respondents of #GardenChat were either the only one or one of two people from their specified state.

Respondents rated themselves based on their Twitter experience. Of the #AgChat respondents, seven rated themselves as an expert, 24 rated themselves as intermediate users, and three rated themselves as novice users. No #AgChat respondents rated themselves as having no Twitter experience. Respondents also indicate how many #AgChat discussions they had participated in on a range of zero to more than ten. The most frequent responses were more than 10 (n = 15), two (n = 5), one (n = 4 ), and four (n = 3). Of the #AgChat respondents, 23 reported that the environment around them while participating in the conversation contained some background noise such as people talking or television sounds, 10 reported that it was peaceful and quiet, and one reported that it was noisy and stressful. When asked if they had ever met in person any of the other #AgChat participants before the most recent discussion, 22 #AgChat respondents reported, “Yes” and 12 reported, “No.”

Of the #GardenChat respondents, when asked to rate their level of Twitter experience, six rated themselves as expert Twitter users, seven rated themselves as intermediate users, and two rated themselves as novice Twitter users. No #GardenChat respondents rated themselves as having no Twitter experience. Respondents were also asked to indicate how many #GardenChat discussions they had participated in on a range of zero to more than ten. The most frequent responses were more than 10 (n = 9) and six (n = 2). Of the #GardenChat participants, six reported that the environment around them while participating in the conversation contained some background noise such as people talking or television sounds, six reported that it was peaceful and quiet, two reported that it was noisy and stressful, and one reported that the environment was not like any of these options. When asked if they had ever met in person any of the other #GardenChat participants before the most recent discussion, six #GardenChat respondents reported, “Yes” and nine reported, “No.”

Respondents’ Interest in Agriculture

Many respondents reported an interest in agriculture through some form of marketing and communications. Of the #AgChat respondents, 38.2 % reported that they were involved in marketing and communications, while 32.3 % reported that they were involved in production. Other frequent interests of #AgChat participants included farming and sales/business. Twelve of the 34 #AgChat respondents indicated more than one interest in agriculture. Of the #GardenChat respondents, 46.7 % reported that they were involved in marketing and communications, while 46.7 % reported that they had a home garden. Other interests of #GardenChat participants included production, green living, sales/supplies, and public gardening. Eleven of the 15 #GardenChat respondents indicated more than one interest in agriculture.

Social Presence Dimensions - #AgChat

The first archived conversation for #AgChat included 1,308 total tweets, the second included 915 tweets, and the third included 1,130 tweets. In each conversation, interactive tweets were the most prominent, with over 75 % of the total tweets falling into that category (see Table 2).

Table 2Categorization of #AgChat Tweets

Affective Interactive CohesiveWeek Before Survey

Tweets/Category 432 1,017 467Total Tweets 1,308 1,308 1,308% of Total 33.03% 77.75% 35.70%

Week Of SurveyTweets/Category 307 761 329Total Tweets 915 915 915% of Total 33.55% 83.17% 35.96%

Week After SurveyTweets/Category 217 1,006 311Total Tweets 1,130 1,130 1,130% of Total 19.20% 89.03% 27.52%

Though the #AgChat conversations officially started at 8 p.m. and ended at 10 p.m. Eastern, the conversations were archived and analyzed from 7:30 p.m. to 10:30 p.m. to view tweets from a full range of users, including those who may engage early, late, and throughout the official conversation.

It appeared that cohesive tweets in the #AgChat transcript played a prominent role in fostering a structured conversation, especially tweets from the moderator. For example, 30 minutes before each #AgChat conversation began, the moderator of #AgChat sent a tweet announcing the start of conversation, such as: “Hope folks are grabbing a snack & getting ready for #agchat cause we're T-minus 30 minutes -- please use twubs.com.” This tweet was coded as cohesive due to the use of the group pronoun, “we’re,” and affective due to the use of the word “Hope” (Rourke et al., 2001). While this tweet and others like it are directed to the group as a whole, it does not interact with specific individuals or refer to previous comments. Thus, it was not coded as interactive.

Later, the moderator sent another cohesive tweet announcing the format of the conversation that said, “Format for #agchat 1) Networking 8-8:15 pm ET 2) Moderated ?s 3) Executable idea 4) 9:55 Ask your own ?s, pitch your site or get ideas.” Some participants retweeted this message, making the message interactive. However, the original message not only reinforced the structure of the conversation, but helped foster a cohesive environment by addressing the group with guidelines that apply to everyone in the conversation.

Other cohesive tweets emphasized the format of the conversation and highlighted the importance of time. For example, the moderator noted a one-minute tardy in officially starting the conversation by sending a message that said, “Welcome all, a minute late in officially opening doors! #agchat.” Participants also were kept on schedule with warnings from the moderator such as, “Couple more minutes and then we'll be going to another female in ag question. Great job Tweeps! #agchat,” or “Q3 coming on up and we'll be moving on to new topic... #agchat.” All of these tweets were coded as cohesive due to the use of greetings and group pronouns. One of these tweets was coded as interactive since the phrase “Great job” complimented others. While cohesive tweets seemed to maintain structure of the conversation, it appeared that affective tweets may have helped participants become acquainted with each other. Participants were asked by the moderator to provide meaningful introductions that include their locations and interests in agriculture. Though the moderator sent out a cohesive tweet to request introductory information such as, “Guidelines for #agchat, 8-10pmET 1) intro w/ location & #ag interest 2) stay on topic 3) start,” the responses were affective due to the disclosure of information. The moderator also sent a tweet directed to Twitter users who may have been watching the streaming conversation but not introducing themselves; “Intro time. Tell us who you are, even if you are lurking tonight. #agchat.” Some participants were located in the eastern, central, and western part of the United States, in states such as New York, Oklahoma, and California. Other participants were located in Canada. Participants’ relationships with agriculture ranged from those in academia, such as a judging team coach or adviser, to farmers to people with little or no agricultural background. Moreover, many tweets during the first 15 minutes of the conversation included the user's name, state, and relationship to agriculture, all of which fall under self-disclosure, and thus, affective responses.

In addition to serving as introductory messages, it appeared that affective tweets may have provided unrequested information. Rather, affective tweets often included information that was irrelevant to the main topic of conversation. During the time allowed for introductions, participants not only shared the requested information, but shared their most recent activity, what they were doing while participating in #AgChat, and even their food and beverage choices. One participant tweeted, “Will try not to get my

keyboard greasy from the cheese curd goodness since I'm tweeting in from my new #Wisconsin home for #agchat tonight.” Even after the time allotted for introductions, participants who joined the conversation late contributed with similar information.

It appeared that the most prominent category of tweets, interactive, occurred during the middle of the conversation when participants were asked questions and given the opportunity to respond. After introductions, participants were asked between 12 and 14 questions that related to agriculture. Responses to these questions were, even if nothing else, coded as interactive due to the fact that they were responding to a previous comment or question. These tweets were often recognized by a letter “Q” followed by the current question number. Though participants were asked and reminded to indicate the question they were responding to by a cohesive message from the moderator, questions containing the “Q” were coded as interactive. For example, if a participant was responding to question one, they would include “Q1” in their response. Some participants responded to the questions by sending a message to the entire group. In other words, some responses were not directed at another user and did not retweet other users’ messages. However, some participants seemed to engage in conversation with just one or two individuals instead of the group as a whole by using specific Twitter usernames in the beginning of their responses. This situation is illustrated by tweets such as, “@TruffleMedia very cool that you had it ‘up your sleeve’ #Agchat.” Tweets such as these were coded as cohesive for the use of an individual users’ name. Still, some participants retweeted other participants’ messages either with or without an additional comment. These kinds of tweets were coded as interactive due to the reference of a previous message. Many participants sent messages in reply to questions that included emoticons such as, “Q12: Every now and then step outside your comfort zone ;-) #agchat.” These tweets were coded as affective for the use of a text-based expression of emotion. Before the last five minutes of the conversation, a tweet was sent out announcing the time allotted for personal pitches. The tweet said, “You've done great and it's now PITCH time. Feel free to share your "stuff", ask a ? of your own, get feedback. #agchat.” This announcement tweet was coded as interactive due to the complimentary nature. It seemed that tweets in response to this interactive message were more affective. Many participants expressed self-disclosure by sending links for personal blogs and websites, as well as tweets with personal recommendations and information

As the #AgChat conversation came to a close, many participants expressed appreciation for an enjoyable conversation through affective and cohesive tweets. These tweets noted the end of the conversation by saying things like, “that’s a wrap” and “Very well done.” Some latecomers expressed disappointment for missing the conversation with affective tweets that included statements such as, “Sad I missed #AgChat ...”

Overall, the #AgChat conversations appeared to be very structured through many cohesive tweets by the moderator that gave instructions for format and introductory content, as well as indicators of time. Questions and responses in interactive tweets were easily followed with the use of “Q” followed by the question number before each question and before participants’ responses. Participants generally seemed to be speaking to the #AgChat community as a whole through interactive and cohesive tweets, with exceptions of cohesive and interactive comments directed to individual users by a few individuals. If the conversation were compared to a traditional (not Web-based) conversation, it would have been comparable to a situation where a moderator stands in front of the room and asks a group of people one question at a time while each person responds to the entire group with his or her answer.

Social Presence Dimensions - #GardenChat

The first archived conversation for #GardenChat included 998 total tweets, the second included 1,452 tweets, and the third included 1,162 tweets. Of these, interactive tweets were the most prominent (see Table 3).

Table 3Categorization of #GardenChat Tweets

Affective Interactive CohesiveWeek Before Survey

Tweets/Category 368 659 457Total Tweets 998 998 998% of Total 36.87% 66.03% 45.79%

Week Of SurveyTweets/Category 340 1,067 727Total Tweets 1,452 1,452 1,452% of Total 23.42% 73.48% 50.07%

Week After SurveyTweets/Category 258 844 688Total Tweets 1,162 1,162 1,162% of Total 22.20% 72.63% 59.21%

Though the #GardenChat conversations officially started at 9 p.m. and ended at 10 p.m. Eastern, the conversations were archived and analyzed from 8:30 p.m. to 10:30 p.m. to view tweets from a full range of users, including those who may engage early, late, and throughout the official conversation.

It appeared that tweets before the #GardenChat conversation began were interactive and may have functioned as a way to make online users aware of the upcoming conversation. While these interactive tweets in the #GardenChat transcript did not seem to indicate a specific format, they did seem to indicate that the conversation would soon begin. Before the advertised start of #GardenChat at 9 p.m. Eastern, tweets were sent that indicated participants were preparing for the evening’s conversation. These tweets included statements such as, “Getting ready for #gardenchat tonight? ...” and “T minus 25< and counting!!” These tweets seemed to encourage other potential participants and were coded as interactive and affective due to the question sent to others and the expression of emotion through punctuation.

As 9 p.m. Eastern approached, participants began to send messages with more of a social function such as greetings like, “Hello! #gardenchat.” Information such as name or location was not requested of participants. However, some participants indicated their location by tweets such as “#gardenchat hello from the drought land TX.” These tweets were coded as affective due to the volunteered, personal information that expresses self-disclosure. Many participants did not include this type of information in their introductions. Therefore, many tweets in the first few minutes of the conversation were interactive or cohesive.

Participants were welcomed by the moderator at the beginning of the conversations with a message that said, “Welcome to #gardenchat : 9-10 p.m. ET on Twitter ...” Some participants continued to send greeting-type messages as the conversation began. These types of messages were coded as cohesive due to the use of words that address the group as a united entity.

It appeared that participants used interactive tweets to gain information about the upcoming conversation. For example, some participants in multiple conversations sent messages that said, “@TheGardenChat Topic tonight? #gardenchat” and “Hi #gardenchat! What's the topic tonight? #gardenchat.” These tweets were later addressed in the conversation through additional interactive tweets. Many participants’ interactive tweets related to gardening or questions asked by the moderator, while many affective tweets related to participants’ snacks, favorite dining venues, and other topics unrelated to gardening.

After participants were welcomed, the greetings became fewer and fewer. It appeared that questions in interactive tweets were sent to the group by random participants as they were developed rather than having been planned ahead of time and sent out by the moderator. Participants were not asked to indicate what question they were responding to, so responses to each question were not obviously apparent. In two of the three archived conversations, some tweets indicated that participants were watching a live streaming video of the moderator; “OMG! I'm on Ustream and I can see and hear ya'll! So much fun #gardenchat.” Tweets like this one were coded as affective for the expression of emotion through punctuation and cohesive for the use of the group pronoun “ya’ll.” Tweets in the #GardenChat conversation seemed to imitate many small groups of people in a room rather than one large group of people having a discussion. As 10 p.m. Eastern approached, there was no warning that the conversation was about to end. Many users noted the end of the conversation and complimented others with affective tweets such as, “This was fun to watch. Thanks. Have to go see if my garden is OK after the hard rain. Night. #gardenchat.”

Overall, tweets in #GardenChat seemed to surround several small conversations between several individuals more than one conversation among all participants. It did not appear that one category of tweets heavily influenced the conversation more than another category. No formal structure or attention to time was apparent through a concentrated collection of tweets. Participants generally seemed to be speaking to other individual users rather than the #GardenChat community as a whole.

Conclusions and Discussion

For this study, the definition of social presence was operationalized as the level of salience between two people using a communication medium (Short et al., 1976). Social presence was viewed as a function of communication mediums and social variables found within #AgChat and #GardenChat messages. Based on the finding that most tweets in both conversations were interactive, it seemed that social presence on Twitter is often created through interactive responses such as asking other people questions and referring to previous comments. This conclusion aligned with previous research that says reaching out to others contributes to social presence, helps users to identify with others in a group, and contributes to useful knowledge contribution (Shen, 2010).

Further, Twitter messages indicated that it might be possible for interactive responses, as well as cohesive and affective responses, to function differently. For example, many interactive responses in the #AgChat conversation took place in a structured format during the time when the moderator asked questions and gave participants the opportunity to respond. Interactive responses in the #GardenChat conversation took place in a less structured environment where participants were engaging in with others through a combination of affective and interactive responses. Further, cohesive tweets in #AgChat helped maintain conversation structure by announcing important times and format for the upcoming conversation, while cohesive tweets in #GardenChat announced the upcoming conversation,

encouraged others to participate, and acknowledged participants’ contributions as a whole. Affective tweets in #AgChat contained more personal information such as location and occupation, while affective tweets in #GardenChat focused on expression of emotions. These conclusions aligned with previous research that found Twitter hosts a variety of users with different goals and interests (Java, Finin, Song, & Tseng, 2007) and that social presence can be separated into different dimensions (Rourke et al., 2001).

Overall, it appeared that social dimensions in #AgChat and #GardenChat conversations involve mostly messages that acknowledge and express appreciation for participants in the group. Participants do not appear to be heavily concerned with developing and maintaining close relationships with other participants. Rather, most social dimensions supported a general relationship founded on commonalities of agriculture and gardening. Outside of these general topics in these one or two hour conversations, it did not seem that participants cared to associate closely with other participants. This conclusion supports previous research that Twitter users fall into different categories depending on their intentions, and that if Twitter is irrelevant to their intentions, they are less likely to use it (Java et al., 2007; Dunlap & Lowenthal, 2009). It can be suspected that the moderator of #AgChat and #GardenChat conversations greatly influence the social dynamics of participants. This conclusion aligned with previous research that says is important for online moderators to practice techniques in support of social presence (Tu, 2002).

Recommendations

Many studies on social presence have been conducted to explain the differences between CMC and face-to-face communication (Short et al., 1976). More research should be conducted to directly compare social presence dimensions in a CMC and face-to-face environment. For example, it would be helpful for a researcher to compare the social presence dimensions that exist among a sample group engaging in conversation in a face-to-face environment with the social presence dimensions that exist among the same sample group engaging in conversation in a CMC environment. To build on this study, further research should be conducted to investigate the best methods of supporting components of social presence. Future research should also be conducted to improve methods of measuring social presence, especially since some aspects of social presence have been deemed highly subjective and are thought to be measured best by self-report tools (Biocca & Harms, 2002). Finally, further research should examine social presence dimensions among varying populations and sample groups that convene about topics outside of agriculture or subtopics of agriculture such as sustainability, production, organics, and more. Members of these groups should include individuals outside of #AgChat, #GardenChat, and Twitter to investigate social presence dimensions within other forms of computer-mediated communication.

Implications

Studies on social presence and CMC have been conducted to investigate the possible benefits that CMC can provide for businesses (Kaplan & Haenlein, 2010). However, some researchers have found that a lack of nonverbal and paraverbal cues such as tone, pitch, and inflection in CMC can result in unorganized conversations, misperceptions, and confusion (Rhoades, 2001; Taylor et al., 2011). Other researchers have found that lack of social cues in CMC result in a depersonalized or anonymous experience (Taylor, 2011). However, both conversations in this study, whether structured or unstructured, portrayed elements of social presence and appeared to be successful. Therefore,

agricultural communicators should be confident that with certain social presence dimensions, Twitter conversations can be a successful way to communicate agricultural stories to others.

References

#AgChat Foundation. (2011). FAQ on the Twitter Convos #AgChat & #FoodChat. Retrieved May 2011 from http://agchat.org/about/twitter-agchat-foodchat%20

Argyle, M., & Dean, J. (1965). Eye-contact, distance and affiliation. Sociometry, 28(3), 289-304.

Bartter, A., Fellow, A., Fernandez, N.P., Hidalgo, R., Martin, L., Underdue, S.,Vu, M., & Won, M. (2009). New digital media. Retrieved 2010 from http://vpadmin.fullerton.edu/AssociateVP/OrgDev/UnivLeadAcademy/LeadDevProg/ProjectReports/New_Digital_Media.pdf

Biocca, F., & Harms, C. (2002). Defining and measuring social presence: Contribution to the networked minds theory and measure. Retrieved 2010 from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.84.8350

Biocca, F., Harms, C., & Burgoon, J. (2003). Towards a more robust theory and measure of social presence: Review and suggested criteria. Presence: Teleoperators and virtual environments. Manuscript. Retrieved 2010 from http://www.mitpressjournals.org/doi/pdf/10.1162/105474603322761270

Born, K., & Miller, G. (1999). Facultry perceptions of web-based distance education in agriculture. Journal of Agricultural Education. 40(3). 30-39.

Chan, A. (2008). Social Media: Paradigm Shift. Retrieved April 24, 2011, from http://www.gravity7.com/paradigm_shift_1.html

Daft, R. L., & Lengel, R. H. (1984). Information richness: A new approach to managerial behavior and organizational design. Research in Organizational Behavior 6,191-233.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, Mail, and Mixed-modeSurveys: The Tailored Design Method (3rd ed.). Hoboken, NJ: John Wiley & Sons, Inc.

Doerfert, D. L. (Ed.). (2011). National research agenda America Association for Agricultural Eductaion’ research priority areas for 2011-2015 Lubbock TX Texas Tec University Department of Agricultural Education and Communications

Dunlap, J., & Lowenthal, P. (2009). Tweeting the night away. Journal of Information Systems Education. Retrieved from www.allbusiness.com

Ferguson, D., & Pettit, C. (2009). lrnchat: An introduction to Twitter's weekly learning chat. ELearn, 2009 doi:10.1145/1626550.1636673

Gardenchat. (2011). Welcome to the homepage of #gardenchat. Retrieved May 2011from http://www.bggarden.com/gardenchat

Gunawardena, C. N. (1995). Social presence theory and implications for interaction and collaborative learning in computer conferences. International Journal of Educational Telecommunications, 1(2), 147-166. Charlottesville, VA: AACE

Honeycutt, C., & Herring, S. (2009). Beyond microblogging: Conversation and collaboration via Twitter. Proceedings of the 42nd Hawaii International Conference on System Sciences, 42, 1-10.

Internet World Stats. (2011). Internet usage and population in North America. Retrieved from http://www.internetworldstats.com/stats14.htm

Jansen, B., & Zhang, M. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188. doi:10.1002/asi.21149

Java A., Finin, T., Song, X., & Tseng, B. (2007). Why we Twitter: Understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (WebKDD/SNA(TRUNCATED)

Johansen, R., Valle, J., & Spangler, K. (1988). Teleconferencing: electronic group communication. In R. S. Cathcart & L. A. Samovar (Eds.), Small group communication: A reader (5th ed., pp. 140-154). Menlo Park, CA: Institute for the Future.

Kalman, Y., & Rafaeli, S. (2010). Online pauses and silence: Chronemic expectancy violations in written computer-mediated communication. Communication Research 38(1) 54-69. doi: 10.1177/0093650210378229

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. doi: 10.1016/j.bushor.2009.09.003

Mehrabian, A., & Diamond, S. (1971) Seating arrangement and conversation. Sociometry, 34(2). 74 (August) :281-289.

Merriam, S. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA. Jossey-Bass.

Miller, S. (2010). Enhance your twitter experience. Learning and Leading with Technology, 37(8), 14-17.

Murphrey, T., & Dooley, K. (2000). Perceived strengths, weaknesses, opportunities, and threats impacting the diffusion of distance education technologies in a college of agriculture and life sciences. Journal of Agricultural Education. 41(4). 39-50.

Nelson, S., & Thompson, G. (2005). Barriers perceived by administrators and facultry regarding the use of distance education technologies in preservice programs for secondary agricultural education teachers. Journal of Agricultural Education. 46(4). 36-48.

Picciano, A. (2002). Beyond student perceptions: Issues of interaction, presence, and performance in an online course. JALN, 6(1), 21-40.

Rhoads, M. (2011). Computer-mediated communication: What does theory tell us and what have we learned so far? Journal of Planning Literature, 25(2), 111-120.

Richardson, J., & Swan, K. (2003). Examining social presence in online courses in relation to students' perceived learning and satisfaction. JALN, 7(1), 68-88.

Rourke, L., Anderson, T., Garrison, R. D., & Archer, W. (2001).Assessing social presence in asynchronous text-based computer conferencing. Journal of Distance Education, 14(2), 50-71. Retrieved from http://www.mendeley.com/research/assessing-social-presence-in-asynchronous-textbased-computer-conferencing-1/

Spears, R, & Lea, M. (1992). Social influence and the influence of the ‘‘social’’ in computer-mediated communication. In M. Lea (Ed.), Contexts of computer-mediated communication (pp. 30–65). London: Harvester-Wheatsheaf.

Spitzberg, B. (2006). Preliminary development of a model and measure of computer- mediated communication (CMC) competence. Journal of Computer-Mediated Communication, 11, 629-666.

Shen, K. N., & Khalifa, M. (2007.) Exploring multi-dimensional conceptualization of social presence in the context of online communities. In: Human-Computer Interaction International 2007, Beijing, China.

Short, J., Williams, E., et al. (1976). The social psychology of telecommunications, London: John Wiley & Sons.

Sproull, L., & Kiesler, S. (1986). Reducing social context cues: Electronic mail in organizational communications. Management Science, 32(11), pp. 1492-1512. Retrieved from http://www.jstor.org/stable/2631506

Taylor, M., Jowi, D., Schreier, H., & Bertelsen, D. (2011). Students' perceptions of E-mail interaction during student-professor advising sessions: the pursuit of interpersonal goals. Journal of Computer-Mediated Communication, 16(2), 307-330. doi:10.1111/j.1083-6101.2011.01541.x

Tu, C. (2002). The impacts of text-based CMC on online social presence . Journal of Interactive Online Learning, 1(2). 1-24.

Twitter. (2011). About. An information network, what are hashtags?. Retrieved April 2011 from http://www.twitter.com

Twubs. (2011). #hashtags made useful. Retrieved September 2011 from http://twubs.com/

Walther, J. (1992). Interpersonal effects in computer-mediated interaction; a relational perspective. Communication Research 19(1): 52-90.

Zhao, D., & Rosson, M. B. (2009). How and why people Twitter: The role that micro-blogging plays in informal communication at work. Proceedings of the ACM 2009 International Conference on Supporting Group Work (GROUP '09). ACM, New York, NY, USA,. 243-252. doi:10.1145/1531674.1531710

Participant Satisfaction Related to Social Presence in Agricultural Conversations using Twitter: Implications for Agricultural Communications

Kelly M. Pritchett, Texas A&M UniversityTheresa Pesl Murphrey, Texas A&M University

Traci L. Naile, Oklahoma State University

Abstract

Communication has shifted from predominantly face-to-face environments to greater use of computer-mediated environments (CMC) such as social networking sites for sharing information, meeting new people, and learning. Aspects of CMC related to perceptions of social presence impact the way communication occurs in un-mediated environments. This study examined perceived social presence, participant satisfaction, and relationships between social presence and satisfaction among Twitter users during streaming conversations. Data were collected through an online questionnaire that was created using qualtrics.com and made available to respondents over a one-week period. Two groups of survey respondents agreed with 10 out of 21 and 13 out of 21 statements about social presence and 10 out of 13 and 12 out of 13 statements about satisfaction. Findings indicated that positive and negative relationships exist between social presence and satisfaction. It was concluded that participants felt they were in close virtual proximity with other participants and that social presence can be fostered through text-based variables, such as emoticons, to compensate for lack of nonverbal or face-to-face cues. Therefore, agricultural educators and communicators should use techniques that foster social presence to support virtual relationships and circulate honest agricultural information through chatting, messaging, and blogging.

Introduction and Literature Review

In recent years, Internet media and social networking have become the main sources of news and information for many people (Prasarnphanich & Wagner, 2011). In North America, almost 78% of the population uses the Internet (Internet World Stats, 2011), with social networking sites being used by 50% of young adults (Lewandowski, Rosenberg, Parks, & Siegel, 2011). The Internet, originally an objective research tool of the information age, has grown to become a powerful catalyst for societal change where people are able to network through chatting, messaging, and blogging (Bartter et al., 2009). These types of social activities have allowed individuals to collaborate and form communities in which the contributions of each participant support the group as a single entity. These groups often seek new information, expertise, and informal interactions with others through computer-mediated communication (Prasarnphanich & Wagner, 2011).

Computer-mediated communication (CMC) is “synchronous or asynchronous electronic mail and computer conferencing” by which communicators send and receive text-based messages via computers (Walther, 1992, p. 52). Synchronous communication allows users to communicate in real-time, while asynchronous communication allows users to send and receive messages at their convenience (Tu, 2002). One social platform called Twitter is a microblogging tool that allows users to send and receive short text messages of 140 characters called tweets (Twitter, 2011; Jansen & Zhang, 2009; Zhao & Rosson, 2009). Tweets can be marked with a hash tag to identify topics and allow users to find other tweets about different topics of interest (Twitter, 2011). #AgChat and #GardenChat are two agricultural examples of social communities that use hash tags. #AgChat and #GardenChat are weekly moderated

conversations on Twitter for farmers, ranchers, and other people interested in agriculture and gardening. #AgChat and #GardenChat conversations can be viewed by searching for the #AgChat and #GardenChat hash tags in one of the many third-party applications that enhance the Twitter experience. Twitter.com has approximately 175 million registered users (Twitter, 2011)

With the growing popularity of virtual technologies and their resulting social communities such as #AgChat and #GardenChat, communication has shifted from predominantly face-to-face communication to greater use of “online computer-mediated communication (CMC)” (Zhao & Rosson, 2009, p. 243). Aspects of CMC alter face-to-face communication, including fewer social cues and a sense of depersonalization or deindividuation (Spears & Lea, 1994; Taylor, Jowi, Schreir, & Bertelsen, 2011). Research on CMC has found that perceptions of social presence can significantly influence the way people communicate and relate to overall satisfaction with a communication experience (Lowenthal, 2009).

Examinations of face-to-face communication and CMC have been conducted in relation to social presence theory to look for differences; however, in a search of previous literature no research was identified that examined perceived social presence in agricultural communications. Further research was needed to determine differences related to participant perception and communication interactions in agriculture-focused venues with CMC such as Twitter. Two National Research Agenda (Doerfert, 2011) priorities were addressed: “Priority 2: New Technologies, Practices and Products Adoption Decisions” (p. 8) and “Priority 4: Meaningful, Engaged Learning in All Environments” (p.9). This study contributes to achieving these priorities by examining ways in which communicators can more effectively engage and educate multiple audiences through emerging media.

Theoretical Framework

Social presence and satisfaction within computer-mediated communication was the focus of this study. Thus, the theoretical framework of the study was social presence. The first definition of social presence was defined as the level of salience between two people using a communication medium (Short et al., 1976). Since then, many researchers have developed their own versions of social presence and applied them to computer-mediated communication as a function of medium characteristics, as well as a function of user adaptations to social context (Richardson & Swan, 2003; Walther, 1992). For example, to compensate for lack of social cues in computer-mediated communication, a user may insert emoticons or personalize their messages (Picciano, 2002; Richardson & Swan, 2003; Rourke, Anderson, Garrison, & Archer, 2001; Taylor et al., 2011).

Social presence is a core concept in online learning and distance education. Studies have shown correlations between social presence and student satisfaction (Gunawardena, 1995; Gunawardena & Zittle, 1997; Richardson & Swan, 2003), social presence and learning communities (Rourke et al., 2001; Rovai, 2002), and social presence and perceived learning (Richardson & Swan, 2003). Some researchers have suggested that learning online can be just as successful as learning in a classroom when they found that nonverbal behaviors contributing to social presence were independent of learning in a student-teacher relationship (Taylor et al., 2011). Gunawardena and Zittle (1997) found it important for instructors to develop skills to create social presence when providing feedback to individuals.

In studies about online collaborative learning, researchers found that learners placed high importance on feelings of “connectedness and belonging” (Hara, Bonk, & Angeli, 2000; Harasim, 1993; Kitchen & McDougall, 1998; So & Kim, 2005). Gunawardena and McIsaac (2004) found that social presence affects

distance learners’ “perceptions of psychological distance,” or immediacy with their teacher and fellow learners. This aligns with research in distance education identifying a trend that defines distance in terms of psychological aspects rather than physical proximity (Garrison, 2000; So & Brush, 2008).

Measuring Social Presence

Measurement of social presence has been an evolving practice that started with a survey instrument through which 17 learner reactions were captured on a range of bipolar scales, such as stimulating / dull, personal / impersonal, and sociable / unsociable (Gunawardena, 1995). After the GlobalEd conference in 1993, Gunawardena and Zittle (1997) developed a 61-item questionnaire that measured “participants’ responses to CMC,” conference experience, and factors suspected to influence CMC satisfaction. The majority of the conference instrument included five-point Likert-scale items about nine different areas: “1) social presence; 2) active participation in the conference; 3) attitude toward CMC; 4) barriers to participation, which included technical problems and lack of access; 5) confidence in mastering CMC; 6) perception of having equal opportunity to participate in the conference; 7) adequate training in CMC at participant's site; 8) technical skills and experience using CMC; and 9) overall satisfaction with the GlobalEd conference” (Gunawardena & Zittle, 1997, p. 14).

Some aspects of social presence have been deemed highly subjective and are thought to be measured best by self-report tools that indicate social awareness (Biocca & Harms, 2002). While self-report measures of social awareness such as eye fixation or body movement can be observed, these observed measures are difficult to collect and may not be directly related to social awareness (Biocca & Harms, 2002). Accordingly, Rourke et al. (2001) classified social presence into interactive, affective, and cohesive responses to conduct a qualitative study on computer-mediated conversation transcripts and found problems with observational tools that related to the challenges of accurately transcribing “real-time, face-to-face interactions” (p. 6). To overcome challenges such as these, some researchers turned to conferencing software that “automatically and faithfully records all online interactions in a machine-readable format” (Rourke et al., 2001, p.6).

In 2002, Tu created the Social Presence and Privacy Questionnaire (SPPQ) to measure students’ perceptions of social context, online communication, interactivity, and privacy. Tu collected data through interviews, direct observation, document analysis, and a survey. Finally, parts of the satisfaction scale by Gunawardena and Zittle (1997), SPPQ by Tu (2002), and previous research by Driver (2002) and Kitchen and McDougall (1998) were merged to form the Collaborative Learning, Social Presence, and Satisfaction (CLSS) questionnaire (So & Brush, 2008; Lowenthal, 2009). The CLSS questionnaire captures general demographic information, satisfaction, and social presence (So & Brush, 2008). Despite proposed alternative social presence scales (Kreijns, Kirschner, Jochems, & van Buuren, 2010) and arguments for multidimensional approaches (Russo & Benson, 2005), most researchers are comfortable with or adapt to the instruments developed by Gunawardena and Zittle (1997), Rourke et al. (2001), or Tu (2002) (Lowenthal, 2009).

Purpose

Understanding social presence in the context of agricultural conversations in computer-mediated environments was the focus of this study. The overall study consisted of two parts. This portion was focused on identifying social variables through qualitative examination of tweets. The purpose of this paper is to report the findings from the second part of the study. The objectives that guided this part of

the study included: a) describe #AgChat and #GardenChat users’ perceptions of social presence during a Twitter conversation; b) describe #AgChat and #GardenChat users’ satisfaction with a Twitter conversation; and c) describe relationships between perceptions of social presence and satisfaction during #AgChat and #GardenChat conversations.

Methods and Procedures

Mixed methods were utilized for the entire study. However, the findings reported here focus on data collected from the quantitative surveys administered to participants engaged in the study. Individuals who had participated during a specified seven weeks of #AgChat and #GardenChat Twitter conversations were recruited to complete the survey. Each #AgChat conversation occurred once a week for two hours, while each #GardenChat conversation occurred once a week for one hour. Specifically, participants who contributed to the fourth week of these conversations were directly requested via Twitter to participate. The survey was made available for one week after each conversation.

Survey Instrument Design

The survey instrument was adapted from the four sections and 56 items in the Collaborative Learning, Social presence, and Satisfaction (CLSS) questionnaire to have 51 items (So & Brush, 2008). Five survey items were not relevant to the study and were excluded. Section one of the survey asked participants questions related to age, ethnicity, Twitter experience, and number of #AgChat or #GardenChat conversations in which they had participated. Section two of the survey asked participants about their satisfaction with their ability to learn and understand during the conversation, as well as their satisfaction with the diversity of topics in #AgChat and #GardenChat. The third section asked participants to indicate the amount of learning and sharing ideas that took place during #AgChat and #GardenChat. Section four of the survey asked participants to indicate where they participate in conversations, as well as their comfort level with familiar and unfamiliar conversation topics.

Validity of the survey instrument was established through previous studies that used similar instruments (Gunawardena & Zittle, 1997; Tu, 2002; Driver, 2002; Kitchen & McDougall, 1998). Data from Q14, Q15, and Q16, which were the only questions containing scaled data, were used to calculate a Cronbach’s alpha. The Cronbach’s alpha coefficient estimates the internal consistency of attitude scales. The coefficient for #AgChat was 0.85 and the coefficient for #GardenChat was 0.92.

Quantitative Data Collection

Quantitative data collection took place during the fourth week of August 2011 on Monday and Tuesday during the regularly scheduled #GardenChat, and #AgChat conversations, respectively. Recruitment of participants was based on the guidelines provided by Dillman, Smyth, and Christian (2009); modifications were made to recruitment procedures to accommodate the quick pace of Twitter interactions. Previous studies using similar methods were not found.

The moderators of each Twitter conversation agreed to send a Twitter message with the link to the survey at the end of the conversation. Survey responses for #GardenChat were collected from Aug. 22, 2011 to Aug. 29, 2011. Survey responses for #AgChat were collected from Aug. 23, 2011 to Aug. 30, 2011.

The moderator of #GardenChat tweeted the survey link at 9:26 p.m. (26 minutes after the end of the #GardenChat conversation). The Twitter message said, “If anyone is interested check out [researcher’s Twitter handle] Survey at http://ow.ly/6a2yo #GardenChat.” The moderator of #AgChat tweeted the survey at 8:56 p.m. (four minutes before the end of the #AgChat conversation). The Twitter message said, “Let’s help [researcher’s twitter handle] with her graduate thesis by taking this survey! http://ow.ly/69wNv #AgChat.”

For each conversation, the researcher retweeted the moderator’s original tweet immediately after the moderator sent out the survey link. The researcher retweeted the moderator’s tweet six times, eight hours apart, starting eight hours after the end of each conversation. The researcher also sent out six original Twitter messages for each conversation, eight hours apart, starting at 9:00 a.m. on the morning after each conversation. Based on response rates, three days after the conversations took place, the researcher sent a series of five reminder tweets. The first two reminder tweets were sent out eight hours apart and the last three reminder tweets were sent out 24 hours apart. To specifically target individuals that participated in #AgChat and #GardenChat on August 22 and August 23, the survey was made available until the day of #GardenChat and #AgChat’s next scheduled conversation for a total of seven days. In addition, reminder tweets asked for individuals who had participated in the most recent conversation. The accessible population of #GardenChat and #AgChat users during the seven days that the survey was available was used to represent the target population of #GardenChat and #AgChat users who participated during that week’s conversation. During the week of the survey, the #AgChat conversation contained 915 tweets from 148 users. Fifty-five of these users responded to the survey for a response rate of 37.16%. The #GardenChat conversation contained 1,452 tweets from 87 users. Nineteen of these users responded to the survey for a response rate of 21.84%. The numbers of participants during the week of the survey was representative of previous conversations held by these two groups. Low response rate is recognized as a limitation of the study. However, it is believed that findings from this study, even with a low response rate, can assist agricultural educators and communicators in gaining a better understanding of social media and social presence.

The Statistical Package for Social Sciences (SPSS®) was used to analyze data. Descriptive statistics, including means, standard deviations, medians, frequencies, percentages, and correlations were calculated to interpret the data. In order to measure participant responses on satisfaction and social presence, a scale was used to measure the mean response where 1.00 – 1.44 = strongly disagree, 1.45 – 2.44 = disagree, 2.45 – 3.44 = neutral, 3.45 – 4.44 = agree, and 4.45 – 5.00 = strongly agree. This scale utilized means in order to distinguish ranges of agreement. Correlations were used at the p <.05 level to analyze the relationships between social presence and satisfaction. Survey results were examined to describe participants’ demographic information, perceived levels of satisfaction, perceived levels of social presence and possible correlations among satisfaction and social presence. Institutional Review Board approval was received to conduct this study.

Findings and Discussion

Participant Profile

Overall, 18 states and two countries were represented by #AgChat respondents. Multiple #AgChat respondents indicated that they were located in either California (n = 4), Indiana (n = 4), Iowa (n = 3), or Wisconsin (n = 3). Other respondents were either the only one or one of two people from their specified state. Eleven states and one country were represented by #GardenChat respondents. Respondents of

#GardenChat were either the only one or one of two people from their specified state. Most participants responding were Caucasian. Each conversation had one Latino respondent. One respondent of #AgChat was Asian/Pacific Islander, while one respondent of #GardenChat was African American. Of the #AgChat survey respondents, 65% were female and 35% were male. Of the #GardenChat survey respondents, 72% were female and 28% were male. More than half of the #AgChat respondents were between 26 and 45 years of age. More than half of the #GardenChat respondents were between 36 and 45 years of age.

Participants rated their Twitter experience as expert, intermediate, novice, or a user with no experience. Seven #AgChat respondents rated themselves as expert users, 24 as intermediate users, three as novice users, and none as having no Twitter experience. Six #GardenChat respondents rated themselves as expert users, seven as intermediate users, two as novice users, and none as having no Twitter experience. When asked to indicate the number of discussions in which they had participated, the most frequent responses for #AgChat respondents were more than 10 (n = 15), two (n = 5), one (n = 4 ), and four (n = 3). The most frequent responses for #GardenChat respondents were more than 10 (n = 9) and six (n = 2). Twenty three #AgChat respondents and six #GardenChat respondents reported that the environment around them while participating in the conversation contained some background noise such as people talking or television sounds. Ten #AgChat respondents and six #GardenChat respondents indicated that their environment was peaceful and quiet. One #AgChat respondent and two #GardenChat respondents reported that their environment was noisy and stressful. Twenty two #AgChat respondents and six #GardenChat respondents reported, “Yes” when asked if they had ever met in person any of the other participants in their conversation, while 12 #AgChat respondents and nine #GardenChat respondents reported, “No” to this survey item.

Many respondents reported more than one interest in agriculture. Marketing and communications were interests of 38.2% of #AgChat respondents and 46.7% of #GardenChat respondents. Other frequent interests of #AgChat participants included production, farming, and sales/business. Other frequent interests of #GardenChat respondents included home gardening, production, green living, sales/supplies, and public gardening.

Users’ Satisfaction

To measure participant responses on satisfaction, a scale was used to measure the mean response where 1.00 – 1.44 = strongly disagree, 1.45 – 2.44 = disagree, 2.45 – 3.44 = neutral, 3.45 – 4.44 = agree, and 4.45 – 5.00 = strongly agree. #AgChat respondents agreed with 10 out of 13 statements about satisfaction. For example, respondents agreed with the statements that as a result of their participation in #AgChat, they made acquaintances electronically in other parts of the country and/or world (M = 4.35, SD = .95, Mdn = 5.00), and that they were able to learn through the medium of computer-mediated communication (M = 4.03, SD = .79, Mdn = 4.00). Respondents were neutral about statements related to diversity of topics prompting them to participate in the discussion (M = 3.29, SD = 1.12, Mdn = 3.00), their level of learning being at the highest quality during the conversation (M = 3.21, SD = 0.81, Mdn = 3.00), and the amount of effort put forth in learning computer-mediated communication skills to participate in the conversation (M = 2.85, SD = 1.13, Mdn = 3.00). #AgChat respondents did not “disagree” with any statements related to satisfaction, indicating that the experience was positive.

#GardenChat respondents agreed with 12 out of 13 statements about satisfaction. For example, respondents agreed with the statement that as a result of their experience, they would like to participate in another discussion in the future (M = 4.40, SD = 0.83, Mdn = 5.00) and that they were stimulated to do additional readings or research about topics discussed during #GardenChat (M = 4.33,

SD = .62, Mdn = 4.00). Respondents agreed least with the statement that their level of learning that took place in the discussion was of the highest quality (M = 3.93, SD = 1.03, Mdn = 4.00). Respondents were neutral about the statement related to the amount of effort put forth in learning computer-mediated communication skills to participate in the conversation (M = 2.93, SD = 1.22, Mdn = 3.00). #GardenChat respondents did not “disagree” with any statements related to satisfaction, indicating that the experience was positive.

Relationships between Social Presence and Satisfaction

Pearson’s product-moment correlation coefficients were calculated to find statistical relationships between social presence and satisfaction at the p <.05 level. For #AgChat respondents, the social presence item stating that computer-mediated communication messages convey feeling and emotion showed a low to medium, positive correlation with six other statements about satisfaction. The strongest of these correlations related to the level of learning that took place (r = .52), ability to learn through the medium of computer-mediated communication (r = .50), and the discussion as a useful experience (r = .48). Responses showed a low, negative correlation between the social presence statement that computer-mediated communication messages are impersonal and five statements about satisfaction. The strongest of these correlations related to wanting to participate in another discussion in the future (r = -.46), overall satisfaction with the #AgChat discussion (r = -.45), and the discussion as a useful learning experience (r = -.44). A low to medium, positive correlation also existed between the social presence statement related to computer-mediated communication being a pleasant way to communicate with others and six statements about satisfaction. The strongest of these correlations related to overall satisfaction (r = .53), ability to learn through computer-mediated communication (r = .51), and level of learning (r = .50). A low to medium, positive correlation also existed between the social presence statement related to the language used by others to express themselves in computer-mediated communication being easily understood and six statements about satisfaction. The strongest of these correlations related to overall satisfaction with the #AgChat discussion (r = .59 ),overall satisfaction with the moderator’s guidance during the discussion (r = .59), and the discussion assisting in understanding other points of view (r = .54).

For #GardenChat participants, a high to medium, positive correlation existed between the social presence statement that computer-mediated communication messages are social forms of communication and 12 other statements about satisfaction. The strongest of these 12 correlations related to level of learning that took place being at the highest quality (r = .80), the discussion as a useful experience (r = .80), overall satisfaction with what was learned (r = .79), and the discussion assisting in understanding other points of view (r = .76). A medium to high, positive correlation existed between the social presence statement that computer-mediated communication permits the building of trust relationships and eight statements about satisfaction. The strongest of these eight correlations relate to level of learning being at the highest quality (r = .75) and the diversity of topics prompting respondents to participate (r = .70). A medium, negative correlation existed between the statement that it is unlikely for someone else to re-send messages and nine statements about satisfaction. The strongest of these correlations relate to overall satisfaction with the moderator’s guidance (r = -.73) and overall satisfaction with what was learned during the discussion (r = -.72).

Conclusions

Perceptions of Social Presence

Participants of both conversations appeared to sense a social presence and indicated that they are communicating and interacting with other people. Participants of #GardenChat strongly agreed and participants of #AgChat agreed that computer-mediated communication allows relationships to be established based upon sharing and exchanging information. Participants of both conversations agreed that computer-mediated communication allows them to build more caring social relationships with others. Therefore, participants do not appear to feel a sense of deindividuation, the feeling of a loss of individuality caused by the lack of social cues that communication via computers can cause as described by Taylor et al. (2011).

Based on the findings that participants of both conversations disagreed with the statement that it is unlikely someone else might re-send their messages and that participants disagreed with the statement that they were uncomfortable communicating with a person unfamiliar to them, it appeared that participants have a sense that other participants are in close virtual proximity. This conclusion aligns with previous research that says perceptions of social presence can influence psychological distance or felt immediacy during online communication (Gunawardena & McIsaac, 2004). This conclusion also aligns with research in distance education identifying a trend that defines distance in terms of psychological aspects rather than physical proximity (Garrison, 2000; So & Brush, 2008).

Satisfaction

Based on findings in this study that participants of #AgChat and #GardenChat agreed with most statements about satisfaction, such as they would like to participate in another conversation in the future, they were stimulated to do additional readings, they were able to learn, and that they were overall satisfied with the #AgChat and #GardenChat discussions, it appeared that participants maintained attention and developed an attitude about their communication experience. Kupritz and Cowell (2011) report that how a person maintains attention and develops an attitude about communication is influenced by nonverbal cues found in face-to-face communication, such as eye contact, voice inflections, wardrobe, and facial expressions. Therefore, based on findings in this study and the study by Kupritz and Cowell (2011), perhaps there are components within #AgChat and #GardenChat conversations that compensate for the nonverbal cues found in face-to-face communication that influence how much and how a person maintains attention and develops an attitude about communication. This conclusion aligns with previous studies that report social presence can be fostered through text-based variables, such as emoticons, to compensate for lack of nonverbal or face-to-face cues (Gunawardena & Zittle, 1997). This conclusion also supports previous studies that report missing social cues in CMC can be compensated for with response time; humorous or personalized message content; or paralanguage and emoticons, such as happy and sad faces (Picciano, 2002; Richardson & Swan, 2003; Rourke et al., 2001; Taylor et al., 2011).

Relationships between Perceptions of Social Presence and Satisfaction

Based on findings that participants of #AgChat and #GardenChat are more satisfied when their discussions convey feeling and emotion, it appeared that it is important for users to craft their messages with sentiment and express their feelings as best as possible through text. These expressions could include special punctuation, the use of capital letters, emoticons, and descriptive language. This supports Tu’s study which indicated that emoticons and paralanguage made the conversation more comfortable for participants (Tu, 2002).

It appeared that for #GardenChat participants, the more they felt CMC messages were social forms of communication, the more satisfied they were with their level of learning, specifically in the realm of making acquaintances or connecting with people in other parts of the world. Therefore, it is possible that for some people, learning through a social form of communication, such as Twitter, may be more satisfying than other forms of learning. This conclusion aligns with previous studies about online collaborative learning where researchers found that learners placed high importance on feelings of “connectedness and belonging” (Hara, Bonk, & Angeli, 2000; Harasim, 1993; Kitchen & McDougall, 1998; So & Kim, 2005).

Recommendations

Findings and conclusions in this study suggested that social presence, satisfaction and the relationships among them influence satisfaction in computer-mediated communication, specifically in Twitter conversations. It is recommended that when interacting or teaching in a computer-mediated environment such as Twitter, agricultural communicators use responses that support components of social presence. Studies have shown correlations between social presence and student satisfaction (Gunawardena, 1995; Gunawardena & Zittle, 1997; Richardson & Swan, 2003), social presence and learning communities (Rourke et al., 2001; Rovai, 2002), and social presence and perceived learning (Richardson & Swan, 2003).

These recommendations are supported by previous studies that show introductions and salutations build social presence, and thus, trust and participation in online communications (Gunawardena, 1995; Johansen, Vallee, & Spangler, 1988; Tu, 2002). Recommendations are also supported by Vrasidas and McIsaac (1999) who found that more structure in computer-mediated communication led to more interaction. Agricultural communicators may notice more involvement in online conversations if they encourage users to reveal information about themselves, and convey feeling and emotion.

Studies as described above can allow communicators to closely define the similarities and differences between face-to-face and CMC, and better understand how structure levels in Twitter conversations relate to satisfaction levels of participants. To build on this study, additional research should be conducted using self-report mechanisms by participants. It is possible that digital scales allowing users to indicate their level of agreement on a continuum rather than one a one through five Likert scale may yield more accurate responses. Since many tweets in this study were sent by or related to the moderator of each conversation, the field of agricultural communications will benefit from a study that examines the role of moderators in Twitter conversations.

Implications

This study has provided insight on perceptions of social presence that exist during Twitter conversations in agricultural settings. Perhaps agricultural businesses can use these findings to better understand how to connect with existing and potential customers on Twitter, thus leading to the benefit of new or increased sales. For example, based on the finding that over 50 % of respondents in this study were female, it is implied that females may be more interested in and likely to recognize and support social presence dimensions. Therefore, businesses may search for female consumers on Twitter and connect with them through the use of appropriate social presence dimensions.

Insight on participants’ perceptions of social presence and how they relate to perceptions of satisfaction could allow agricultural communicators and other social media users to implement Twitter strategies

that are more satisfying. For example, suppose a Twitter user or organization on Twitter has the goal of educating their audience. Since participants in this study indicated that the more they felt CMC conveyed feeling and emotion, the more they felt their learning experience was of the highest quality, the Twitter user or organization on Twitter would most likely achieve their goal of education by using affective responses. These responses express feeling and emotion. Thus, this study provided useful insights for professionals seeking to understand social networks as a business tool and how these social networks can be adapted to make up for lack of face-to-face social cues.

Study results revealed that agricultural communicators and other Twitter users cannot only feel comfortable with an increased use of text-based communication for their own purposes, but they can guide populations across the globe as they increasingly rely on the Internet to support everyday activities. Though the Internet creates a unique social environment and has somewhat discouraged relational connections, agricultural communicators can and should utilize findings from this study to support virtual relationships that circulate honest agricultural information through chatting, messaging, and blogging. Finally, understanding the similarities and differences in perceived social presence and satisfaction of users in face-to-face communication and CMC supported the National Research Agenda for Agricultural Education and Communication. Specifically, this study provided insight that addresses priority areas related to understanding new technologies (Priority 2) and understanding engaged learning (Priority 4) (Doerfert, 2011).

References

Bartter, A., Fellow, A., Fernandez, N.P., Hidalgo, R., Martin, L., Underdue, S.,Vu, M., & Won, M. (2009). New digital media. Retrieved 2010 from http://vpadmin.fullerton.edu/AssociateVP/OrgDev/UnivLeadAcademy/LeadDevProg/ProjectReports/New_Digital_Media.pdf

Biocca, F., & Harms, C. (2002). Defining and measuring social presence: Contribution to the networked minds theory and measure. Retrieved 2010 from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.84.8350

Driver, M. (2002). Exploring student perceptions of group interaction and class satisfaction in the web-enhanced classroom. The Internet and Higher Education, 5(1), 35-45. doi: 10.1016/S1096-7516(01)00076-8

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, Mail, and Mixed-modeSurveys: The Tailored Design Method (3rd ed.). Hoboken, NJ: John Wiley & Sons, Inc.

Doerfert, D. L. (Ed.). (2011). National research agenda America Association for Agricultural Eductaion’ research priority areas for 2011-2015 Lubbock TX Texas Tec University Department of Agricultural Education and Communications

Garrison, R. (2000). Theoretical challenges for distance education in the 21st century: A shift from structural to transactional issues. International Review of Research in Open and Distance Learning, 1(1), 1–17.

Gunawardena, C. N. (1995). Social presence theory and implications for interaction and collaborative learning in computer conferences. International Journal of Educational Telecommunications, 1(2), 147-166. Charlottesville, VA: AACE.

Gunawardena, C. N., & McIsaac, M. S. (2004). Distance education. In D. Jonassen (Ed.), Handbook of research for educational communications and technology, 2, (pp. 355-395). Bloomington, IN: Association for Educational Communications &Technology.

Gunawardena, C. N., & Zittle, F. J. (1997). Social presence as a predictor of satisfaction within a computer-mediated conferencing environment. American Journal of Distance Education, 11(3), 8-26.

Hara, N., Bonk, C., & Angeli, C. (2000). Content analysis of online discussion in an applied educational psychology. Instructional Science, 28(2), 115–152.

Harasim, L. M. (1993). Networlds: Networks as social space. In L. M. Harasim (Ed.), Global networks: Computers and international communication (pp. 15–34). Cambridge, MA: MIT Press.

Internet World Stats. (2011). Internet usage and population in North America. Retrieved from http://www.internetworldstats.com/stats14.htm

Jansen, B., & Zhang, M. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188. doi:10.1002/asi.21149

Johansen, R., Vallee, J., & Spangler, K. (1988). Teleconferencing: electronic group communication. In R. S. Cathcart & L. A. Samovar (Eds.), Small group communication: A reader (5th ed., pp. 140-154). Menlo Park, CA: Institute for the Future.

Kitchen, D., & McDougall, D. (1998). Collaborative learning on the internet. Journal of Educational Technology Systems, 27(3), 245-258.

Kreijns, K., Kirschner, P., Jochems, W., & van Buuren, H. (2010). Measuring perceived social presence in distributed learning groups. Education and Information Technologies, 1. doi:10.1007/s10639-010-9135-7 ER

Kupritz, V.W., & Cowell, E. (2011). The impact of the physical environment on supervisory communication skills transfer. Journal of Business Communication, 48(1), 148-185. doi: 10.1177/0021943610385656

Lewandowski, J., Rosenberg, B.D., Parks, J.M., & Siegel, J.T. (2011). The effect of informal social support: Face-to-face versus computer-mediated communication. Computers in Human Behavior, 27(5), 1806-1814. doi:10.1016/j.chb.2011.03.008

Lowenthal, P. R. (2009). Social Presence. In P. Rogers, G. Berg, J. Boettcher, C. Howard, L. Justice, and K. Schenk (eds.), Encyclopedia of Distance and Online Learning, 2nd Edition, (pp. 1900-1906). IGI Global, Hershey, PA.

Picciano, A. (2002). Beyond student perceptions: Issues of interaction, presence, and performance in an online course. JALN, 6(1), 21-40.

Prasarnphanich, P., & Wagner, C. (2011). Explaining the sustainability of digital ecosystems based on the Wiki model through critical-mass theory. Industrial Electronics, 58(6) 2065-2072. doi: 10.1109/TIE.2009.2027248

Richardson, J., & Swan, K. (2003). Examining social presence in online courses in relation to students' perceived learning and satisfaction. JALN, 7(1), 68-88.

Rourke, L., Anderson, T., Garrison, R. D., & Archer, W. (2001).Assessing social presence in asynchronous text-based computer conferencing. Journal of Distance Education, 14(2), 50-71. Retrieved from http://www.mendeley.com/research/assessing-social-presence-in-asynchronous-textbased-computer-conferencing-1/

Rovai, A. (2002). Sense of community, perceived cognitive learning, and persistence in asynchronous learning networks. Internet and Higher Education, 5, 319-332.

Russo, T., & Benson, S. (2005). Learning with invisible others: Perceptions of online presence and their relationship to cognitive and affective learning, educational technology & society. Educational Technology and Society, 8(1), 54-62.

Spears, R., & Lea, M. (1994). Panacea or panopticaon? The hidden power in computer mediated communication. Communication Research, 21, 427–459.

Short, J., Williams, E., et al. (1976). The social psychology of telecommunications, London: John Wiley & Sons.

So, H., & Brush, T. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Computers and Education. 51, 318-366.

So, H. J., & Kim, B. (2005). Instructional methods for computer supported collaborative learning (CSCL): A review of case studies. Paper presented at the 10th CSCL Conference, Taipei, Taiwan.

Taylor, M., Jowi, D., Schreier, H., & Bertelsen, D. (2011). Students' perceptions of E-mail interaction during student-professor advising sessions: the pursuit of interpersonal goals. Journal of Computer-Mediated Communication, 16(2), 307-330. doi:10.1111/j.1083-6101.2011.01541.x

Tu, C. (2002). The impacts of text-based CMC on online social presence . Journal of Interactive Online Learning, 1(2). 1-24.

Twitter. (2011). About. An information network, what are hashtags?. Retrieved April 2011 from http://www.twitter.com

Vrasidas, C., & McIsaac, M. S. (1999). Factors influencing interaction in an online course. The American Journal of Distance Education, 13(3), 22-36.

Walther, J. (1992). Interpersonal effects in computer-mediated interaction; a relational perspective. Communication Research 19(1): 52-90.

Zhao, D., & Rosson, M. B. (2009). How and why people Twitter: The role that micro-blogging plays in informal communication at work. Proceedings of the ACM 2009 International Conference on Supporting Group Work (GROUP '09). ACM, New York, NY, USA,. 243-252. doi:10.1145/1531674.1531710

Effectiveness of Using Electronic Self-Assessment Rubrics in a University Core Curriculum Writing-Intensive Course

Holli R. Leggette, Texas A&M UniversityBilly R. McKim, Texas A&M University

Deborah Dunsford, Texas A&M University

Abstract

Writing is a necessary skill required for graduates of colleges of agriculture; however, according to the National Commission on Writing (2003), writing education falls short. Because writing is a process (White, 1991), students need continuous feedback and assessment, which can be done using summative, formative, peer, and self-assessment methods. The purpose of this descriptive study, guided by Bandura’s theory of self-efficacy (1986; 1994; 1997), was to describe the effectiveness of using self-assessment electronic rubrics in a university core curriculum writing-intensive course, delivered at Texas A&M University in the College of Agriculture and Life Sciences. Scores were derived from the writing rubric adapted by Texas A&M University for the Writing Assessment Project. Findings revealed that, as students progressed in the semester, their ability to calculate their grade using an electronic rubric increased. Students’ perceived and self-calculated scores for all four constructs—Idea and Content Development, Style, Organization, and Conventions—increased. Over time students’ perceived and self-calculated scores were within 0.56 points of each other, therefore, concluding that students became better assessors of their own abilities and more confident in their writing abilities. More research needs to be done on how instructors of university core curriculum writing-intensive courses can use self-assessment to enhance the learning process and help students understand writing as a process.

Introduction

Writing competence is a necessary skill in the 21st century. According to the Office of Undergraduate Studies at Texas A&M University (2011), students “will have acquired the knowledge and skills necessary to … communicate effectively, including the ability to … demonstrate effective writing skills” (para. 1). The National Commission on Writing (2003) claimed “American education will never realize its potential as an engine of opportunity and economic growth until a writing revolution puts language and communication in their proper place in the classroom” (p. 3). Making a claim that students have the inability to write is inaccurate; however, students do lack the ability to write well (National Commission on Writing, 2003). Further, the American Association for Agricultural Education’s National Research Agenda called for the preparation of career-ready graduates who could meet the challenges of the 21st century workforce (Doerfert, 2011), which at the very core of this preparation is writing (National Commission on Writing, 2003).

Although writing is considered be an essential skill in the workplace, the 2007 National Writing Report Card showed no significant change since 2002 and only a slight change since 1998 in the United States’ 12th graders who could perform at or above the Proficient level of writing (Salahu-Din, Persky, & Miller, 2008). Only 24 % of the 12th graders could “produce an effective

fully developed response within the time allowed that uses analytical, evaluative, and creative thinking” (Salahu-Din et al., 2008, p. 43). Further, Murphy, Lindner, Kelsey, Wingenbach (2005) found that 7% of graduate students in three agricultural education graduate programs studied had adequate writing ability. Therefore, universities and colleges are admitting students who do not have proficient writing skills (Salahu-Din et al., 2008) while employers’ needs are becoming greater (Peddle, 2000). This leaves an even larger gap between the writing abilities of students entering college and the needs of employers and graduate programs—students exiting college and entering the workplace or pursuing graduate education. Yet, universities and colleges including Marymount University, Tulane University, University of Missouri-Columbia, Texas A&M University, and Colorado State University, continue to require students to enroll in writing intensive courses in effort to improve written communication skills.

Moreover, writing is a process learned through consistent writing, assessment, and feedback (Bok, 2006; Cho & Schunn, 2010; White, 1991); it is more than rules (White, 1991). Although assessment is a component of the teaching and learning process, limited time and resources can restrict instructors’ ability to teach and assess students’ writing abilities (Andrade, 2008; Cho & Schunn, 2010). In its current state, writing can be assessed through summative assessment (Brown, 1999; Trotter, 2006), formative assessment (Andrade, 2008; Brown, 1999; McDonald & Boud, 2003), peer assessment (Brew, 1999; Cho & Schunn, 2010), and self-assessment (Andrade, 2008; Boud, 1991; McDonald & Boud, 2003).

Summative assessment is an end assessment, an impacting outcome (Trotter, 2006), used to make judgments about the product (Brown, 1999). It is an assessment of students’ achievement (Trotter, 2006); whereas, formative assessment is the continuity of assessment throughout the duration of a project that focuses on improvement and not the final product (Brown, 1999; McDonald & Boud, 2003). In which case, a rough draft would be considered a type of formative assessment. Andrade (2008) claimed feedback is an important part of formative assessment and just as valuable when given by the students themselves if the right conditions exist.

Peer assessment is using comments and judgments based on the criteria learned in class to assess their classmates (Brew, 1999). According to Cho and Schunn (2010), peer assessment provides students with more opportunities for writing and revising than an instructor could typically provide. Students can learn from both giving feedback to and receiving feedback from their peers (Cho & Schunn, 2010). Boud (1991) stated self-assessment is “the involvement of students identifying standards and/or criteria to apply to their work and making judgments about the extent to which they have met these criteria and standards” (p. 5). It is more than grading; it is evaluating writing on the basis of knowing what good writing is, which is enhanced through instructor and peer assessment (Andrade, 2008). For students, the ability to apply what they gain as a result of the writing process to other areas of their lives is an important part of the realization of themselves and self-directed learning (Merriam, 2001).

Theoretical Framework

Bandura’s theory of self-efficacy (1986; 1994; 1997) provided conceptual guidance for this study. According to Bandura (1997), self-efficacy is defined as the “beliefs in one’s capabilities to organize and execute the course of action required to produce given attainments” (p. 3).

Additionally, self-efficacy influences a person’s choices, actions, effort, perseverance when faced with obstacles, resilience, thought patterns and emotional reactions, and level of achievement ultimately attained (Bandura, 1986). Writing is about the process through which students develop confidence in their ability and not about the product or the end result (White, 1991). Mastering experiences helps students feel more confident in themselves and their abilities (Bandura, 1994).

Further, self-efficacy is an essential component in the transition from youth to adulthood, development of adults, and achievement of success (Bandura, 1994), which are important outcomes in the collegial years and development steps in the transition from high school to college to adulthood. In each stage of development, humans should begin to take responsibility for their lives, successes, and challenges (Bandura, 1994). As students enter the collegial years, they are transitioning from pedagogy to andragogy where self-directed learning begins (Knowles, Holton, & Swanson, 2005; Merriam, 2001). Whereas, some students will begin the transition early and become self-directed learners while others will not (Merriam, 2001). Additionally, students’ confidence in themselves and their abilities is linked to the instructor and the feedback provided by the instructor (Nicholson, Putwain, Connors, & Hornby-Atkinson, 2011). Instructors’ predetermined beliefs often influence how they connect the classroom to real-life applications in the laboratory (Knobloch, 2008). These beliefs are developed because of personal beliefs about curriculum or content (Borko & Putnam, 1996; Moseley, Reinke, & Bookout, 2002; Pajares, 1992), availability of time and instructional resources, level of preparation regarding content (Thompson & Balschweid, 1999), comfort level with content, (Knobloch & Ball, 2003), perceived value of content (Lawrenz, 1985), past experiences with content area (Calderhead, 1996; Thompson & Balschweid, 1999), teaching environment (Knobloch, 2001), and motivation (Bandura, 1997; Tschannen-Moran, Woolfolk-Hoy, & Hoy, 1998).

Purpose/Objectives

The classroom has transitioned from a teacher-centered environment to a more student-centered (Catalano & Catalano, 1997), self-directed (Merriam, 2001) learning environment. “The emphasis on life-long learning, on developing the skills which students need for independent study, for discriminating good information from bad and for practice as a professional is now a priority” (Brew, 1999, p. 162). Further, being able to separate good information from bad information (Brew, 1999) and to disseminate knowledge through oral and written communication are necessary skills in today’s workforce, government, and society (National Writing Commission, 2003).

The purpose of this study was to describe the effectiveness of using self-assessment electronic rubrics in a university core curriculum writing-intensive course, delivered at Texas A&M University in the College of Agriculture and Life Sciences. Three objectives guided this study:

1. Describe students’ overall perceived grade, self-calculated grade (based on electronic rubric), and assigned grade;

2. Compare students’ self-perceived grade to their self-calculated grade (based on the electronic rubric) for each construct on each assignment; and

3. Describe students’ perceived levels of confidence for each assignment.

Methods and Procedures

This non-experimental, descriptive study sought to measure the effectiveness of using self-assessment rubrics in an upper-level, core curriculum writing-intensive course at Texas A&M University in the College of Agriculture and Life Sciences. Scores derived from the electronic writing rubric served as the dependent variable. Assignments one to six served as the independent variables.

Communicating Agricultural Information to the Public, taught in the Department of Agricultural Leadership, Education and Communications, is a senior-level, university core curriculum course that fulfills the requirement of a writing-intensive course at Texas A&M University. Students (n = 16) enrolled in the course for fall 2011 represented a variety of majors, including those outside of the College of Agriculture and Life Sciences, and had varying levels of writing ability. The writing rubric used was adapted by Texas A&M University for the Writing Assessment Project. It consisted of four categories: Idea and Content Development, Style, Organization, and Conventions. Each category was measured at four levels—developing, sufficient, proficient, and exemplary. We adapted the rubric to an electronic format, so the assessment link could be distributed 48 hours prior to the due date of the assignment.

The rubric was considered content valid because it was extensively vetted and adopted by the Writing Assessment Project at Texas A&M University. Students with similar characteristics who were not selected to participate in the study were included in a pilot test of the rubric. The group of students (n = 7) independently assessed the same assignment using the electronic rubric. They were provided step-by-step instruction on how to complete the rubric and were instructed to ask for clarification on any unclear procedures. Data collected during the pilot test using the electronic rubric were included in the estimates of split-half reliability, resulting in a reliability coefficient of .85.

Because instructor scoring was included in analyses, inter-rater reliability needed to be addressed. According to Ary, Jacobs, and Sorensen (2010), inter-rater reliability can be determined when two or more trained observers independently complete the same test producing a positive and high coefficient (≥ .90). Inter-rater reliability for this study was estimated using rubric scores after two instructors who had previously taught course independently completed the electronic rubric assessing the same assignment, which resulted in Spearman’s r of .92.

Students were asked to complete a self-assessment for six writing-intensive assignments throughout the semester: Journal Assignment, Technical Memorandum, Press Release, Business Letter, Application Letter and Résumé, and Technical Report. For all assignments, students were expected to take on the role of a technical writer. Students were asked to use the electronic rubric to self-assess each assignment prior to submitting it. All students (n = 16) participated in the self-assessment activity; however, not every student participated on all assignments. Prior to using the rubric, the instructor discussed self-assessment and the rubric with the students. Additionally, throughout the semester, the instructor taught the students course material related to each category of the rubric.

The students were asked to estimate their grade on a percentage basis for each category in the rubric and for the overall assignment using the electronic rubric. Each measure—estimated vs. calculated—was independent. As suggested by Andrade (2008), self-assessments were not included in students’ grades because when self-assessments are included in grades the assessment becomes an evaluation, which leads to students’ negative opinions. However, every student completed the assessment at least one time during the semester.

Prior to submitting the final report, students submitted formative assessments throughout the semester: topic and audience, empirical sources, topic outline, and rough draft. Students wrote a rough draft and attended mandatory student/instructor meetings to discuss their report. Additionally, throughout the semester, students were provided opportunities to serve as peer reviewers (Brew, 1999; Cho & Schunn, 2010) for two of their classmates.

Data were analyzed using SPSS® version 20 to determine frequencies, means, standard deviations, and reliability.

Findings

Sixteen students in a upper-level, writing-intensive course completed six assessments (70 observations during the semester) using an electronic rubric that contained four constructs: Idea and Content Development, Style, Organization, and Conventions. Of the 16 students, 14 assessed themselves on assignment one, 12 on assignment two, 13 on assignment three, nine on assignment four, 11 on assignment five, and 11 on assignment six.

Objective One

For objective one, students reported their overall perceived grade, self-calculated grade, and assigned grade on a scale of 1 to 100. With the exception of assignment four (business letter), students’ self-calculated grade increased throughout the semester from assignment one (M = 81.61, SD = 10.77) to assignment six (M = 97.39, SD = 3.14). Their self-perceived grade increased from assignment one (M = 86.93, SD = 6.57) to assignment three (M = 92.08, SD = 4.92). However, self-perceived grades for assignment four to assignment six fluctuated between 88.0% and 94.5%. Students’ grade earned (assigned grade) fluctuated between assignments but remained consistent between 89.0% and 91.5%. Students’ self-calculated grade (M = 89.52, SD = 8.59) most closely aligned with students’ grade earned (M = 89.33, SD = 4.48) on assignment three. Whereas, students’ self-perceived grade (M = 89.11, SD = 8.28) most closely aligned with students’ grade earned (M = 89.31, SD = 6.26) on assignment four (See Figure 1).

Figure 1. Mean percentage scores for students’ overall perceived grade, self-calculated grade (based on electronic rubric), and *grade earned (n = 16).

Objective Two

For objective two, students’ self-perceived grades and self-calculated grades were compared for each construct—Idea and Content Development (0 to 18 scale); Style (0 to 24 scale); Organization (0 to 24 scale); Conventions (0 to 14 scale).

Students’ self-perceived scores for Idea and Content Development slightly increased from assignment one (M = 15.52, SD = .98) to assignment three (M = 16.49, SD = .83) while students’ self-calculated scores increased by more than three points from assignment one (M = 12.43, SD = 3.90) to assignment three (M = 15.62, SD = 2.84). Over time, students’ perceived and self-calculated ability for Idea and Content Development increased from assignment one (perceived, M = 15.52, SD = 0.98; calculated, M = 12.43, SD = 3.90) to assignment six (perceived, M = 16.77, SD = 0.66; calculated, M = 17.09, SD = 1.81).

Students’ self-perceived scores for Style increased from assignment one (M = 20.81, SD = 1.28) to assignment three (M = 21.86, SD = 1.26) while students’ self-calculated scores consistently increased between assignment three (M = 20.85, SD = 3.53) and assignment five (M = 23.73, SD = 0.90). Overall, students’ perceived and calculated ability for Style increased from assignment

one (perceived, M = 20.81, SD = 1.28; calculated, M = 19.36, SD = 3.39) to assignment six (perceived, M = 22.26, SD = 0.69; calculated, M = 22.82, SD = 1.89).

Students’ self-perceived scores for Organization fluctuated between 21.20 and 22.69, while, with the exception of assignment four, students’ self-calculated scores increased between assignment one (M = 21.36, SD = 2.24) and assignment six (M = 24.00, SD = 0.00). Overall, students’ perceived and calculated scores for Style were more in line with assignment one (perceived, M = 21.26, SD = 1.47; calculated, M = 21.36, SD = 2.24) than assignment six (perceived, M = 22.38, SD = 0.71; calculated, M = 24.00, SD = 0.00).

Students’ self-perceived scores for Conventions remained fairly steady with less than a one point increase at any point during the semester while students’ self-calculated scores steadily increased during the semester from assignment one (M = 12.14, SD = 2.85) to assignment six (M = 14.00, SD = 0.00). During the semester, students’ perceived and calculated ability for Conventions remained within one point of each other on all six assignments (See Figure 2).

Objective Three

For objective three, students reported their perceived level of confidence on a scale of 1 to 100. With the exception of assignment six, students’ perceived level of confidence increased for Idea and Content Development, Style, Organization, and Conventions on each assignment. Overall, students appeared to become progressively more confident in their writing ability with the exception of assignment six (See Figure 3). Additionally, the correlation between self-perceived and self-calculated scores increased from .42 to .72.

Figure 2. Mean scores for students’ self-perceived and self-calculated grade (based on the electronic rubric) for Idea and Content Development, Style, Organization, and Conventions of each assignment.

Figure 3. Mean percentage scores of students’ perceived levels of confidence for Idea and Content Development, Style, Organization, and Conventions of each assignment and their overall confidence on the assignment.

Conclusions/Recommendations/Implications

Writing instructors and assessors need to realize that not every student fits the same mold and some students respond better to different types of assessment. If instructors continue to label writing as correct and incorrect, students will likely miss the principle and most important part of writing—the process (White, 1991). With that said, using self-assessment in writing education, students can assess their own level of performance and achievement (Bandura, 1986; 1994; 1997) and improve their writing abilities at their own pace.

As students progressed through the semester, their ability to calculate their grade using an electronic rubric increased. Therefore, students became more accurate assessors of their own writing during the course of the semester. Students’ self-calculated grade most closely aligned with the instructor’s grade on assignment three, Press Release, and their self-perceived grade aligned with the instructor’s grade on assignment four, Business Letter. Although students’ calculated grades increased throughout the semester, students’ earned grade remained consistent between 89.0% and 91.5%. Therefore, one can conclude that, although students’ earned grades did not increase drastically, their writing improved because writing is about the process and not the product as noted by White (1991).

Students’ perceived and self-calculated scores for all four constructs—Idea and Content Development, Style, Organization, and Conventions—increased throughout the semester. Therefore, students became more comfortable in assessing their writing using an electronic rubric, assigning themselves a grade, and improving their quality of writing. According to Andrade (2008), using a rubric helps students understand what elements are needed to produce quality writing and improve their writing based on the feedback received through the assessment.

Further, students’ perceived and self-calculated scores for Conventions steadily increased over time, whereas confidence scores showed fluctuation between assignments. As White (1991) noted, students are accustomed to a set of rules and think once they learn the rules their writing will improve. Further then, if students believe writing is a set rules, it is obvious students would be feel more confident assessing their Conventions abilities because over time they would learn grammar, punctuation, and spelling rules. Between assignments three and four the scores dropped for each construct. The researchers concluded students’ lack of ability and confidence in their ability to write business letters were the reasons for lower scores. Based on this study, students became more confident in their writing, with the exception of assignment six, and more aware of their strengths and weaknesses based on assignment scores. Yet, students perceived and self-calculated scores were within 0.56 points of each other. Therefore, concluding that students became better assessors of their own abilities and more competent in their writing abilities, which was arguably in line with Bandura (1986; 1994; 1997).More research needs to be done on how instructors of university core curriculum writing-intensive courses can use self-assessment to enhance the learning process and help students understand writing. “If students produce it, they can assess it; and if they can assess it, they can improve it” (Andrade, 2008, p. 63). Further, research needs to be conducted to determine if there are differences between using self-assessment in university core curriculum writing-intensive courses and major specific writing-intensive courses. An experimental design study could be

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done using two sections of the same course taught by the same instructor to determine if differences exist between atypical self-assessment (Andrade, 2008; Boud, 1991; McDonald & Boud, 2003) rubrics and typical summative assessment (Brown, 1999; Trotter, 2006).

Educators should continue to use self-assessment in their writing intensive courses because self-assessment enables students to become critics of their work and life-long, effective, and responsible learners (McDonald & Boud, 2003). As students piece together the elements of writing and move through the writing process, they begin to understand, assess, and evaluate good writing, as suggested by Andrade in 2008. Self-assessments could help increase students’ ability to take responsibility of their education by providing a self-delivered learning activity. The electronic self-assessment used in this study disassembled the assessment component and provided students an opportunity to ensure they addressed each component of the assignment. Self-assessment would shift the classroom from a teacher-centered environment to a student-centered (Catalano & Catalano, 1997) environment where students focus on the writing process instead of the end result (White, 1991).

It is important to note that this is the first stage of developing a strong writing assessment program that could be used nationwide across multiple disciplines in colleges of agriculture. The findings of this study cannot be generalized to other populations because this study describes one course at one university. However, the findings can be used as a basis to compare to future writing assessment studies in agricultural leadership, education, and communications. Similar, yet, more in depth randomized experimental design studies can be conducted comparing the findings of this study. Colleges of agriculture could implement an assessment program specific to each field of study that would move writing assessment towards a more objective form of assessment. Before higher education can encourage higher-level skills in application, analysis, synthesis, and evaluation, a new level of assessment needs to be developed (Brown, 1999).

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References

Andrade, H. (2008). Self-assessment through rubrics. Educational Leadership, 65(4), 60–63. Retrieved from http://web.ebscohost.com

Ary, D., Jacobs, L. C., & Sorenson, C. (2010). Introduction to research in education (8th ed.). Belmont, CA: Thomson - Wadsworth.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71-81). New York, NY: Academic Press.

Bandura, A. (1997). Self-efficacy: The exercise of control, New York, NY: W.H. FreemanBok, D. C. (2006). Our underachieving colleges: A candid look at how much students learn and

why they should be learning more. [Google books]. Retrieved from http://books.google.com

Borko, H., & Putnam, R. H. (1996). Learning to teach. In handbook of educational psychology, Eds. D.C. Berlinger and R.C. Calfee, (pp. 673–708). New York, NY: Simon, Schuster, & MacMillan.

Boud, D. (1991). Implementing student self-assessment. HERDSA Green Guide (2nd ed.). Sydney, Australia: Higher Education Research and Development Society of Australasia.

Brew, A. (1999). Towards autonomous assessment: Using self-assessment and peer assessment. In S. Brown, & A. Glasner (Eds.), Assessment Matters in Higher Education (pp. 159–171). Philadelphia, PA: The Society for Resesarch into Higher Education & Open University Press.

Brown, S. (1999). Institutional strategies for assessment. In S. Brown, & A. Glasner (Eds.), Assessment Matters in Higher Education (pp. 3–13). Philadelphia, PA: The Society for Resesarch into Higher Education & Open University Press.

Calderhead, J. (1996). Teachers: Beliefs and knowledge. In handbook of educational psychology, Eds. D.C. Berlinger and R.C. Calfee, 673-708. New York, NY: Simon, Schuster & MacMillian.

Catalano, G. D., & Catalano, K. C. (1997). Transformation: From teacher-centerd to student-centered engineering education. Paper presented at the Frontiers in Education 1997 27th Annual Conference, Pittsburgh, PA.

Cho, A., & Schunn, C. (2010). Developing writing skills through studnets giving instructional explanations. In M. K. Stein & L. Kucan (Eds.), Instructional Explanations in the Disciplines (pp. 207–221). New York, NY: Springer.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Knobloch, N. A. (2001). The influence of peer teaching and early field experience on teaching efficacy beliefs of preservice educators in agriculture. Paper presented at the 28th National Agricultural Education Research Conference, New Orleans, LA, 119–131.

Knobloch, N. A. & Ball, A. (2003). An examination of elementary teachers’ and agricultural literacy coordinators’ beliefs related to the integration of agriculture. Retrieved from http://www.agriculturaleducation.org/LinkPages/AgLiteracyK8.asp

87

Knobloch, N. A. (2008). Factors of teacher beliefs related to integrating agriculture into elementary school classrooms. Agriculture and Human Values, 25(4), 529–539. doi: 10.1007/s10460-008-9135-z

Knowles, M. S., Holton III, E. F., & Swanson, R. A. (2005). The adult learner: The definitive classic in adult education and human resource development (6th ed.). San Diego, CA: Elsevier.

Lawrenz, F. (1985). Impact on a five week energy education program on teacher beliefs and attitudes. School Science and Mathematics, 85(1), 27–36. doi: 10.1007/BF02277231

McDonald, B. & Boud, D. (2003). The impact of self-assessment on achievement: The effects of self-assessment training on performance in external examinations. Assessment in Eduction: Principles, Policy, & Practice, 10(2), 209–220. doi: 10.1080/0969594032000121289

Merriam, S. B. (2001). Andragogy and self-directed learning: Pillars of adult learning theory. New Directions for Continuting and Adult Education, 89, 3–13. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/ace.3/pdf

Moseley, C., Reinke, K., & Bookout, V. (2002). The effect of teaching outdoor environmental education on preservice teachers’ attitudes toward self-efficacy and outcome expectancy. Journal of Environmental Education, 34(1), 9–15. doi: 10.1080/00958960209603476

Murphy, T. H., Lindner, J. R., Kelsey, K. D., & Wingenbach, G. J. (2005). Authenticated writing competencies of agricultural education graduate students: A comparison of distance and on-campus students. Journal of Agricultural Education, 46(4), 13–22. doi: 10.5032/jae.2005.04013

The National Commission on Writing for America’s Schools and Colleges. (2003). The neglected “R”: The need for a writing revolution. Retrieved from http://www.writingcommission.org /prod_downloads/writingcom/neglectedr.pdf

Nicholson, L., Putwain, D., Connors, L., & Hornby-Atkinson, P. (2011). The key to successful achievement as an undergraduate student: Confidence and realistic expectations. Studies in Higher Education, 1–13. doi: 10.1080/03075079.2011.585710

Office of Undergraduate Studies at Texas A&M University. (2011, December 19). University learning outcomes for undergraduates [web page]. Retrieved from http://us.tamu.edu/programs/high-impact-practices-in-undergraduate-education/university-learning-outcomes-for-undergraduates/

Pajares, M. F. (1992). Teachers’ beliefs and educational research: Cleaning up a messy construct. Review of Research in Education, 62(3), 307–332. doi: 10.3102/00346543062003307

Peddle, M. T. (2000). Frustration at the factory: Employer perceptions of workforce deficiencies and training needs. The Journal of Regional Analysis & Policy, 30(1), 23-40. Retrieved from http://www.jrap-journal.org/pastvolumes/2000/v30/30-1-2.pdf

Salshu-Din, D., Persky, H., & Miller, J. (2008). The Nation’s Report Card: Writing 2007 (NCES 2008–468). National Center for Education Statistics, Institute of Educations Sciences, U.S. Department of Education, Washington, D.C.

Thompson, G. W. & Balschweid, M. (1999). Attitudes of Oregon agricultural science and technology teachers toward integrating science. Journal of Agricultural Education, 40(3), 21–29. doi: 10.5032/jae.1999.03021

Trotter, E. (2006). Student perceptions of continuous assessment. Assessment & Evaluation in Higher Education, 31(5), 505–521. doi: 10.1080/02602930600679506

88

Tschannen-Moran, M., Woolfolk-Hoy, A., Hoy, W.K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68(2), 202–248. doi: 10.3102/00346543068002202

White, E. (1991). Assessing higher order thinking and communication skills in college graduates through writing. Paper presented at the National Assessment Conference, Washington, DC.

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Selected Factors Influencing Missouri School-Based Agricultural Educators to Teach Agricultural Mechanics Curriculum

P. Ryan Saucier, Texas State University – San Marcos

Abstract

The National Research Agenda for Agricultural Education and Communications suggests that teachers promote “highly effective educational programs [that] will meet the academic career and developmental needs of diverse learners in all settings and at all levels” (Doerfert, 2011, p. 24). The purpose of this census was to determine the factors influencing Missouri school-based agricultural educators to instruct the curriculum found within the course Agricultural Construction 1 and/or 2. Data was collected via Hosted Survey™ from all teachers who instructed this course during the 2009-2010 academic school year (N = 257). A total of 203 (79%) teachers responded. The majority of the respondents chose to teach the curriculum areas found within the course; however Project Construction curriculum was the most commonly taught curriculum area. The factor, Personal Importance, was found to be the most influential factor impacting school-based agriculture educators’ decision to teach all of the agricultural mechanics curriculum areas. Administration Importance was the least influential factor persuading teachers to instruct the agricultural mechanics curriculum areas. Researchers recommend future research to better understand the phenomenon of curriculum instruction choice by teachers and implement professional development to increase teacher skill and pedagogy competence.

Introduction and Literature Review

Instructional practices, which are implemented in the classroom and laboratory, are somewhat based on how teachers choose to teach the curriculum content with the resources allocated to them and within the schools’ learning environment (Knobloch, 2008). The predetermined beliefs of teachers often influence how they connect academic content in the classroom to real-life applications in the laboratory or community (Knobloch, 2008). Frequently, these beliefs are developed in part to personal beliefs about the curriculum or content (Borko & Putnam, 1996; Moseley, Reinke, & Bookout, 2002; Pajares, 1992); availability of time, availability instructional resources, level of preparation regarding the content (Thompson & Balschweid, 1999), comfort level with the content, (Knobloch & Ball, 2003), perceived value of the content (Lawrenz, 1985), past experiences with the content area (Calderhead, 1996; Thompson & Balschweid, 1999), teaching environment (Knoblock, 2001) and motivation (Bandura, 1997; Tschannen – Moran, Woolfolk-Hoy, & Hoy, 1998). The development and performance of teachers is also influenced by the interaction of these personal and environmental factors and the situations in which they teach (Knobloch, 2001). The National Research Agenda for Agricultural Education and Communications suggests that teachers promote “highly effective educational programs [that] will meet the academic career and developmental needs of diverse learners in all settings and at all levels” (Doerfert, 2011, p. 24). As teacher educators, if we can understand the factors that influence teachers’ decisions to instruct various aspects of the curriculum, can we then help shape a more fruitful environment for student academic mastery and teacher performance?

Theoretical Framework

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The Theory of Planned Behavior was used as the theoretical base for this study (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). This theory was developed to understand a persons’ behaviors over which people have incomplete volitional control. Furthermore, this theory suggests that investigators should not only look at beliefs, attitudes, and intentions of individuals, but also their behavior (Ajzen, 1988). A central factor in the Theory of Planned Behavior (Ajzen, 1991) is that an individuals’ intention to perform a given behavior, i.e. instruct agricultural mechanics curriculum, are assumed, and can be used to capture the motivational factors that influence a behavior. Motivational factors are considered to be indications of how hard people are willing to try and how much of an effort they are planning to exert in order to perform the behavior (Ajzen, 1991). The theory further indentifies non-motivational factors that can be used to determine a person’s performance at a given behavior. These non-motivational factors can include the availability of requisite opportunities and resources that include: time, money, personal skill level, and the cooperation of others (Ajzen, 1991). Collectively, motivational and non-motivational factors represent a person’s actual control over a behavior. Furthermore, the theory states that if a person has the required opportunities and resources, and intends to perform the behavior, the person should succeed in their behavior. Additionally, the component, subjective norm, included in Ajzen's theory (1991) represents the perceived social pressures on the individual. These subjective norms refer to peoples’ beliefs about other people's attitudes towards the behavior and how important their opinions are. In this study, the perceived behavioral control component refers to the extent to which teachers believe themselves to be capable of teaching curriculum which is assumed to reflect past experience as well as anticipated impediments and obstacles (Ajzen, 1988). The inclusion of this component in Ajzen's theory recognizes that if teachers are not confident about their ability to perform curriculum skills, then they may feel unable to teach the curriculum in the classroom or laboratory. By understanding the factors that influence a teachers’ decision to instruct curriculum, professional development opportunities can be developed to aid teachers in skill and pedagogy development; thus, aid in student academic achievement by providing quality skill-based experiential learning opportunities in the classroom and the laboratory.

Purpose and Research Questions

The purpose of this study was to describe the factors that influence Missouri school-based agriculture teachers’ choice to teach specific curriculum found within the agricultural education course entitled Agricultural Construction 1 and/or 2.

1. What are the personal, professional, and program characteristics of school-based agricultural educators in Missouri who teach the agricultural education course Agricultural Construction 1 and/or 2?

2. Which of the selected curriculum components of the agricultural education course Agricultural Construction 1 and/or 2 do Missouri school-based agricultural educators choose to teach?

3. What factors influence Missouri school-based agricultural educators’ decisions to teach selected curriculum components of the agricultural education course Agricultural Construction 1 and/or 2?

Methods

Population

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The target population consisted of all school-based agriculture teachers (N = 257) in Missouri who taught the agricultural education course Agricultural Construction 1 and/or 2 during the 2009-2010 academic school year. The frame for this study was obtained from the 2009-2010 Missouri Agricultural Education Directory, published by the Missouri Department of Elementary and Secondary Education.

Instrumentation

Data were collected through a researcher-designed, web-based questionnaire. The two section questionnaire was developed by the researcher and distributed using Hosted Survey™. Section I was composed of questions related to the instruction of six skill-related curriculum areas (e.g. Arc Welding, Project Construction, Oxy-Gas and Other Cutting/Welding Processes, Woodworking, Metals, and Finishing) included in the Agricultural Construction 1 and/or 2 curriculum. This section also contained questions relating to the factors that influence, or do not influence, a teacher to teach the selected components of the curriculum. A five-point, summated rating scale was offered for respondents to provide information about factors that influence their decision to teach, or not to teach, a curriculum component. The response scale for each factor was: 0 = no influence, 1 = little influence, 2 = some influence, 3 = moderate influence, and 4 = great influence. Section II of the instrument consisted of ten questions designed to collect information on personal, professional and program information of the respondents and the school-based agricultural education program in which they teach.

To ensure the validity of the instrument, a panel of experts (N = 7) was used to review the instrument for face and content validity. Recommendations from the panel were then utilized to improve the instrument design. To estimate the reliability of the instrument, a pilot study was conducted with a similar population of 23 school-based agriculture teachers in the neighboring Commonwealth of Kentucky. Of the 23 teachers contacted, 22 (96%) completed all items in Sections I and II. The resulting Cronbach’s alpha coefficients ranged from .73 to .91. Garson (2008) and Nunnelly (1978) identified .70 as the level at which a scale may be considered internally consistent, thus the constructs found within the instrument were deemed reliable.

Procedures

The Dillman (2007) Tailored Designed Method for Internet Surveys was utilized to guide the data collection process of this study. For this study, subjects were contacted up to five potential times through electronic mail from the researcher. In the end, 203 (79%) Missouri agricultural educators provided usable responses for this study.

Data Analysis

Data were analyzed using the Statistical Package for the Social Sciences® (SPSS) 17.0 for Windows and Microsoft Office Excel® 2007. Data analysis methods were selected as a result of determining the scales of measurement for the variables measured.

Results

Research Question One

Of the 203 teachers who participated in this study, 83.30% were male (n = 169). The mean age for teachers was 37.26 years (SD = 9.83). The mean number of university semester credit hours earned in agricultural mechanics coursework was 10.71 (SD = 11.35). On average, Missouri school-based agriculture teachers who instruct Agricultural Construction 1 and/ or Agricultural

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Construction 2 had 12.66 years of teaching experience (SD = 9.06). The subjects supervised students’ agricultural mechanics SAEs for an average of 4.90 hours per week (SD = 6.65). Additionally, the mean enrollment of students in an agricultural education program, in which the respondents taught, was approximately 94 students (M = 93.71; SD = 65.38). Furthermore, more than 90% (n = 185; 91.10%) of the respondents reported that they completed a traditional teacher certification. The remainder of the subjects reported they completed some form of an alternative teacher certification (n = 18; 8.90%). No respondents indicated they completed any form of an emergency teacher certification (n = 0; 0.00%).

Research Question Two

Research question two sought to identify the curriculum areas that school-based Missouri agricultural educators teach within the course Agricultural Construction 1 and/or 2. The majority of respondents (n = 172; 84.70%) reported they teach arc welding curriculum. Nearly 9 of every 10 teachers (n = 180; 88.70%) also indicated they teach project construction curriculum. Oxy-gas and other cutting/welding processes was the third curriculum area that was taught the most. Respondents (n = 171; 84.20%) indicated that they teach this curriculum in the course Agricultural Construction 1 and/or 2. Almost two-thirds of respondents (n = 124; 61.10%) reported that they taught the curriculum area, woodworking. Metals curriculum was also reported as being taught by two-thirds of the respondents (n = 140; 69.00%). Finally, 143 (70.40%) teachers indicated to the researcher that they teach finishing curriculum. (see Table 1)

Table 1

Curriculum Areas Taught by Missouri School-Based Agriculture Teachers Who Instruct Agricultural Construction 1 and/or 2 (n = 203)

Curriculum AreasYes No

f % f %

Arc Welding 172 84.70 31 15.30

Project Construction 180 88.70 23 11.30

Oxy-Gas and Other Cutting/Welding Processes 171 84.20 32 15.80

Woodworking 124 61.10 79 38.90

Metals 140 69.00 63 31.00

Finishing 143 70.40 60 29.60

Research Question Three

Missouri school-based agriculture teachers who instruct Agricultural Construction 1 and/or 2, indicated that Personal Importance was the greatest factor that influenced their decision to teach arc welding curriculum to students (Mean = 3.50; SD = 0.63). Conversely, the factor Administration Importance was the least important factor influencing their decision to teach arc welding curriculum to students (Mean = 2.41SD = 1.10). A summary of the remaining factors that influenced teachers to instruct arc welding are displayed in Table 2.

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Table 2

Factors Influencing Missouri School-Based Agriculture Teachers to Instruct Arc Welding Curriculum (n = 172)

Factor

Levels of Influence

0 1 2 3 4

f % f % f % f % f % M SD

Personal Importance 0 0.00 1 0.50 9 4.40 65 32.00 97 47.80 3.50 0.63

Personal Ability to Teach 2 1.00 7 3.40 21 10.30 60 29.60 82 40.40 3.24 0.90

Personal Interest in Teaching 2 1.00 10 4.90 20 9.90 55 27.10 85 41.90 3.23 0.95

Experience in Teaching 1 0.50 9 4.40 25 12.30 65 32.00 72 35.50 3.15 0.90

Equipment Available to Teach 0 0.00 13 6.40 24 11.80 63 31.10 72 35.50 3.13 0.92

Student Importance 2 1.00 9 4.40 27 13.30 64 31.50 70 34.50 3.11 0.93

Facilities Available to Teach 0 0.00 15 7.40 31 15.30 61 30.00 65 32.00 3.02 0.95

Community Importance 0 0.00 10 4.90 50 24.60 69 34.00 43 21.20 2.84 0.87

Budget Available to Teach 1 0.50 19 9.40 49 24.10 50 24.60 53 26.10 2.78 1.02

Administration Importance 8 3.90 29 14.30 49 24.10 57 28.10 29 14.30 2.41 1.10

Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Within the curriculum area of project construction, teachers indicated that the factor Personal Importance impacted their decision to teach this curriculum area to their students the greatest (Mean = 3.40; SD = 0.73). Moreover, Administration Importance influenced teachers the least to teach project construction curriculum to students enrolled in Agricultural Construction 1 and/or 2 (Mean = 2.57; SD = 1.09). Additional data concerning this curriculum area are displayed in Table 3.

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Table 3

Factors Influencing Missouri School-Based Agriculture Teachers to Instruct Project Construction Curriculum (n = 180)

Factor

Levels of Influence

0 1 2 3 4

f % f % f % f % f % M SD

Personal Importance 1 0.50 0 0.00 20 9.90 64 31.50 96 47.30 3.40 0.73

Personal Interest in Teaching 3 1.50 4 2.00 29 14.30 60 29.60 85 41.90 3.22 0.91

Experience in Teaching 2 1.00 6 3.00 28 13.80 72 35.50 73 36.00 3.15 0.88

Equipment Available to Teach 1 0.50 7 3.40 33 16.30 63 31.00 77 37.90 3.15 0.89

Personal Ability to Teach 2 1.00 5 2.50 31 15.30 73 36.00 70 34.50 3.12 0.87

Facilities Available to Teach 1 0.50 10 4.90 35 17.20 57 28.10 78 38.40 3.11 0.94

Student Importance 1 0.50 8 3.90 34 16.70 74 36.50 64 31.50 3.06 0.77

Community Importance 3 1.50 12 5.90 36 17.70 68 33.50 62 30.50 2.96 0.98

Budget Available to Teach 4 2.00 11 5.40 47 23.20 58 28.60 61 30.00 2.89 1.02

Administration Importance 5 2.50 27 13.30 52 25.60 54 26.60 43 21.20 2.57 1.09

Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Oxy-gas and other cutting/welding processes was another curriculum area found with the agricultural education course entitled Agricultural Construction 1 and/or 2. Personal Importance was the factor that had the greatest influence on a teachers’ decision to teach oxy-gas and other cutting curriculum to students (Mean = 3.16 SD = 0.81). However, the factor Administration Importance played the least important role in influencing a teachers’ decision to teach oxy-gas and other cutting/welding processes curriculum to

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students (Mean = 2.24; SD = 1.04). The remaining factors that influenced teachers to instruct oxy-gas and other cutting/welding processes are displayed in Table 4.

Table 4

Factors Influencing Missouri School-Based Agriculture Teachers to Instruct Oxy-Gas Cutting/Welding Curriculum (n = 171)

Factor

Levels of Influence

0 1 2 3 4

f % f % f % f % f % M SD

Personal Importance 1 0.50 2 1.00 32 15.80 70 34.50 66 32.50 3.61 0.81

Personal Interest in Teaching 2 1.00 6 3.00 39 19.20 73 36.00 51 25.10 2.97 0.88

Experience in Teaching 4 2.00 6 3.00 38 18.70 68 33.50 55 27.10 2.96 0.99

Equipment Available to Teach 2 1.00 10 4.90 41 20.20 62 30.50 56 27.60 2.94 0.95

Personal Ability to Teach 5 2.50 7 3.40 38 18.70 66 32.50 55 27.10 2.93 0.99

Facilities Available to Teach 4 2.00 7 3.40 46 22.70 62 30.50 52 25.60 2.88 0.97

Student Importance 3 1.50 12 5.90 59 29.10 48 23.60 49 24.10 2.75 1.00

Community Importance 1 0.50 17 8.40 51 25.10 63 31.00 39 19.20 2.71 0.95

Budget Available to Teach 1 0.50 28 13.80 53 26.10 56 27.60 33 16.30 2.54 1.00

Administration Importance 8 3.90 34 16.70 56 27.60 55 27.10 18 8.90 2.24 1.04

Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Teachers also indicated that the factor Personal Importance had the greatest impact on their decision to teach the curriculum area of woodworking to their students (Mean = 2.98; SD = 0.87). However, Administration Importance influenced teachers the least to teach

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woodworking curriculum to students enrolled in Agricultural Construction 1 and/or 2 (Mean = 2.26; SD = 0.95). Additional data concerning this curriculum area are displayed in Table 5.

Table 5

Factors Influencing Missouri School-Based Agriculture Teachers to Instruct Woodworking Curriculum (n = 124)

Factor

Levels of Influence

0 1 2 3 4

f % f % f % f % f % M SD

Personal Importance 0 0.00 2 1.00 42 20.70 36 17.70 44 21.70 2.98 0.87

Personal Ability to Teach 2 1.00 9 4.40 36 17.70 38 18.70 39 19.20 2.83 1.01

Equipment Available to Teach 0 0.00 6 3.00 46 22.70 36 17.70 36 17.70 2.82 0.91

Facilities Available to Teach 1 0.50 7 3.40 49 24.10 31 15.30 36 17.70 2.76 0.97

Personal Interest in Teaching 3 1.50 11 5.40 37 18.20 36 17.70 37 18.20 2.75 1.06

Experience in Teaching 3 1.50 8 3.90 40 19.70 42 20.70 31 15.30 2.73 0.99

Student Importance 0 0.00 13 6.40 45 22.20 41 20.20 25 12.30 2.63 0.92

Budget Available to Teach 0 0.00 14 6.40 53 26.10 30 14.80 27 13.30 2.56 0.96

Community Importance 3 1.50 17 8.40 45 22.20 43 21.20 16 7.90 2.42 0.96

Administration Importance 4 2.00 18 8.90 57 28.10 32 15.80 13 6.40 2.26 0.95

Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

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Respondents who instruct Agricultural Construction 1 and/or 2, indicated that Personal Importance was the greatest factor that influenced their decision to teach metals curriculum to students (Mean = 2.72; SD = 0.91). However, the factor Administration Importance played the least important role in influencing their decision to teach metals curriculum to students (Mean = 1.95; SD = 1.06). A summary of the remaining factors that influenced teachers to instruct metals are displayed in Table 6.

Table 6

Factors Influencing Missouri School-Based Agriculture Teachers to Instruct Metals Curriculum (n = 140)

Factor

Levels of Influence

0 1 2 3 4

f % f % f % f % f % M SD

Personal Importance 1 0.50 8 3.90 52 25.60 47 23.20 32 15.80 2.72 0.91

Personal Interest in Teaching 4 2.00 20 9.90 38 18.70 49 24.10 29 14.30 2.56 1.06

Equipment Available to Teach 5 2.50 16 7.90 48 23.60 40 19.70 31 15.30 2.54 1.07

Facilities Available to Teach 5 2.50 16 7.90 49 24.10 40 19.70 30 14.80 2.53 1.06

Experience in Teaching 3 1.50 19 9.40 44 21.70 53 26.10 21 10.30 2.50 0.98

Personal Ability to Teach 5 2.50 16 7.90 51 25.10 44 21.70 24 11.80 2.47 1.02

Budget Available to Teach 8 3.90 19 9.40 49 24.10 37 18.20 27 13.30 2.40 1.12

Student Importance 6 3.00 22 10.80 60 29.60 36 17.70 16 7.90 2.24 1.00

Community Importance 7 3.40 27 13.30 57 28.10 36 17.70 13 6.40 2.15 1.00

Administration Importance 13 6.40 32 15.80 55 27.10 29 14.30 11 5.40 1.95 1.06

Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

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Within the curriculum area of finishing, teachers indicated that the factor Personal Importance impacted teachers’ decision to instruct this curriculum area to their students the greatest (Mean = 2.86; SD = 0.95). Moreover, Administration Importance influenced teachers the least to teach finishing curriculum to students enrolled in Agricultural Construction 1 and/or 2 (Mean = 2.12; SD = 1.09). Additional data concerning this curriculum area are displayed in Table 7.

Table 7

Factors Influencing Missouri School-Based Agriculture Teachers to Instruct Finishing Curriculum (n = 143)

Factor

Levels of Influence

0 1 2 3 4

f % f % f % f % f % M SD

Personal Importance 2 1.00 7 3.40 42 20.70 49 24.10 43 21.20 2.86 0.95

Personal Ability to Teach 2 1.00 12 5.90 50 24.60 48 23.60 31 15.30 2.66 0.96

Personal Interest in Teaching 4 2.00 11 5.40 49 24.10 48 23.60 31 15.30 2.63 0.99

Experience in Teaching 2 1.00 18 8.90 45 22.20 50 24.60 28 13.80 2.59 0.99

Facilities Available to Teach 7 3.40 20 9.90 43 21.20 43 21.20 30 14.80 2.48 1.12

Equipment Available to Teach 7 3.40 22 10.80 41 20.20 44 21.70 29 14.30 2.46 1.12

Student Importance 5 2.50 18 8.90 54 26.60 44 21.70 22 10.80 2.42 1.01

Budget Available to Teach 8 3.90 25 12.30 45 22.20 35 17.20 30 14.80 2.38 1.16

Community Importance 8 3.90 19 9.40 52 25.60 44 21.70 20 9.90 2.34 1.06

Administration Importance 12 5.90 26 12.80 53 26.10 37 18.20 15 7.40 2.12 1.09

Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Conclusions, Implications, & Recommendations

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Research Question One

Research question one sought to describe the personal, professional, and program characteristics of school-based agricultural educators in Missouri who instruct the agricultural education course Agricultural Construction 1 and/or 2. Teachers who instruct this course, are mostly male, 37 years old, and completed a traditional teaching certification program. These teachers have about 13 years of teaching experience, earned almost 11 university semester credit hours in agricultural mechanics coursework, and teach about 94 students per semester. Furthermore, as FFA advisors, these teachers spend about 5 hours per week supervising agricultural mechanics related SAE projects.

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Research Question Two

Research question two sought to identify the selected curriculum components of the agricultural education course Agricultural Construction 1 and/or 2 that school-based agricultural educators in Missouri instructed. The curriculum components that are included in this school-based agricultural education course include: arc welding, project construction, oxy-gas and other cutting/welding, woodworking, metals, and finishing. The majority of teachers indicated that they instruct all curriculum areas included in this course. However, teachers instruct the curriculum areas related to hot metal work, specifically arc welding, project construction, and oxy-gas and other cutting/welding processes, more than the curriculum areas related to woodworking, metals, and finishing.

Numerous questions are raised from these results. Why do teachers choose to teach certain curriculum areas over others? What factors influence these teachers’ decisions concerning their choice to instruct curriculum? Why is curriculum related to hot metal skills instructed more than curriculum related to woodworking, metals (cold metal skills), and finishing in Agricultural Construction 1 and/or 2 courses? Are other extraneous factors not found in this study impacting teachers’ decisions to instruct agricultural mechanics curriculum? These questions and others are grounds for future research to better understand teachers’ curriculum instruction decisions. Researchers recommend future research to fully understand this phenomenon.

Research Question Three

Research question three sought to determine the level of influence selected factors have upon a teacher’s choice to instruct various curriculum components included in the course Agricultural Construction 1 and /or 2. Teachers indicated that the factor of Personal Importance was the most influential factor that persuaded them to instruct each curriculum area. Furthermore, Administration Importance was the least influential factor that persuaded these teachers to instruct each curriculum area. The remaining factors were distributed sporadically between the most influential factor and least influential factor, and thus, no measurable pattern was established.

The Theory of Planned Behavior (Ajzen, 1991) played a major role in the development of the theoretical foundation for this study. The results of this study can be applied to this theory and conceptually worked in reverse order. If researchers can understand teachers’ behavior (the decision to teach or not to teach the curriculum), future research can be conducted to determine their intention to teach. According to Ajzen (1991), a teachers intention to teach is based upon four influential factors: attitude towards the behavior, or teaching agricultural mechanics; the subjective norm, or the social pressures that the administration, the community, and the students themselves, place upon the teacher to instruct the curriculum; motivational factors, such as amount of personal effort, level of intention to teach, and non-motivational factors such as budget, personal skill level, equipment, facilities; and perceived behavioral control, or the extent to which teachers believe themselves to be capable of teaching curriculum which is assumed to reflect past experience as well as anticipated impediments and obstacles. As agricultural educators, if we can unlock these factors and ensure that new teachers have positive experiences, can we then determine if teachers will choose to teach agricultural mechanics curriculum? These questions and others are grounds for future research in this subject area.

Several implications can be extrapolated from these results. Why does the factor Personal Importance play such a significant role in determining the curriculum that Missouri teachers

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instruct in Agricultural Construction 1 and/or 2? How is agriculture teacher’s personal importance toward the instruction of agricultural mechanics curriculum developed? At what point during an agriculture teacher’s career is their level of importance toward the instruction of agricultural mechanics curriculum developed? What factors attribute to the development of a teachers’ level of importance toward the instruction of agricultural mechanics curriculum? Can a teacher’s level of importance toward the instruction of agricultural mechanics curriculum be altered or improved? If so, what methods or opportunities have the potential to influence change in a teacher’s level of importance toward the instruction of agricultural mechanics curriculum?

Another notable result of this study concerns the factor Administration Importance. For every curriculum area found within the course Agricultural Construction 1 and/or 2, teachers indicated that Administration Importance was the least important factor that influenced their decision to teach the various curriculum areas. Why does the factor Administration Importance play such an insignificant role in determining the curriculum that school-based agricultural educators in Missouri teach? Is this influential factor limited to the course Agricultural Construction 1 and/or 2 or does it impact all courses taught under the agricultural education umbrella? Do teachers not care about the opinion of administrators when it pertains to the instruction of curriculum at their school? Or do administrators not have knowledge of the curriculum found within the course Agricultural Construction 1 and/or 2, and therefore, don’t make an influence? These questions and others are grounds for future research regarding curriculum choice in agricultural mechanics programs.

In the realm of education, the responsibility for teacher development is often thought to rest on the shoulders of teacher educators and the teacher development process. However, a question must be posited, does previous experience, or their lack of, aid in the development of an individual’s motivation or personal importance to teach curriculum? To better understand this phenomenon of teacher development and a teachers’ intent to teach curriculum, it is recommended that future research concerning the study of pre-service teacher curriculum experiences be conducted. Ajzen (1991) found that a teacher’s intention to teach is based upon four influential factors: attitude towards the behavior; the subjective norm; motivational factors; and perceived behavioral control. These factors should be studied to determine if they impact pre-service teachers’ ability to teach agricultural mechanics curriculum and to better understand and assess the professional development needs of future teachers.

Additionally, professional development for teachers, who lack skill and pedagogy knowledge and experience to teach agricultural mechanics curriculum, should be designed, implemented, and assessed. Such programs should be offered with frequency and variety and should be delivered in formats and at times that will have the greatest impact upon the largest number of teachers. This goal could be accomplished by providing teachers with workshops offered during the winter and summer breaks, agricultural mechanics courses offered for continuing education or university graduate courses for credit. Even online, self-directed courses might be an option. Winter and summer workshops focusing on agricultural mechanics should be offered at regional locations throughout the state of Missouri and could be located at university or public school facilities.

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References

Ajzen, I. (1988). Attitude, personality, and behavior. Chicago, IL: Dorsey Press.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 179-211. DOI: 10.1016/0749-5978(91)90020-T

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, N.J.: Prentice Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control, New York: W.H. Freeman.

Borko, H., & Putnam, R. H. (1996). Learning to teach. In Handbook of educational psychology, eds. D.C. Berlinger and R.C. Calfee, 673-708. New York: Simon, Schuster, & MacMillan.

Calderhead, J. (1996). Teachers: beliefs and knowledge. In Handbook of educational psychology, eds. D.C. Berlinger and R.C. Calfee, 673-708. New York: Simon, Schuster & MacMillian.

Dillman, D. A. (2007). Mail and internet surveys: The tailored design method (2nd ed.). John Wiley & Sons: Hoboken, NJ.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, M.A.: Addison – Wesley.

Garson, G. D. (2008, March). Scales and Standards of Measures. Retrieved from http://faculty.chass.ncsu.edu/garson/PA765/standard.htm#internal.

Knobloch, N. A. (2001). The influence of peer teaching and early field experience on teaching efficacy beliefs of preservice educators in agriculture. Paper presented at the 28th National Agricultural Education Research Conference, 119-131.

Knobloch, N.A. (2008). Factors of teacher beliefs related to integrating agriculture into elementary school classrooms. Agriculture and Human Values, 25(4), 529-539. DOI: 10.1007/s10460-008-9135-z

Knobloch, N.A. & Ball, A. (2003). An examination of elementary teachers’ and agricultural literacy coordinators’ beliefs related to the integration of agriculture. Retrieved from http://www.agriculturaleducation.org/ LinkPages/AgLiteracyK8.asp.

103

Lawrenz, F. (1985). Impact on a five week energy education program on teacher beliefs and attitudes. School Science and Mathematics, 85(1), 27-36.

Missouri Department of Elementary and Secondary Education (2009). 2009-2010 Missouri Agricultural Education Directory. Jefferson City, MO.

Moseley, C., Reinke, K., & Bookout, V. (2002). The effect of teaching outdoor environmental education on preservice teachers’ attitudes toward self-efficacy and outcome expectancy. Journal of Environmental Education, 34(1), 9-15. DOI: 10.1080/00958960209603476

Nunnelly, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw Hill.

Pajares, M. F. (1992). Teachers’ beliefs and educational research: Cleaning up a messy construct. Review of Research in Education, 62(3), 307-332.

Thompson, G. & Balschweid, M. (1999). Attitudes of Oregon agricultural science and technology teachers toward integrating science. Journal of Agricultural Education, 40(3), 21-29. DOI: 10.5032/jae.1999.03021

Tschannen-Moran, M., Woolfolk-Hoy, A., Hoy, W.K. (1998). Teacher efficacy: It’s meaning and measure. Review of Educational Research, 68(2), 202-248.

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Assessing Performance and Consequence Competence in a Technology-Based Professional Development for Agricultural Science Teachers: An Evaluation of the Lincoln Electric

Welding Technology Workshop

P. Ryan Saucier, Texas State University – San MarcosBilly R. McKim, Texas A&M University

Joe E. Muller & Doug M. Kingman, Sam Houston State University

AbstractProfessional development education for teachers is essential to improving teacher retention, program continuity, and the preparation of fully qualified and highly motivated agricultural educators at all career stages (Osborne, 2007). Furthermore, it is necessary to link industry experts with teachers to improve their competence in teaching curriculum, establishing mechanisms of consultation and support, and to introduce new ideas (Little, 1993). This study sought to evaluate the effectiveness of the Lincoln Electric welding technology workshop, determine if teachers’ Performance and Consequence Competence changed, and determine the professional development needs of the participants. The Borich (1980) needs assessment model was used to determine mean weighted discrepancy scores. Findings indicated that the workshop was effective; teachers Performance Competence and Consequence Competence changed positively after completion of the workshop; and participants were mostly in need of future professional development in the welding technology skill area of Gas Tungsten Arc Welding.

Introduction & Literature ReviewAt the inception of school-based agricultural education programs in the early 1900s, the focus of programs was primarily centered on production agriculture with the ultimate goal of preparing students to return to the farm or pursue a career in production agriculture (Leake, 1915). Originally educational programs utilized three main instructional techniques: classroom lecture, recitation, and manual labor (Stimson, 1920). Over time, instructional and curriculum challenges facing agricultural educators have changed (Layfield & Dobbins, 2002; Saucier, Tummons, Terry, & Schumacher, 2010; Washburn & Dyer, 2006) along with the need for in-service education programming (Saucier & Terry, 2011). Washburn and Dyer (2006) found that agricultural education programs have evolved from production oriented training at their creation and have evolved to the consumption based curriculum and courses offered today. Consequently, modern teachers are expected to provide a positive learning environment for students and ultimately prepare them for productive lives in a fast-paced world (Layfield & Dobbins, 2002). Washburn and Dyer (2006) found teachers are encouraged to integrate science, mathematics, engineering, and technology (STEM) concepts and curriculum into many of the agricultural education courses that they teach. Moreover, the evolution of agricultural education programs and the addition of core subject content skills have required many agriculture teachers to seek professional development opportunities to meet the demands of the changing emphasis of their programs (Washburn & Dyer, 2006).

The fundamental intention of professional development is to provide educators the essential knowledge, skills, and technical information required for them to effectively carry out their professional duties and meet the demands of a changing educational environment (Barrick, Ladewig, & Hedges, 1983; Birkenholz & Harbstreit, 1987; Nesbitt & Mundt, 1993; Washburn,

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King, Garton, & Harbstreit, 2001). By tradition, professional development education for agriculture teachers has been a duty of collegiate agricultural education programs and state agricultural education supervisory staff (Barrick et al., 1983) who plan and implement continuing education opportunities. In many cases, the planning and implementation of these educational opportunities has generally been developed with little input from educators in the field (Washburn et al., 2001). A review of literature yielded three key methods used by teacher educators and state supervisory staff to determine the in-service needs of agriculture educators: research (Layfield & Dobbins, 2000; Washburn et al., 2001), personal experiences (Barrick et al., 1983), and informal inquiry with current agricultural educators (Barrick et al., 1983; Roberts & Dyer, 2004).

A critical factor in developing successful teachers is correctly identifying professional development needs that are in the greatest demand (Layfield & Dobbins, 2002). By recognizing the problems faced by agricultural educators, teacher education faculty and state agricultural education supervisory staff can improve professional development programs to address the needs of teachers (Mundt & Connors, 1999). Providers of continuing education programs have experienced difficulties, at times, in identifying appropriate topics to include in professional development programs (Washburn et al., 2001). According to Birkenholz and Harbstreit (1987), providers of professional development education should monitor the needs of agriculture teachers over time and provide educational programs, based upon their current needs. Garton and Chung (1995) concluded that “the in-service needs of agriculture teachers should be assessed and prioritized on a continual basis” (p. 78).

Successful professional development must be sustained overtime and directly related to everyday teaching (Kent, 2004). To provide professional development to teachers, it is necessary to link industry experts with teachers to improve their competence in teaching curriculum, establishing mechanisms of consultation and support, and to introduce new ideas (Little, 1993). Numerous studies have found that the teachers’ expertise and teaching ability is crucial for student achievement (Darling-Hammond, 1997; 2000; Rivers & Sanders, 1996).

Professional development education for teachers is essential to improving teacher retention, program continuity, and the preparation of fully qualified and highly motivated agricultural educators at all career stages (Osborne, 2007). The National Research Agenda for Agricultural Education and Communications, Research Priority 3, suggests that teachers “must be prepared for discovery science, teaching and learning, science, technology, engineering, and mathematics (STEM) integration” (Doerfert, 2011, p. 19). Additionally, Doerfert (2011) also suggested that “teachers stay up-to-date with the ever-changing advancements in education and in the agriculture industry” (p. 25). If more industry sponsored professional development opportunities were offered and attended by teachers, would the aforementioned changes take place in U.S. schools?

Conceptual FrameworkBorich’s (1980) needs assessment model served as the conceptual framework for this study and served as a guide for the data collection efforts. Since Borich’s model was proposed in 1980, numerous studies in the broader agricultural education literature have used the model to varying extents. When considering a teacher’s self-perceived efficacy (competence) in relation to a single item (competency), teachers may vary; thus, Borich proposed three perspectives of

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competency—knowledge, performance, and consequence (see Table 1)—to “…permit a more refined evaluation of the training program” (1980, p. 40).

Table 1Borich (1980, p. 40) Needs Assessment Model CompetenciesCompetency Construct DefinitionKnowledge Competence Ability to accurately recall, paraphrase, or summarize the

procedural mechanics of the behavior on a paper and pencil testPerformance Competence Ability to accurately execute the behavior in a real or simulated

environment in the presence of an observerConsequence Competence Ability to elicit learning from pupils by using the behavior in the

classroom

Theoretical FrameworkArguably, educators are in need of constant professional development to meet the needs of their positions; however, little research has been conducted to determine if industry supported professional development education are effective. The Lincoln Electric Company has been providing welding technology workshops in Texas since 2003 for agricultural educators. However, an assessment of the effectiveness of this workshop is not apparent in the literature and, therefore, should be assessed to determine if teachers actually improve skill acquisition and improve their teaching ability.

To better understand the professional development needs of agricultural educators in the technical curriculum area of welding technology, two theories were used to form the theoretical base for this non-experimental, quantitative study: Knowles’ theory of andragogy (Knowles, Holton III, & Swanson, 2005) and Bandura’s theory of self-efficacy (Bandura, 1997.) Knowles’ theory of andragogy (Knowles et al., 2005) proposes that the adult learner must know why they must know a concept, which will likely motivate them to engage in the learning process. Consequently, adults learn experientially, learn as problem solvers, and learn best when the topic is of immediate value to them. Knowles further determined that adults should be engaged in the development of their own learning experiences.

Bandura (1997) defined self-efficacy as the “beliefs in one’s capabilities to organize and execute the course of action required to produce given attainments” (p. 3). Furthermore, self-efficacy influences a person’s choices, actions, the amount of effort they give, how long they persevere when faced with obstacles, their resilience, their thought patterns and emotional reactions, and the level of achievement they ultimately attain (Bandura, 1986). Although Bandura’s theory of self-efficacy is important in understanding a person’s actions, etc., an education-directed concept known as teacher self-efficacy has been determined to also be an important concept of understanding teacher motivation in the classroom and laboratory (Knobloch & Whittington, 2002; Tschannen-Moran, Woolfolk-Hoy, & Hoy, 1998).

It is critical to understand teachers’ professional development education needs in the technical area of welding technology for future professional development opportunities to be planned, delivered, and evaluated by teacher educators and state agricultural education leaders. Due to the limited amount of research regarding the professional development needs of Texas agricultural

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educators in the curriculum area of welding technology, and the continual need for research regarding professional development of these specialized teachers on a national level (Osborne, 2007), a current assessment of these needs is warranted and should be conducted.

Purpose and Research ObjectivesThe purpose of this study was to determine the self-perceived efficacy levels of teachers (n = 26) concerning 16 welding technology competencies instructed in the Lincoln Electric welding technology workshop, determine if change occurred in participants’ self-perceived efficacy levels to perform and teach the welding technology competencies, and identify the professional development needs of the workshop participants based upon the welding technology competencies instructed.

1. Describe participants’ perceived levels of efficacy for the constructs associated with the Lincoln Electric welding technology professional development.

2. Describe the change in participants’ perceived ability to perform and teach the Lincoln Electric welding technology workshop competencies, based on pre- and post-test scores.

3. Describe the professional development needs related to participants’ perceptions of performing and teaching the Lincoln Electric welding technology workshop competencies.

ProceduresAs part of a larger study, the research design of this quantitative study was descriptive in nature. School-based agricultural science teachers who attended a Lincoln Electric workshop during the fall of 2010 (n = 15) and fall of 2011(n = 11) served as the population for this study. The overarching construct of this study was to measure perceptions of teachers’ ability to perform (Performance Competence) and teach (Consequence Competence) 16 Lincoln Electric welding technology workshop competencies as defined by Borich’s (1980) needs assessment model. A review of the literature did not reveal an obvious data collection instrument. Hence, a two-section instrument to address the research objectives of this study was developed by the researchers. The first section of the instrument consisted of a triple-matrix containing 16 statements representing the workshop competencies addressed in the week-long professional development welding technology workshop delivered by the Lincoln Electric Company (LEPD). The 5-point summated rating scale associated with each of the three matrices allowed workshop participants to respond to each statement three times: When responding to the first matrix, participants were asked to indicate their level of importance for each competency. For the second and third matrices, participants were asked to rate their ability to perform each competency and their ability to teach students how to perform each competency. The second section sought to identify individuals’ demographic characteristics (e.g., years of teaching experience, university semester credit hours completed in agricultural mechanics, hours spent supervising students in the agricultural mechanics laboratory, etc.).

The design and format of the paper data collection instrument were guided by the suggestions of Dillman, Smyth, and Christian (2009). The paper questionnaire was created and distributed to a panel of experts to assess face validity. The panel of nine experts consisted of faculty members from university level agricultural teacher education and agricultural systems management programs. Post hoc Cronbach’s alpha coefficients were calculated for the scales—importance,

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ability to perform, ability to teach—reliability indices were generated using the participants in the LEPD and yielded coefficients of .94 and .97 (n = 24) respectively. Pre-LEPD data were collected by inviting LEPD participants (N = 26) to complete the paper questionnaire, proctored immediately before the LEPD activities began; 24 participants completed the pre-LEPD questionnaire. Similarly, LEPD participants were invited to complete the paper questionnaire, proctored at the conclusion of LEPD activities; 21 participants completed the post-LEPD questionnaire. A summarized description of LEPD participants are provided in Table 2.

Table 2Selected Demographics of Agricultural Science Teachers who Participated in LEPDCharacteristic M SD Min Max

Years of teaching experience (n = 24) 8.75 8.68 0 29

University semester credit hours earned in agricultural mechanics coursework (n = 21) 11.38 11.57 0 36

Hours spent weekly supervising student work in the agricultural mechanics laboratory (n = 22) 17.50 13.10 0 40

Data AnalysisFor research question one, frequency and percentage were reported for each competency by Pre-Professional Development Intervention and Post-Professional Development Intervention. Research question two sought to identify the change in competence levels based on the Borich (1980) needs assessment model competencies; importance of competencies, ability to perform competencies, and ability to teach competencies. For research question three, mean weighted discrepancy scores (MWDS) were calculated using an Excel-based MWDS calculator (McKim & Saucier, 2011) for pre-LEPD and post-LEPD. Corresponding changes in MWDSs were also reported. Larger MWDSs represent greater in-service needs; whereas, smaller scores represent lesser in-service needs (Borich, 1980).

FindingsResearch objective one sought to describe participants’ perceived levels of efficacy for the constructs associated with the Lincoln Electric professional development. Pre- and post-LEPD frequencies and percentages for the 16 statements representing the welding technology competencies were reported by construct: perceived level of importance for each competency (see Table 3), self-perceived ability to perform each competency (see Table 4), and self-perceived ability to teach students the competency (see Table 5).

Research objective two sought to describe the change in participants’ perceived importance of each Lincoln Electric welding technology competency and ability to perform and teach Lincoln Electric welding technology competencies, based on pre- and post-test construct scores. Mean and standard deviations were reported by construct. Based on the summated construct means, LEPD participants perceived the LEPD competencies to be of average importance at the beginning of the LEPD. However, at the completion of the LEPD, participants’ perceptions of LEPD competencies, increased to above average importance (∆M = +.42). Additionally, participants’ perceived an increased ability to perform (∆M = +.76) and teach (∆M = +.92) the competencies (see Table 6).

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Table 3Lincoln Electric Workshop Participants Self-Perceived Level of Importance of Teaching Welding Technology Competencies to Students (n = 25)

Competency

Pre-Professional Development Intervention

Post-Professional Development Intervention

1 2 3 4 5 1 2 3 4 5f % f % f % f % f % f % f % f % f % f %

Shielded Metal Arc Welding (SMAW)

Mild steel 0 0.0 0 0.0 7 28.0 7 28.0 11 44.0 0 0.0 0 0.0 1 4.3 8 34.8 14 60.9Stainless steel 0 0.0 3 12.0 14 56.0 5 20.0 3 12.0 0 0.0 2 8.7 8 34.8 8 34.8 5 21.7Hardfacing mild

steel 0 0.0 2 8.0 12 48.0 10 40.0 1 4.0 0 0.0 2 8.7 4 17.4 11 47.8 6 26.1

Gas Metal Arc Welding (GMAW)

Mild steel 0 0.0 0 0.0 4 16.0 11 44.0 10 40.0 0 0.0 0 0.0 1 4.5 8 36.4 13 59.1Stainless steel 0 0.0 3 12.0 12 48.0 7 28.0 3 12.0 0 0.0 2 8.7 6 26.1 8 34.8 7 30.4Aluminum 0 0.0 1 4.0 11 44.0 10 40.0 3 12.0 0 0.0 2 9.1 3 13.6 9 40.9 8 36.4

Flux Cored Arc Welding (FCAW)

Mild steel 0 0.0 3 12.0 7 28.0 9 36.0 6 24.0 0 0.0 2 9.1 3 13.6 9 40.9 8 36.4Gas Tungsten Arc Welding (GTAW)

Mild steel 0 0.0 1 4.0 8 32.0 10 40.0 6 24.0 0 0.0 1 4.5 3 13.6 8 36.4 10 45.5Stainless steel 0 0.0 2 8.0 9 36.0 9 36.0 5 20.0 0 0.0 3 13.6 4 18.2 7 31.8 8 36.4Aluminum 0 0.0 1 4.0 10 40.0 10 40.0 4 16.0 0 0.0 2 9.1 3 13.6 8 36.4 9 40.9

Oxygen/Fuel Cutting (OFC)Mild steel 0 0.0 0 0.0 6 24.0 10 40.0 9 36.0 1 4.5 0 0.0 1 4.5 5 22.7 15 68.2

Oxygen/Fuel Welding (OFW)Mild steel 0 0.0 5 20.0 12 48.0 5 20.0 3 12.0 1 4.5 3 13.6 4 18.2 7 31.8 7 31.8

Oxygen/Fuel Brazing (OFB)Dissimilar metals 0 0.0 5 20.0 15 60.0 5 20.0 0 0.0 1 4.5 1 4.5 7 31.8 8 36.4 5 22.7

Plasma Arc Cutting (PAC)Mild steel 0 0.0 0 0.0 5 20.0 13 52.0 7 28.0 0 0.0 0 0.0 2 8.7 8 34.8 13 56.5Stainless steel 0 0.0 1 4.0 10 40.0 9 36.0 5 20.0 0 0.0 1 4.3 5 21.7 6 26.1 11 47.8Aluminum 0 0.0 1 4.0 9 36.0 12 48.0 3 12.0 0 0.0 1 4.3 5 21.7 7 30.4 10 43.5

Note. Scale: 1.00 – 1.50 = No Importance, 1.51 – 2.50 = Below Average Importance, 2.51 – 3.50 = Average Importance, 3.51 – 4.50 = Above Average Importance, 4.51 to 5.00 = Utmost Importance.

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Table 4Lincoln Electric Workshop Participants Self-Perceived Ability Level to Perform Welding Technology Competencies to Students (n = 25)

Competency

Pre-Professional Development Intervention

Post-Professional Development Intervention

1 2 3 4 5 1 2 3 4 5f % f % f % f % f % f % f % f % f % f %

Shielded Metal Arc Welding (SMAW)Mild steel 0 0.0 2 8.3 11 45.8 10 41.7 1 4.2 0 0.0 0 0.0 4 17.4 14 60.9 5 21.7Stainless steel 5 20.8 9 37.5 6 25.0 3 12.5 1 4.2 0 0.0 5 22.7 8 36.4 8 36.4 1 4.5Hardfacing mild steel 3 12.5 6 25.0 10 41.7 4 16.7 1 4.2 1 4.5 1 4.5 8 36.4 10 45.5 2 9.1

Gas Metal Arc Welding (GMAW)Mild steel 1 4.2 2 8.3 7 29.2 12 50.0 2 8.3 0 0.0 0 0.0 6 28.6 11 52.4 4 19.0Stainless steel 5 20.8 8 33.3 8 33.3 2 8.3 1 4.2 1 4.5 4 18.2 6 27.3 10 45.5 1 4.5Aluminum 6 25.0 6 25.0 9 37.5 2 8.3 1 4.2 0 0.0 4 18.2 9 40.9 6 27.3 3 13.6

Flux Cored Arc Welding (FCAW)Mild steel 4 16.7 4 16.7 10 41.7 5 20.8 1 4.2 0 0.0 3 13.6 6 27.3 12 54.5 1 4.5

Gas Tungsten Arc Welding (GTAW)Mild steel 5 20.8 10 41.7 7 29.2 1 4.2 1 4.2 0 0.0 6 27.3 4 18.2 10 45.5 2 9.1Stainless steel 6 25.0 12 50.0 3 12.5 2 8.3 1 4.2 1 4.5 7 31.8 5 22.7 7 31.8 2 9.1Aluminum 6 25.0 11 45.8 5 20.8 1 4.2 1 4.2 0 0.0 7 31.8 6 27.3 7 31.8 2 9.1

Oxygen/Fuel Cutting (OFC)Mild steel 1 4.2 1 4.2 7 29.2 9 37.5 6 25.0 1 4.5 0 0.0 2 9.1 11 50.0 8 36.4

Oxygen/Fuel Welding (OFW)Mild steel 3 12.5 7 29.2 5 20.8 7 29.2 2 8.3 3 13.6 2 9.1 4 18.2 9 40.9 4 18.2

Oxygen/Fuel Brazing (OFB)Dissimilar metals 5 20.8 7 29.2 6 25.0 6 25.0 0 0.0 3 13.6 1 4.5 7 31.8 10 45.5 1 4.5

Plasma Arc Cutting (PAC)Mild steel 1 4.2 4 16.7 7 29.2 9 37.5 3 12.5 0 0.0 1 4.5 8 36.4 7 31.8 6 27.3Stainless steel 3 12.5 6 25.0 7 29.2 6 25.0 2 8.3 1 4.5 4 18.2 5 22.7 9 40.9 3 13.6Aluminum 4 16.7 6 25.0 7 29.2 4 16.7 3 12.5 1 4.5 4 18.2 6 27.3 9 40.9 2 9.1

Note. Scale: 1.00 – 1.50 = No Ability, 1.51 – 2.50 = Below Average Ability, 2.51 – 3.50 = Average Ability, 3.51 – 4.50 = Above Average Ability, 4.51 to 5.00 = Exceptional Ability.

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Table 5Lincoln Electric Workshop Participants Self-Perceived Ability Level to Teach Welding Technology Competencies to Students (n = 25)

Competency Pre-Professional Development Intervention

Post-Professional Development Intervention

1 2 3 4 5 1 2 3 4 5f % f % f % f % f % f % f % f % f % f %

Shielded Metal Arc Welding (SMAW)Mild steel 0 0.0 2 8.3 15 62.5 6 25.0 1 4.2 0 0.0 0 0.0 4 18.2 13 59.1 5 22.7Stainless steel 5 20.8 10 41.7 7 29.2 1 4.2 1 4.2 0 0.0 2 9.1 12 54.5 6 27.3 2 9.1Hardfacing mild steel 3 12.5 6 25.0 11 45.8 3 12.5 1 4.2 1 4.5 0 0.0 8 36.4 9 40.9 4 18.2

Gas Metal Arc Welding (GMAW)Mild steel 1 4.2 2 8.3 12 50.0 7 29.2 2 8.3 0 0.0 0 0.0 6 28.6 9 42.9 6 28.6Stainless steel 5 20.8 7 29.2 9 37.5 2 8.3 1 4.2 1 4.8 2 9.5 11 52.4 5 23.8 2 9.5Aluminum 6 25.0 8 33.3 8 33.3 1 4.2 1 4.2 0 0.0 2 9.5 10 47.6 6 28.6 3 14.3

Flux Cored Arc Welding (FCAW)Mild steel 4 16.7 5 20.8 10 41.7 4 16.7 1 4.2 0 0.0 2 9.5 9 42.9 9 42.9 1 4.8

Gas Tungsten Arc Welding (GTAW)Mild steel 6 25.0 11 45.8 5 20.8 1 4.2 1 4.2 0 0.0 4 19.0 7 33.3 8 38.1 2 9.5Stainless steel 8 33.3 9 37.5 5 20.8 1 4.2 1 4.2 0 0.0 4 20.0 8 40.0 6 30.0 2 10.0Aluminum 8 33.3 8 33.3 6 25.0 1 4.2 1 4.2 0 0.0 5 23.8 7 33.3 7 33.3 2 9.5

Oxygen/Fuel Cutting (OFC)Mild steel 1 4.2 1 4.2 8 33.3 10 41.7 4 16.7 1 4.8 0 0.0 2 9.5 10 47.6 8 38.1

Oxygen/Fuel Welding (OFW)Mild steel 3 12.5 7 29.2 5 20.8 8 33.3 1 4.2 3 14.3 2 9.5 3 14.3 9 42.9 4 19.0

Oxygen/Fuel Brazing (OFB)Dissimilar metals 6 25.0 6 25.0 7 29.2 4 16.7 1 4.2 3 14.3 1 4.8 6 28.6 10 47.6 1 4.8

Plasma Arc Cutting (PAC)Mild steel 1 4.2 5 20.8 10 41.7 4 16.7 4 16.7 0 0.0 1 4.8 6 28.6 8 38.1 6 28.6Stainless steel 4 16.7 5 20.8 9 37.5 3 12.5 3 12.5 1 4.8 2 9.5 6 28.6 8 38.1 4 19.0Aluminum 3 12.5 7 29.2 8 33.3 3 12.5 3 12.5 1 4.8 2 9.5 6 28.6 9 42.9 3 14.3

Note. Scale: 1.00 – 1.50 = No Ability, 1.51 – 2.50 = Below Average Ability, 2.51 – 3.50 = Average Ability, 3.51 – 4.50 = Above Average Ability, 4.51 to 5.00 = Exceptional Ability.

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Table 6Change in Self-Perceived Efficacy Levels of Lincoln Electric Workshop Participants Regarding the Importance to Teach, Ability to Perform, and Ability to Teach Welding Technology Competencies to Students (n = 25)

Pre PostConstruct n M SD n M SD ΔMImportancea of Competency ( = .935) 25 3.73 .597 23 4.15 .710 +.42Abilityb to Perform Competency ( = .964) 24 2.79 .858 23 3.55 .767 +.76Abilityb to Teach Competency ( = .967) 24 2.70 .862 22 3.62 .745 +.92Note. aImportance Scale 1.00 to 1.50 = No Importance, 1.51 to 2.50 = Below Average Importance, 2.51 to 3.50 = Average Importance, 3.51 to 4.50 = Above Average Importance, 4.51 to 5.00 = Utmost Importance. bAbility Scale 1.00 to 1.50 = No Ability, 1.51 to 2.50 = Below Average Ability, 2.51 to 3.50 = Average Ability, 3.51 to 4.50 = Above Average Ability, 4.51 to 5.00 = Exceptional Ability

Based on these findings, there was an increase from average ability to above average ability, in both Performance Competence and Consequence Competence, as measured pre- and post-LEPD. Therefore, the LEPD was effective in increasing teachers’ ability to perform and teach the competencies addressed in the LEPD training.

Research objective three sought to prioritize the professional development needs related to performing and teaching Lincoln Electric welding technology competencies. The Borich (1980) needs assessment model was used to determine teachers’ ability to perform (Performance Competence) and teach (Consequence Competence) Lincoln Electric welding technology competencies. By assessing teachers’ needs for professional development separately, Performance Competence and Consequence Competence, it was obvious that for many LEPD competencies, teachers’ need for each type of competence differed in rank order. To determine the extent to which the LEPD addressed the professional development needs of participants on a per-competency basis, MWDSs were calculated pre- and post-LEPD.

The greatest changes in Performance Competence MWDSs were related to Gas Tungsten Arc Welding (GTAW; ΔMWDS +3.61 to +2.33)—arguably, the most technical competency found in school-based agricultural mechanics laboratories—followed by Shielded Metal Arc Welding (SMAW; ΔMWDS = +1.98) of stainless steel and Flux Cored Arc Welding (FCAW; ΔMWDS = +1.23) of mild steel. Therefore, the LEPD was effective in increasing teachers’ Performance Competence.

Although the LEPD increased teachers’ Performance Competence in several areas, planning additional professional development is needed, essentially guiding the next step. Hence, post-LEPD Performance Competence needs in the competencies related to GTAW require additional professional development. Performance Competence in the areas of hard-facing mild steel using the SMAW process (post-LEPD MWDS = 0.96), Oxygen/Fuel Welding (OFW) of mild steel (post-LEPD MWDS = 0.91), SMAW of stainless steel (post-LEPD MWDS = 0.90) were the least in need of professional development (see Table 7).

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Table 7Lincoln Electric Welding Technology Workshop Participants’ Performance Competence Needs, Based on MWDS

CompetencyPre-LEPD(n = 24)

Post-LEPD(n = 23) Δ

MWDSRank MWDS Rank MWDSGas Tungsten Arc Welding (GTAW) of stainless

steel2 5.50 11 1.89 +3.61

Gas Tungsten Arc Welding (GTAW) of aluminum 2 5.50 2 3.00 +2.50Gas Tungsten Arc Welding (GTAW) of mild steel 1 5.91 1 3.58 +2.33Gas Metal Arc Welding (GMAW) of aluminum 4 4.18 10 2.08 +2.10Shielded Metal Arc Welding (SMAW) of stainless

steel12 2.88 16 0.90 +1.98

Flux Cored Arc Welding (FCAW) of mild steel 5 3.40 8 2.17 +1.23Hardfacing mild steel using the Shielded Metal Arc

Welding (SMAW) process13 2.11 14 0.96 +1.15

Gas Metal Arc Welding (GMAW) of stainless steel 6 3.23 9 2.13 +1.10Plasma Arc Cutting (PAC) of aluminum 9 3.06 7 2.23 +0.83Plasma Arc Cutting (PAC) of stainless steel 10 2.94 6 2.26 +0.68Shielded Metal Arc Welding (SMAW) of mild steel 8 3.13 4 2.53 +0.60Gas Metal Arc Welding (GMAW) of mild steel 7 3.19 3 2.62 +0.57Plasma Arc Cutting (PAC) of mild steel 11 2.89 5 2.46 +0.43Oxygen/Fuel Cutting (OFC) of mild steel 14 1.55 13 1.34 +0.21Oxygen/Fuel Welding (OFW) of mild steel 16 0.94 15 0.91 +0.03Oxygen/Fuel Brazing (OFB) of dissimilar metals 15 1.23 12 1.43 -0.20

Similar to Performance Competence, the greatest changes in Consequence Competence MWDSs were related to GTAW (ΔMWDS +2.83 to +2.00). The next greatest changes in Consequence Competence MWDSs were Gas Metal Arc Welding (GMAW) of aluminum (ΔMWDS = +1.86), followed by SMAW of stainless steel (ΔMWDS = +1.81).

To guide the planning of additional professional development addressing Consequence Competence, post-LEPD Consequence Competence needs in the competencies related to GTAW and Plasma Arc Cutting (PAC) require additional professional development to increase teachers’ ability to teach related competencies. Consequence Competence in the areas of hard-facing mild steel using the SMAW process (post-LEPD MWDS = 1.40) and OFW of mild steel (post-LEPD MWDS = 1.13) were the least in need of professional development (see Table 8).

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Table 8Lincoln Electric Workshop Participants’ Consequence Competence Needs, Based on MWDS

CompetencyPre-LEPD(n = 24)

Post-LEPD(n = 23) Δ

MWDSRank MWDS Rank MWDSGas Tungsten Arc Welding (GTAW) of mild steel 1 6.32 2 3.49 +2.83Gas Tungsten Arc Welding (GTAW) of stainless

steel 2 5.72 3 3.06 +2.66Gas Tungsten Arc Welding (GTAW) of aluminum 3 5.56 1 3.56 +2.00Gas Metal Arc Welding (GMAW) of aluminum 4 4.50 8 2.64 +1.86Shielded Metal Arc Welding (SMAW) of stainless

steel 10 3.30 14 1.49 +1.81Shielded Metal Arc Welding (SMAW) of mild steel 6 4.03 9 2.38 +1.65Flux Cored Arc Welding (FCAW) of mild steel 8 3.70 10 2.11 +1.59Gas Metal Arc Welding (GMAW) of mild steel 5 4.30 6 2.80 +1.50Gas Metal Arc Welding (GMAW) of stainless steel 11 3.24 11 1.99 +1.25Plasma Arc Cutting (PAC) of mild steel 7 3.77 7 2.71 +1.06Hardfacing mild steel using the Shielded Metal Arc Welding (SMAW) process 13 2.36 15 1.40 +0.96Oxygen/Fuel Cutting (OFC) of mild steel 14 2.18 13 1.57 +0.61Plasma Arc Cutting (PAC) of stainless steel 9 3.41 5 2.88 +0.53Plasma Arc Cutting (PAC) of aluminum 12 3.21 4 3.02 +0.19Oxygen/Fuel Welding (OFW) of mild steel 16 1.26 16 1.13 +0.13Oxygen/Fuel Brazing (OFB) of dissimilar metals 15 1.54 12 1.60 -0.06

Conclusions, Implications, and Recommendations

Results of this study indicated that for each construct (importance to teach, ability to perform, and ability to teach students to perform), teachers self-perceived efficacy levels increased after participating in the Lincoln Electric welding technology workshop. The construct Importance of Competency increased the least by .42 and the construct Ability to Teach Competency increased the most by .92.

Participants indicated that overall, they need professional development in each of the Lincoln Electric welding technology workshop competencies. Furthermore, the top two needed competencies for professional development for both Performance Competence and Consequence Competence were related to Gas Tungsten Arc Welding of mild steel and aluminum. The least needed competency for the Performance Competence was Shielded Metal Arc Welding of stainless steel. For the Consequence Competence, Oxygen/Fuel Welding of mild steel ranked as the lowest professional development need.

Little (1993) found that to improve teachers’ competence in teaching curriculum, it was necessary to link professional development with industry experts to establish mechanisms of consultation, support, and to introduce new ideas. Based upon the conclusions of this study, several implicative questions arose from the industry sponsored structure of this workshop and the continued professional development needs of the teachers. Does the Lincoln Electric welding

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technology workshop effectively hone a teacher’s skill level to authentically perform each competency and to teach the competencies to students? Is the length and the curriculum content of the workshop appropriate for the existing knowledge base of school-based agricultural educators? Will an increase in teachers’ self-perceived efficacy levels positively impact student achievement in the classroom and laboratory? Can other areas of the agricultural education curriculum benefit from industry sponsored professional development workshops for teachers? These questions and others are grounds for future research concerning industry facilitated professional development workshops.

When measuring the professional development needs of teachers, is the Borich (1980) needs assessment model the most accurate assessment of teachers’ needs? Or as researchers, should we consider measuring a teachers’ self-perceived efficacy (Bandura, 1997; Tschannen-Moran, Woolfolk-Hoy, & Hoy, 1998) on multiple levels, using multiple instruments? As teachers are being assessed in the future, based upon student achievement levels, should we consider if teachers’ self-perceived efficacy levels involving the instruction of highly skilled areas of agricultural education (i.e. welding technology) have an impact on classroom and laboratory learning?

Possibly, the real question is does self-perceived efficacy provide an accurate measure of a teachers’ actual ability level to perform a highly skilled competency such as electric arc welding? Is it quite possible that self-perceived efficacy levels are inflated to account for inadequacies? Is authentic assessment a potentially better tool to understand teachers professional development needs? Several needs assessments, focused on agricultural mechanics, have noted discrepancies in teachers’ self-perceived ability to perform competencies, but few have separated teachers’ self-perceived ability to perform competencies from their ability to teach those competencies. Although this study lends insight to competencies in need of improvement, observed differences in need existed between Performance Competence and Consequence Competence. Further study is needed to determine if differences exist between self-perceived ability and actual ability.

As researchers seek to better understand the professional development needs of teachers and how self-perceived efficacy levels impact student achievement in agricultural education, it is recommended that future research incorporate the full use of the Borich (1980) needs assessment model to better understand the needs of teachers. Furthermore, it is also recommended that all career and technology teacher educators consider using an actual assessment of skill competence combined with the perceived scales utilized in the Borich needs assessment model (importance, ability to perform, and ability to teach students to perform) when evaluating the professional growth needs of teachers at all career stages. If the goal of academic and career and technology education is to prepare students for the college classroom and the workforce, it would be soundly advised that industry partners be collaborated with in order to provide teachers with the most up-to-date professional development education so that students’ career needs can be met.

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References

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, N.J.: Prentice Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control, New York: W.H. Freeman.

Barrick, R. K., Ladewig, H. W., & Hedges, L. E. (1983). Development of a systematic approach to identifying technical inservice needs of teachers. The Journal of the American Association of Teacher Educators in Agriculture, 24(1), 13-19. doi:10.5032/jaatea.1983.01013

Birkenholz, R. J., & Harbstreit, S. R. (1987). Analysis of the inservice needs of beginning vocational agricultural teachers. The Journal of the American Association of Teacher Educators in Agriculture, 28(1), 41-49. doi: 10.5032/jaatea.1987.01041

Borich, G. D. (1980). A needs assessment model for conducting follow-up studies. The Journal of Teacher Education, 31(3), 39-42. doi: 10.1177/002248718003100310

Camp, W. G., Broyles, T., & Skelton, N. S. (2002). A national study of the supply and demand for teachers of agricultural education in 1999-2001. Blacksburg, VA: Virginia Polytechnic Institute and State University.

Connors, J. J. (1998). A regional Delphi study of the perceptions of NVATA, NASAE, and

AAAE members on critical issues facing secondary agricultural education programs. Journal of Agricultural Education, 39(1), 37-47. doi: 10.5032/jae.1998.01037

Darling – Hammond, L. (1997). Doing what matters most: Investing in quality teaching. New York: National Commission on Teaching & America’s Future.

Darling-Hammond, L. (2000). Teacher quality and student achievement: A review of state policy evidence. Education Policy Analysis Archives, 8(1), 1-49.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method (3rd ed.). Hoboken, NJ: Wiley and Sons.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Garton, B. L., & Chung, N. (1995). An analysis of the inservice needs of beginning teachers of agriculture. Proceedings of the 22nd Annual National Agricultural Education Research Meeting, 22, 77-83.

Guskey, T. R. (2002). Professional development and teacher change. Teachers and teaching: Theory and practice, 8(34), 381-391.

Kent, A. M. (2004). Improving teacher quality through professional development. Education, 124(3), 427- 435.

117

Knobloch, N. A. & Whittington, M. S. (2002). Novice teachers' perceptions of support, teacher preparation quality, and student teaching experience related to teacher efficacy. Journal of Vocational Educational Research, 27(3).

Knowles, M. S., Holton III, E. F., & Swanson, R. A. (2005). The adult learner. San Diego, CA: Elsevier.

Layfield, K. D., & Dobbins, T. R. (2000). An assessment of South Carolina agriculture teachers’ inservice needs and perceived competencies. Proceedings of the 2000 National Agricultural Education Research Conference, San Diego, CA, 572-584.

Layfield, K. D., & Dobbins, T. R. (2002). Inservice needs and perceived competencies of South Carolina agricultural educators. Journal of Agricultural Education, 43(4) 46-55. doi: 10.5032/jae.2002.04046

Leake, A. H. (1915). The means and methods of agricultural education. Boston, MA & New York, NY: The Riverside Press Cambridge, 83.

Little, J. W. (1993). Teachers’ professional development in a climate of educational reform. Educational Evaluation and Policy Analysis, 15(2), 129-151.

McKim, B. R., & Saucier, P. R. (2011). Agricultural mechanics laboratory professional development needs of Wyoming secondary agriculture teachers. Journal of Agricultural Education, 52(3), 75-86. doi:10.5032/jae.2011.03075

Mundt, J. P., & Connors, J. J. (1999). Problems and challenges associated with the first years of teaching agriculture: A framework for preservice and inservice education. Journal of Agricultural Education, 40(1), 38-48. doi: 10.5032/jae.1999.01038

Myers, B. E., Dyer, J. E., & Washburn, S. G. (2005). Problems facing beginning agriculture teachers. Journal of Agricultural Education, 46(3), 47-55. doi: 10.5032/jae.2005.03047

National FFA Organization (2010). FFA & agriculture statistics. Retrieved from http://ffagive.org/index.cfm?method=c_about.stats

Nesbitt, D. L., & Mundt, J. P. (1993). An evaluation of the University of Idaho beginning agriculture teacher induction program. Journal of Agricultural Education, 34(2), 11-17.

doi:10.5032/jae.1993.02011

Nunnally, J. C. & Bernstein, I. H. (1994), Psychometric Theory, (3rd ed.) New York, NY: McGraw-Hill Companies

Osborne, E. W. (Ed.) (2007). National research agenda for agricultural education and communication: 2007-2010. Gainesville: University of Florida, Department of Agricultural Education and Communication, 20.

Pratt, D. D. (1988). Andragogy as a relational construct. Adult Education Quarterly, 38(3), 160-181. doi: 10.1177/0001848188038003004

118

Rivers, J. C., & Sanders, W. L. (1996). Cumulative and residual effects on teachers on future student academic achievement. Knoxville: University of Tennessee Value-Added Research and Assessment Center.

Roberts, T. G., & Dyer, J. E. (2004). In-service needs of traditionally and alternatively certified agricultural teachers. Journal of Agricultural Education, 45(4), 57-70. doi: 10.5032/jae.2004.04057

Stimson, R. W. (1920). Vocational Agricultural Education: By Home Projects. New York: The MacMillan Company.

Saucier, P. R., & Terry, Jr., R. (2011, May). Technical curriculum professional development needs of Missouri school-based agriculture teachers based upon career stage. Paper presented at the 2011 American Association for Agricultural Education Conference, Coeur d’Alene, ID.

Saucier, P. R., Terry, Jr. R, & Schumacher, L. G. (2009). Laboratory management in-service needs of Missouri agriculture educators. Paper presented at the 2009 Southern Region of the American Association for Agriculture Education Conference, USA, 176-192.

Saucier, P. R., Tummons, J. D., Terry, Jr. R, & Schumacher, L. G. (2010, May). Professional development in-service needs of Missouri agricultural educators. Paper presented at the 2010 American Association for Agricultural Education Conference, USA.

Tschannen-Moran, M., Woolfolk-Hoy, A.W., & Hoy, W.K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68(2), 202-248.

Washburn, S. G., & Dyer, J. E. (2006). Inservice needs of beginning agriculture teachers. Proceedings of the 2006 American Association for Agricultural Education- Southern Agricultural Education Research Conference, Orlando, FL, 577-589.

Washburn, S. G., King, B. O., Garton, B. L., & Harbstreit, S. R. (2001). A comparison of the professional development needs of Kansas and Missouri teachers of agriculture. Proceedings of the 28th Annual National Agricultural Education Research Conference, New Orleans, LA, 396-408.

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Barriers Influencing Missouri School-Based Agricultural Educators to Avoid the Instruction of Agricultural Mechanics Curriculum

P. Ryan Saucier, Texas State University – San Marcos

Abstract

The National Research Agenda for Agricultural Education and Communications suggests that teachers promote “highly effective educational programs [that] will meet the academic career and developmental needs of diverse learners in all settings and at all levels” (Doerfert, 2011, p. 24). The purpose of this census was to determine the barriers preventing Missouri school-based agricultural educators from instructing the curriculum found within the course Agricultural Construction 1 and/or 2. Data was collected via Hosted Survey™ from all teachers who instructed this course during the 2009-2010 academic school year (N = 257). A total of 203 (79%) teachers responded. The majority of the respondents chose not to teach the curriculum areas found within the course; however Project Construction curriculum was the most commonly taught curriculum area. The factors, Equipment Available to Teach, Facilities Available to Teach, and Budget Available to Teach, was found to be the most influential factor impacting school-based agriculture educators’ decision to not teach all of the agricultural mechanics curriculum areas. Administration Importance was the least influential factor persuading teachers to not instruct some of the curriculum areas. Future research is recommended to better understand the phenomenon of curriculum instruction choice by teachers and to implement teacher professional development.

Introduction and Literature Review

Knobloch (2008) found that classroom and laboratory instructional practices are somewhat based on how teachers choose to teach the curriculum content with the resources allocated to them and within the schools’ learning environment. Additionally, he found that the predetermined beliefs of teachers often influence how they connect academic content in the classroom to real-life applications in the laboratory or community (Knobloch, 2008). Often time, these beliefs are developed in part to personal beliefs about the curriculum or content (Borko & Putnam, 1996; Moseley, Reinke, & Bookout, 2002; Pajares, 1992); availability of time, availability instructional resources, level of preparation regarding the content (Thompson & Balschweid, 1999), comfort level with the content, (Knobloch & Ball, 2003), perceived value of the content (Lawrenz, 1985), past experiences with the content area (Calderhead, 1996; Thompson & Balschweid, 1999), teaching environment (Knoblock, 2001) and motivation (Bandura, 1997; Tschannen – Moran, Woolfolk-Hoy, & Hoy, 1998). In 2001, Knobloch also posited that the development and performance of teachers is also influenced by the interaction of these personal and environmental factors and the situations in which they teach. The National Research Agenda for Agricultural Education and Communications suggests that teachers promote “highly effective educational programs [that] will meet the academic career and developmental needs of diverse learners in all settings and at all levels” (Doerfert, 2011, p. 24). As teacher educators, if we can understand the factors that influence teachers’ decisions to instruct various aspects of the curriculum, can we then help shape a more fruitful environment for student academic mastery and teacher

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performance by providing varied pre-service instruction and professional development opportunities?

Theoretical Framework

The Theory of Planned Behavior, developed by Ajzen and Fishbein (1980) and Fishbein and Ajzen (1975), was used as the theoretical base for this study. Their theory was developed to understand persons’ behaviors over which people have incomplete volitional control. Ajzen further describes this theory suggesting that investigators should not only look at beliefs, attitudes, and intentions of individuals, but also their behavior. A central factor in the Theory of Planned Behavior (Ajzen, 1991) is that an individuals’ intention to perform a given behavior, are assumed, and can be used to capture the motivational factors that influence a behavior. Additionally, motivational factors are considered to be indications of how hard people are willing to try and how much of an effort they are planning to exert in order to perform the behavior (Ajzen, 1991). The authors further indentify non-motivational factors as factors that can be used to determine a person’s performance at a given behavior. These non-motivational factors can include the availability of requisite opportunities and resources that include: time, money, personal skill level, and the cooperation of others (Ajzen, 1991). Communally, motivational and non-motivational factors represent a person’s actual control over a behavior. Therefore, the theory states that if a person has the required opportunities and resources, and intends to perform the behavior, the person should succeed in their behavior. In addition, the component, subjective norm, included in Ajzen's theory (1991) represents the perceived social pressures on the individual. These subjective norms refer to peoples’ beliefs about other people's attitudes towards the behavior and how important their opinions are. In this study, the perceived behavioral control component refers to the extent to which teachers believe themselves to be capable of teaching curriculum which is assumed to reflect past experience as well as anticipated impediments and obstacles (Ajzen, 1988). The inclusion of this component in Ajzen's theory recognizes that if teachers are not confident about their ability to perform curriculum skills, then they may feel unable to teach the curriculum in the classroom or laboratory. By understanding the factors that influence a teachers’ decision to instruct curriculum, professional development opportunities can be developed to aid teachers in skill and pedagogy development; thus, aid in student academic achievement by providing quality skill-based experiential learning opportunities in the classroom and the laboratory.

Purpose and Research Questions

The purpose of this study was to describe the barriers that influence Missouri school-based agriculture teachers’ choice to not teach specific curriculum found within the agricultural education course entitled Agricultural Construction 1 and/or 2.

1. What are the personal, professional, and program characteristics of school-based agricultural educators in Missouri who teach the agricultural education course Agricultural Construction 1 and/or 2?

2. Which of the selected curriculum components of the agricultural education course Agricultural Construction 1 and/or 2 do Missouri school-based agricultural educators choose not to teach?

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3. What barriers influence Missouri school-based agricultural educators’ decisions to not teach selected curriculum components of the agricultural education course Agricultural Construction 1 and/or 2?

MethodsPopulation

The target population consisted of all school-based agriculture teachers (N = 257) in Missouri who taught the agricultural education course Agricultural Construction 1 and/or 2 during the 2009-2010 academic school year. The frame for this study was obtained from the 2009-2010 Missouri Agricultural Education Directory, published by the Missouri Department of Elementary and Secondary Education.

Instrumentation

A researcher-designed, web-based questionnaire was used to collect the data for this census. The two section questionnaire was developed by the researcher and distributed using Hosted Survey™. Section I was composed of questions related to the instruction of six skill-related curriculum areas (e.g. Arc Welding, Project Construction, Oxy-Gas and Other Cutting/Welding Processes, Woodworking, Metals, and Finishing) included in the Agricultural Construction 1 and/or 2 curriculum. This section also contained questions relating to the factors that influence, or do not influence, a teacher to teach the selected components of the curriculum. A five-point, summated rating scale was offered for respondents to provide information about factors that influence their decision to teach, or not to teach, a curriculum component. The response scale for each factor was: 0 = no influence, 1 = little influence, 2 = some influence, 3 = moderate influence, and 4 = great influence. Section II of the instrument consisted of ten questions designed to collect information on personal, professional and program information of the respondents and the school-based agricultural education program in which they teach.Validity of the instrument was determined with use of a panel of experts (N = 7) to review the instrument for face and content validity. Recommendations from the panel were then utilized to improve the instrument design. Reliability of the instrument was estimated with use of a pilot study that was conducted with a similar population of 23 school-based agriculture teachers in the neighboring Kentucky. Of the 23 teachers contacted, 22 (96%) completed all items in Sections I and II. The resulting Cronbach’s alpha coefficients ranged from .73 to .91. The constructs found within the instrument were deemed reliable (Garson, 2008; Nunnelly, 1978).

Procedures

The data collection process for this study utilized the Dillman (2007) Tailored Designed Method for Internet Surveys for guidance. For this study, subjects were contacted up to five times through electronic mail from the researcher. In the end, 203 (79%) Missouri agricultural educators provided usable responses for this study.

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Data Analysis

Data were analyzed using the Statistical Package for the Social Sciences® (SPSS) 17.0 for Windows and Microsoft Office Excel® 2007. Data analysis methods were selected as a result of determining the scales of measurement for the variables measured.

ResultsResearch Question One

Of the 203 teachers who participated in this study, 83.30% were male (n = 169). The mean age for teachers was 37.26 years (SD = 9.83). The mean number of university semester credit hours earned in agricultural mechanics coursework was 10.71 (SD = 11.35). On average, Missouri school-based agriculture teachers who instruct Agricultural Construction 1 and/ or Agricultural Construction 2 had 12.66 years of teaching experience (SD = 9.06). The subjects supervised students’ agricultural mechanics SAEs for an average of 4.90 hours per week (SD = 6.65). Additionally, the mean enrollment of students in an agricultural education program, in which the respondents taught, was approximately 94 students (M = 93.71; SD = 65.38). Furthermore, more than 90% (n = 185; 91.10%) of the respondents reported that they completed a traditional teacher certification. The remainder of the subjects reported they completed some form of an alternative teacher certification (n = 18; 8.90%). No respondents indicated they completed any form of an emergency teacher certification (n = 0; 0.00%).

Research Question Two

Research question two sought to identify the curriculum areas that school-based Missouri agricultural educators do not teach within the course Agricultural Construction 1 and/or 2. Respondents (n = 31; 15.30%) reported they do not teach arc welding curriculum. About a tenth of the teachers (n = 23; 11.30%) indicated they do not teach project construction curriculum. Additionally, respondents indicated that in the curriculum area of oxy-gas and other cutting/welding processes, only about 16% of teachers (n = 32; 15.80%) indicated that they do not teach this curriculum in the course Agricultural Construction 1 and/or 2. Over one-third of respondents (n = 79; 38.90%) reported that they do not teach the curriculum area, woodworking. Metals curriculum was also reported as being not being taught by one-third of the respondents (n = 63; 31.00%). Finally, 60 (29.60%) teachers indicated to the researcher that they do not teach finishing curriculum. (see Table 1)

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Table 1Curriculum Areas Taught by Missouri School-Based Agriculture Teachers Who Instruct Agricultural Construction 1 and/or 2 (n = 203)

Curriculum Areas Yes Nof % f %

Arc Welding 172 84.70 31 15.30Project Construction 180 88.70 23 11.30Oxy-Gas and Other Cutting/Welding Processes 171 84.20 32 15.80Woodworking 124 61.10 79 38.90Metals 140 69.00 63 31.00Finishing 143 70.40 60 29.60 Research Question Three

Missouri school-based agriculture teachers who instruct Agricultural Construction 1 and/or 2, indicated that Equipment Available to Teach was the greatest factor that influenced their decision to not teach arc welding curriculum to students (Mean = 2.59; SD = 1.54). Conversely, the factor Administration Importance was the least important barrier influencing their decision to not teach arc welding curriculum to students (Mean = 2.00; SD = 1.35). A summary of the remaining factors that influenced teachers to not instruct arc welding are displayed in Table 2.

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Table 2Barriers Influencing Missouri School-Based Agriculture Teachers to Avoid the Instruction of Arc Welding Curriculum (n =31)

FactorLevels of Influence

0 1 2 3 4f % f % f % f % f % M SD

Equipment Available to Teach 6 17.60 3 8.80 4 11.80 7 20.60 14 41.20 2.59 1.54Personal Importance 6 17.60 2 5.90 3 8.80 13 38.20 10 29.40 2.56 1.44Facilities Available to Teach 6 17.60 4 11.80 3 8.80 7 20.60 14 41.20 2.56 1.56Personal Interest in Teaching 7 20.60 3 8.80 4 11.80 7 20.60 13 38.20 2.47 1.58Budget Available to Teach 7 20.60 3 8.80 4 11.80 7 20.60 13 38.20 2.47 1.58Student Importance 6 17.60 4 11.80 4 11.80 12 35.30 8 23.50 2.35 1.43Experience in Teaching 7 20.60 4 11.80 4 11.80 9 26.50 10 29.4 2.32 1.53Personal Ability to Teach 8 23.50 4 11.80 5 14.70 8 23.50 9 26.50 2.18 1.55Community Importance 6 17.60 6 17.60 7 20.60 8 23.50 7 20.60 2.12 1.41Administration Importance 6 17.60 5 14.70 13 38.20 3 8.80 7 20.60 2.00 1.35Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Within the curriculum area of project construction, teachers indicated that the factors Facilities Available to Teach (Mean = 2.64; SD = 1.35) and Budget Available to Teach (Mean = 2.64; SD = 1.35) impacted their decision to not teach this curriculum area to their students the most. Moreover, Administration Importance influenced teachers the least to not teach project construction curriculum to students enrolled in Agricultural Construction 1 and/or 2 (Mean = 1.84; SD = 1.21). Additional data concerning this curriculum area are displayed in Table 3.

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Table 3Barriers Influencing Missouri School-Based Agriculture Teachers to Avoid the Instruction of Project Construction Curriculum (n = 23)

FactorLevels of Influence

0 1 2 3 4f % f % f % f % f % M SD

Facilities Available to Teach 3 12.00 2 8.00 4 16.00 8 32.00 8 32.00 2.64 1.35Budget Available to Teach 3 12.00 2 8.00 4 16.00 8 32.00 8 32.00 2.64 1.35Equipment Available to Teach 3 12.00 2 8.00 4 16.00 10 40.00 6 24.00 2.56 1.29Personal Interest in Teaching 4 16.00 2 8.00 3 12.00 11 44.00 5 20.00 2.44 1.36Experience in Teaching 4 16.00 3 12.00 6 24.00 8 32.00 4 16.00 2.20 1.32Personal Importance 4 16.00 3 12.00 6 24.00 8 32.00 4 16.00 2.20 1.32Personal Ability to Teach 4 16.00 4 16.00 5 20.00 7 28.00 5 20.00 2.20 1.38Student Importance 4 16.00 4 16.00 5 20.00 10 40.00 2 8.00 2.08 1.26Community Importance 4 16.00 5 20.00 4 16.00 11 44.00 1 4.00 2.00 1.23Administration Importance 4 16.00 5 20.00 10 40.00 3 12.00 3 12.00 1.84 1.21Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Oxy-gas and other cutting/welding processes was another curriculum area found with the agricultural education course entitled Agricultural Construction 1 and/or 2. Equipment Available to Teach was the factor that had the greatest influence on a teachers’ decision to not teach oxy-gas and other cutting curriculum to students (Mean = 2.55; SD = 1.48). However, the factor Administration Importance played the least greatest role in influencing a teachers’ decision to not teach oxy-gas and other cutting/welding processes curriculum to students (Mean = 1.45; SD = 1.35). The remaining factors that influenced teachers to not instruct oxy-gas and other cutting/welding processes are displayed in Table 4.

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Table 4Barriers Influencing Missouri School-Based Agriculture Teachers to Avoid the Instruction of Oxy-Gas Cutting/Welding Curriculum (n = 32)

FactorLevels of Influence

0 1 2 3 4f % f % f % f % f % M SD

Equipment Available to Teach 5 15.20 3 9.10 7 21.20 5 15.20 13 39.40 2.55 1.48Budget Available to Teach 5 15.20 5 15.20 3 9.10 8 24.20 12 36.40 2.52 1.50Facilities Available to Teach 6 18.20 4 12.10 5 15.20 7 21.20 11 33.30 2.39 1.52Personal Interest in Teaching 8 24.20 3 9.10 6 18.20 11 33.30 5 15.20 2.06 1.44Personal Importance 8 24.20 3 9.10 6 18.20 12 36.40 4 12.10 2.03 1.40Personal Ability to Teach 9 27.30 3 9.10 4 12.10 12 36.40 5 15.20 2.03 1.49Experience in Teaching 8 24.20 4 12.10 5 15.20 12 36.40 4 12.10 2.00 1.41Student Importance 8 24.20 5 15.20 6 18.20 12 36.40 2 6.10 1.85 1.33Community Importance 10 30.30 7 21.20 7 21.20 9 27.30 0 0.00 1.45 1.20Administration Importance 12 36.40 5 15.20 7 21.20 7 21.20 2 6.10 1.45 1.35Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Teachers also indicated that the factor Facilities Available to Teach had the greatest impact on their decision to not teach the curriculum area of woodworking to their students (Mean = 1.87; SD = 1.73). Likewise, Administration Importance influenced teachers the least to not teach woodworking curriculum to students enrolled in Agricultural Construction 1 and/or 2 (Mean = 1.11; Median SD = 1.22). Further data concerning this curriculum area are displayed in Table 5.

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Table 5Barriers Influencing Missouri School-Based Agriculture Teachers to Avoid the Instruction of Woodworking Curriculum (n = 79)

FactorLevels of Influence

0 1 2 3 4f % f % f % f % f % M SD

Facilities Available to Teach 28 35.40 12 15.20 7 8.90 6 7.60 26 32.90 1.87 1.73Equipment Available to Teach 28 35.40 12 15.20 8 10.10 5 6.300 26 32.90 1.86 1.72Budget Available to Teach 31 39.20 16 20.30 8 10.10 7 8.90 17 21.50 1.53 1.59Personal Interest in Teaching 30 38.00 14 17.70 14 17.70 14 17.70 7 8.90 1.42 1.38Personal Importance 32 40.50 10 12.70 20 25.30 11 13.90 6 7.60 1.35 1.34Experience in Teaching 33 41.80 14 17.70 15 19.00 11 13.90 6 7.60 1.28 1.34Community Importance 31 39.20 16 20.30 18 22.80 8 10.10 6 7.60 1.27 1.29Personal Ability to Teach 33 41.80 17 21.50 11 13.90 11 13.90 7 8.90 1.27 1.37Student Importance 31 39.20 13 16.50 24 30.40 6 7.60 5 6.30 1.25 1.23Administration Importance 34 43.00 17 21.50 18 22.80 5 6.30 5 6.30 1.11 1.22Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Respondents who instruct Agricultural Construction 1 and/or 2, indicated that Budget Available to Teach was the greatest factor that influenced their decision to not teach metals curriculum to students (Mean = 1.86; SD = 1.60). However, the factor Community Importance played the least role in influencing their decision to not teach metals curriculum to students (Mean = 1.28; SD = 1.18). A summary of the remaining factors that influenced teachers to not instruct metals are displayed in Table 6.

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Table 6Barriers Influencing Missouri School-Based Agriculture Teachers to Avoid the Instruction of Instruct Metals Curriculum (n = 63)

FactorLevels of Influence

0 1 2 3 4f % f % f % f % f % M SD

Budget Available to Teach 19 29.70 12 18.80 9 14.10 7 10.90 17 26.60 1.86 1.60Equipment Available to Teach 18 28.10 11 17.20 13 20.30 7 10.90 15 23.40 1.84 1.54Facilities Available to Teach 18 28.10 11 17.20 16 25.00 7 10.90 12 18.80 1.75 1.46Personal Interest in Teaching 18 28.10 10 15.60 16 25.00 12 18.80 8 12.50 1.72 1.39Personal Ability to Teach 18 28.10 9 14.10 19 29.70 10 15.60 8 12.50 1.70 1.37Personal Importance 18 28.10 9 14.10 20 31.30 10 15.60 7 10.90 1.67 1.33Experience in Teaching 19 29.70 11 17.20 16 25.00 9 14.10 9 14.10 1.66 1.41Student Importance 20 31.30 16 25.00 16 25.00 8 12.50 4 6.30 1.38 1.23Administration Importance 22 34.40 17 26.60 13 20.30 8 12.50 4 6.30 1.30 1.24Community Importance 22 34.40 15 23.40 16 25.00 9 14.10 2 3.10 1.28 1.18Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Within the curriculum area of finishing, teachers indicated that the factor Equipment Available to Teach impacted their decision to not teach this curriculum area to their students the greatest (Mean = 2.44; SD = 1.59). Moreover, Administration Importance influenced teachers the least to not teach finishing curriculum to students enrolled in Agricultural Construction 1 and/or 2 (Mean = 1.05; SD = 1.01). Additional data concerning this curriculum area are displayed in Table 7.

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Table 7Barriers Influencing Missouri School-Based Agriculture Teachers to Avoid the Instruction of Finishing Curriculum (n = 60)

FactorLevels of Influence

0 1 2 3 4f % f % f % f % f % M SD

Equipment Available to Teach 13 21.30 4 6.60 12 19.70 7 11.50 25 41.00 2.44 1.59Facilities Available to Teach 13 21.30 5 8.20 11 18.00 9 14.80 23 37.70 2.39 1.57Budget Available to Teach 17 27.90 5 8.20 16 26.20 6 9.80 17 27.90 2.02 1.57Experience in Teaching 19 31.10 9 14.80 15 24.60 12 19.70 6 9.80 1.62 1.37Personal Interest in Teaching 18 29.50 10 16.40 20 32.80 7 11.50 6 9.80 1.56 1.30Personal Ability to Teach 19 31.10 9 14.80 18 29.50 10 16.40 5 8.20 1.56 1.31Personal Importance 20 32.80 12 19.70 17 27.90 9 14.80 3 4.90 1.39 1.23Student Importance 20 32.80 15 24.60 19 31.10 5 8.20 2 3.30 1.25 1.11Community Importance 20 32.80 18 29.50 16 26.20 6 9.80 1 1.60 1.18 1.06Administration Importance 23 37.70 17 27.90 17 27.90 3 4.90 1 1.60 1.05 1.01Note. Levels of Influence: 0 to 0.50 = No Influence, 0.51. to 1.50 = Little Influence, 1.51 to 2.50 = Some Influence, 2.51 to 3.50 = Moderate Influence, 3.51 to 4.00 = Great Influence

Conclusions, Implications, & RecommendationsResearch Question OneResearch question one sought to describe the personal, professional, and program characteristics of school-based agricultural educators in Missouri who instruct the agricultural education course Agricultural Construction 1 and/or 2. Teachers who instruct this course, are mostly male, 37 years old, and completed a traditional teaching certification program. These teachers have about 13 years of teaching experience, earned almost 11 university semester credit hours in agricultural mechanics coursework, and teach about 94 students per semester. Furthermore, as FFA advisors, these teachers spend about 5 hours per week supervising agricultural mechanics related SAE projects.

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Research Question Two

Research question two sought to identify the selected curriculum components of the agricultural education course Agricultural Construction 1 and/or 2 that school-based agricultural educators in Missouri instructed. The curriculum components that are included in this school-based agricultural education course include: arc welding, project construction, oxy-gas and other cutting/welding, woodworking, metals, and finishing. The majority of teachers indicated that they instruct all curriculum areas included in this course. However, teachers instruct the curriculum areas related to hot metal work, specifically arc welding, project construction, and oxy-gas and other cutting/welding processes, more than the curriculum areas related to woodworking, metals, and finishing. Numerous questions are raised from these results. Why do teachers choose to teach certain curriculum areas over others? What factors influence these teachers’ decisions concerning their choice to instruct curriculum? Why is curriculum related to hot metal skills instructed more than curriculum related to woodworking, metals (cold metal skills), and finishing in Agricultural Construction 1 and/or 2 courses? Are other extraneous factors not found in this study impacting teachers’ decisions to instruct agricultural mechanics curriculum? These questions and others are grounds for future research to better understand teachers’ curriculum instruction decisions. Researchers recommend future research to fully understand this phenomenon.

Research Question Three

Research question three sought to determine the level of influence selected factors have upon a teacher’s choice to not instruct various curriculum components included in the course Agricultural Construction 1 and /or 2. Among the six constructs, teachers indicated that the factors of Equipment Available to Teach, Facilities Available to Teach, and Budget Available to Teach were the most influential factors that persuaded them to not instruct each curriculum area. Furthermore, Administration Importance was the least influential factor that persuaded these teachers to not instruct each curriculum area except for the curriculum area of metals. The remaining factors were distributed sporadically between the most influential factor and least influential factor, and thus, no measurable pattern was established. The Theory of Planned Behavior (Ajzen, 1991) played a major role in the development of the theoretical foundation for this study. The results of this study can be applied to this theory and conceptually worked in reverse order. If researchers can understand teachers’ behavior (the decision to teach or not to teach the curriculum), future research can be conducted to determine their intention to teach. According to Ajzen (1991), a teachers intention to teach is based upon four influential factors: attitude towards the behavior, or teaching agricultural mechanics; the subjective norm, or the social pressures that the administration, the community, and the students themselves, place upon the teacher to instruct the curriculum; motivational factors, such as amount of personal effort, level of intention to teach, and non-motivational factors such as budget, personal skill level, equipment, facilities; and perceived behavioral control, or the extent to which teachers believe themselves to be capable of teaching curriculum which is assumed to reflect past experience as well as anticipated impediments and obstacles. As agricultural educators, if we can unlock these factors and ensure that new teachers have positive experiences, can we then determine if teachers will choose to teach agricultural mechanics curriculum? These questions and others are grounds for future research in this subject area.

Several implications can be extrapolated from these results. Why do the factors Equipment Available to Teach, Facilities Available to Teach, and Budget Available to Teach play such a significant role in determining the curriculum that Missouri teachers instruct in Agricultural Construction 1 and/or 2? How is agriculture teacher’s personal importance toward the instruction of agricultural mechanics curriculum developed? At what point during an agriculture teacher’s career is their level of importance toward the instruction of agricultural mechanics curriculum developed? What factors attribute to the development of a teachers’ level of importance toward the instruction of agricultural mechanics curriculum? Can a teacher’s level of importance toward the instruction of agricultural mechanics curriculum be altered or improved? If so, what methods or opportunities have the potential to influence change in a teacher’s level of importance toward the instruction of agricultural mechanics curriculum?

Another notable result of this study concerns the factor Administration Importance. For 5 out of 6 curriculum areas found within the course Agricultural Construction 1 and/or 2, teachers indicated that Administration Importance was the least important factor that influenced their decision to not teach the various curriculum areas. Why does the factor Administration Importance play such an insignificant role in determining the curriculum that school-based agricultural educators in Missouri choose not to teach? Is this influential factor limited to the course Agricultural Construction 1 and/or 2 or does it impact all courses taught under the agricultural education umbrella? Do teachers not care about the opinion of administrators when it pertains to not instructing curriculum at their school? Or do administrators not have knowledge of the curriculum found within the course Agricultural Construction 1 and/or 2, and therefore, don’t make an influence? These questions and others are grounds for future research regarding curriculum choice in agricultural mechanics programs.

In the realm of education, the responsibility for teacher development is often thought to rest on the shoulders of teacher educators and the teacher development process. However, a question must be posited, does previous experience, or their lack of, aid in the development of an individual’s motivation or personal importance to not teach curriculum? To better understand this phenomenon of teacher development and a teachers’ intent to teach curriculum, it is recommended that future research concerning the study of pre-service teacher curriculum experiences be conducted. Ajzen (1991) found that a teacher’s intention to teach is based upon four influential factors: attitude towards the behavior; the subjective norm; motivational factors; and perceived behavioral control. These factors should be studied to determine if they impact pre-service teachers’ ability to not teach agricultural mechanics curriculum and to better understand and assess the professional development needs of future teachers.

Additionally, professional development for teachers, who lack skill and pedagogy knowledge and experience to teach agricultural mechanics curriculum, should be designed, implemented, and assessed. Such programs should be offered with frequency, variety, and should be delivered in formats and at times that will have the greatest impact upon the largest number of teachers. This goal could be accomplished by providing teachers with workshops offered during the winter and summer breaks, agricultural mechanics courses offered for continuing education or university graduate courses for credit, and even online, self-directed courses. Winter and summer

workshops focusing on agricultural mechanics should be offered at regional locations throughout the state of Missouri and could be located at university or public school facilities.

References

Ajzen, I. (1988). Attitude, personality, and behavior. Chicago, IL: Dorsey Press.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 179-211. DOI: 10.1016/0749-5978(91)90020-T

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, N.J.: Prentice Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control, New York: W.H. Freeman.

Borko, H., & Putnam, R. H. (1996). Learning to teach. In Handbook of educational psychology, eds. D.C. Berlinger and R.C. Calfee, 673-708. New York: Simon, Schuster, & MacMillan.

Calderhead, J. (1996). Teachers: beliefs and knowledge. In Handbook of educational psychology, eds. D.C. Berlinger and R.C. Calfee, 673-708. New York: Simon, Schuster & MacMillian.

Dillman, D. A. (2007). Mail and internet surveys: The tailored design method (2nd ed.). John Wiley & Sons: Hoboken, NJ.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, M.A.: Addison – Wesley.

Garson, G. D. (2008, March). Scales and Standards of Measures. Retrieved from http://faculty.chass.ncsu.edu/garson/PA765/standard.htm#internal.

Knobloch, N. A. (2001). The influence of peer teaching and early field experience on teaching efficacy beliefs of preservice educators in agriculture. Paper presented at the 28th National Agricultural Education Research Conference, 119-131.

Knobloch, N.A. (2008). Factors of teacher beliefs related to integrating agriculture into elementary school classrooms. Agriculture and Human Values, 25(4), 529-539. DOI: 10.1007/s10460-008-9135-z

Knobloch, N.A. & Ball, A. (2003). An examination of elementary teachers’ and agricultural literacy coordinators’ beliefs related to the integration of agriculture. Retrieved from http://www.agriculturaleducation.org/ LinkPages/AgLiteracyK8.asp.

Lawrenz, F. (1985). Impact on a five week energy education program on teacher beliefs and attitudes. School Science and Mathematics, 85(1), 27-36.

Missouri Department of Elementary and Secondary Education (2009). 2009-2010 Missouri Agricultural Education Directory. Jefferson City, MO.

Moseley, C., Reinke, K., & Bookout, V. (2002). The effect of teaching outdoor environmental education on preservice teachers’ attitudes toward self-efficacy and outcome expectancy. Journal of Environmental Education, 34(1), 9-15. DOI: 10.1080/00958960209603476

Nunnelly, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw Hill.

Pajares, M. F. (1992). Teachers’ beliefs and educational research: Cleaning up a messy construct. Review of Research in Education, 62(3), 307-332.

Thompson, G. & Balschweid, M. (1999). Attitudes of Oregon agricultural science and technology teachers toward integrating science. Journal of Agricultural Education, 40(3), 21-29. DOI: 10.5032/jae.1999.03021

Tschannen-Moran, M., Woolfolk-Hoy, A., Hoy, W.K. (1998). Teacher efficacy: It’s meaning and measure. Review of Educational Research, 68(2), 202-248.

CASE Institute and Science Teaching Efficacy

Jonathan D. Ulmer, Texas Tech UniversityJonathan J. Velez, Oregon State University

Phillip A. Witt, Texas Tech UniversityGregory W. Thompson, Oregon State University

Misty D. Lambert, Oregon State UniversityScott Burris, Texas Tech University

Abstract

This descriptive-correlational study sought to investigate teachers’ levels of Personal Science Teaching Efficacy (PSTE) and Science Teaching Outcome Expectancy (STOE) using the Science Teaching Efficacy Beliefs Instrument (STEBI). The population included all teachers completing a CASE Institute training session during the summer of 2010. Assessments were made at two points. First the participants were assessed by using a post-then-pre assessment with a second, follow-up assessment after nine months of implementing the new curriculum. Demographic characteristics are presented to provide insight into the participants. The teachers experienced gains during the institute on both their personal science teaching efficacy and their science teaching outcome expectancy. However, after nine months of using the curriculum, their efficacy remained high while their outcome expectancy returned to the same levels held before attending the professional development. It appears the CASE Institute had a lasting impact on the participants’ personal efficacy, but not their outcome expectancy beliefs. Recommendations are made for future research.

Introduction and Review of Literature

The National Council for Agricultural Education established the Curriculum for Agricultural Science Education (CASE) in 2007. CASE describes their curriculum as “an instructional system that provides intense teacher professional development and curriculum that is changing the culture of agriculture programs” (CASE, 2011, p. 1). The CASE curriculum was developed in collaboration with Project Lead the Way, a nationally recognized nonprofit organization that prepares students to be leaders in the science, technology, engineering, and mathematics (STEM) areas though the use of problems-based investigation. Their purpose is to develop and implement a national curriculum for secondary agricultural education that provides a high level of rigor and relevance to the agriculture, food, and natural resources (AFNR) subject matter. CASE is aligned with the National Council for Agricultural Education’s Agricultural, Food, and Natural Resources Career Cluster Content Standards (Team AGED, 2007). Additionally, the curriculum is aligned with core academic standards including the National Science Education Standards (National Research Council, 1996),, Principles and Standards for School Mathematics (National Council of Teachers of Mathematics, 2000), and Standards for the English Language Arts (National Council of Teachers of English, 1996).

CASE strives to ensure quality teaching by providing extensive professional development for teachers that leads to certification. The CASE institute is a professional development workshop requiring 80 hours of intense training for each course that CASE has developed.

CASE Institute sessions provide teachers important background related to the pedagogy used in the CASE curricula and practice teaching various lessons to prepare them for classroom instruction. Teachers are required to attend the entire 10-day, 80 hour workshop and CASE Institute instructors determine if each teacher is adequately prepared to provide instruction using CASE curricula. This institute is typically hosted by a college or university, and entails full-time, hands-on training in the use of the CASE curriculum. Institute participants have the opportunity to work their way through the experiments and applied components of the curriculum. In a small case study, Dixon (1999) found that innovative curriculum can impact a teacher in positive ways. While some changes may be a direct result of the written curriculum, others may be a result of changing philosophy from using the curriculum.

The professional development component of the CASE curriculum is unique among the resources typically used in agricultural education. Kent (2003) stated that high quality professional development is a key component to the success of educational programs. As agricultural education continues to evolve with new curricular goals in the areas of math and science, teachers’ professional development needs will grow. Sullivan (1999) found providing prolonged and sustained professional development, in conjunction with teacher quality, can be used as an excellent predictor of student success. In a study of math and science teachers, Garet, Porter, Desimone, Birman, & Yoon (2001) found that professional development that focuses on content, hands-on learning, and application to the classroom is most likely to produce an increase in achievement. These characteristics are all hallmarks of the CASE model of professional development.

Professional development in regard to the incorporation of science and math has received much attention in the agricultural education profession. In a Meta analysis of the research on science integration, Wilson and Curry (2011) found that several researchers have called for increased support for teachers in the form of professional development opportunities. It was reported that teachers who participated in these professional development opportunities were more confident to teach science than the control groups. Darling-Hammond (1996) indicated that the lack of professional development for beginning and seasoned teachers is a barrier for student learning in the United States.

The notion of teacher confidence, or teaching efficacy, is another concept that is prevalent in agricultural education research. Roberts, Harlin, and Ricketts (2006) investigated teaching efficacy of student teachers during their internship experience and found that teachers became more efficacious from the beginning to the end of their teaching experience. Wolf, Foster, and Birkenholz (2010) also explored teaching efficacy of student teachers. They found that certain experiences during the student teaching experience resulted in increased levels of teaching efficacy, while others had no effect. Gill (2009) found pre-service teachers had more confidence in their ability to integrate academic content into their teaching when specifically instructed on integration. This highlights the need to explore professional development opportunities so that their effect on teachers can be better understood.

While teaching efficacy has been studied in practicing teachers with mixed results, Hamilton and Swortzel (2007) studied agricultural science teachers’ ability to teach science and their science teaching efficacy. They found that the teachers in their study had a high science

teaching efficacy, but it correlated negatively with their ability. These results are consistent with the results of Scales, Terry, and Torres (2009) who found that agriculture teachers were confident they could integrate science content, but scored low on a science subject test.

This disconnect between teacher performance and teacher confidence might indicate teachers are not prepared to integrate science into their classroom. Boone, Gartin, Boone, and Hughes (2006) found that agricultural science teachers had limited knowledge of science topics. Warnick and Thompson (2007) found that teachers believed that a lack of funding and equipment were also barriers to integrating science into the agriculture curriculum. These are examples of many barriers to integrating science into the agriculture curriculum that have been reported by researchers. It is important to remember that “agricultural education provides students with transferable academic skills so as to prepare them to achieve in other courses” and higher education (Dailey, Conroy, & Shelley-Tolbert, 2001, p. 18). Often these conclusions result in recommendations of providing professional development programs that are designed to break down these barriers (Wilson & Curry, 2011).

Theoretical Framework

The theoretical framework for this study is based on Bandura’s Social Cognitive Theory and the concept of self-efficacy. Social Cognitive Theory (Bandura, 1986) grew out of Bandura’s frustration with earlier depictions of human agency captured in the Psychodynamic, Trait, and Behaviorist theories. Earlier theories focused on the locus of agency in humans as either autonomous or mechanical. Bandura proffered that neither is entirely true, rather, the locus of agency is interactive and shares a reciprocal relationship between determinants, action and environmental factors (Bandura, 1986). Bandura described this effect and termed it reciprocal determinism. Bandura (1986) stated “the relative influence exerted by the three sets of interacting factors will vary for different activities, different individuals, and different circumstances” (p. 24). After the development of reciprocal determinism, Bandura began to conceptualize his ideas concerning how people develop beliefs in their ability to succeed; he called his idea self-efficacy.

Bandura defined self-efficacy as judgments about one’s ability to organize and execute specific courses of action (Bandura, 1997). Bandura identified four primary sources of self-efficacy, listed in order from the perceived greatest contributor, they are: mastery experiences, vicarious experiences, verbal persuasion, and physiological and affective states (Bandura, 1997). Mastery experiences provide the greatest source of self-efficacy information and can be developed through application of the learning broken down into small steps which yield frequent successes. Vicarious experiences are most frequently provided through modeled experiences. Both students and teachers can enhance self-efficacy by direct observation of their peers. The vicarious effect is enhanced when, through observation, the observer feels a sense of social similarity to the model. Mastery experiences and vicarious experiences are two of the most powerful sources of self-efficacy (Bandura, 2006).

Verbal persuasion serves to strengthen belief in an individual’s ability to succeed by providing positive, social reinforcement. Bandura (1997) believed that verbal persuasion could solidify the beliefs of an individual who was struggling in a given activity. Bandura (1997)

stated “verbal persuasion alone may be limited in its power to create enduring increases in perceived efficacy, but it can bolster self-change if the positive appraisal is within realistic bounds” (p. 101). The last self-efficacy source identified by Bandura was physiological and affective states. Simply put, individuals can establish self-efficacy information through anxiety, stress, arousal, fatigue and mood states (Pajares, 1997). Affective mood states allow individuals to gauge their degree of confidence in a particular activity (Pajares, 2002).

Specific to this study, self-efficacy was focused on teacher efficacy. Teacher self-efficacy has been identified as “the extent to which teachers believe they can affect student learning” (Dembo & Gibson, 1985, p. 1). Teacher self-efficacy has been found to be a very powerful construct with connections to student achievement, motivation, and student self-efficacy (Tschannen-Moran & Hoy, 2001). Woolfolk (2007) identified teacher efficacy as one of the few teacher traits directly connected to student academic achievement. Woolfolk, Hoy, and Hoy (2009) suggested that teaching efficacy is a powerful construct and “helping teachers develop a strong sense of efficacy beliefs early in their career will pay lasting dividends” (p. 169).

Teaching efficacy has been further refined into personal science teaching efficacy. Teachers high in personal science teaching efficacy are likely to persist longer in a task, provide more academic focus, and provide more feedback for students than teachers low in science teaching efficacy (Gibson & Dembo, 1984). Student achievement has been closely linked with teacher efficacy, and personal teaching efficacy has been used to predict teacher behaviors (Ashton, Webb, & Doda, 1983). Teachers who are high in science self-efficacy feel capable to teach science and will likewise persist in their efforts to reach unmotivated students and enlist support from fellow teachers and administrators.

Science teaching efficacy is closely related to outcome expectancies. In fact, Bandura linked the two closely and stated, “The effects of outcome expectancies on performance motivation are partly governed by self-beliefs of efficacy (1989, p. 1180). Outcome expectancy seeks to measure the level at which teachers expect certain behaviors to produce desirable outcomes (Riggs & Enochs, 1989). Bandura theorized that people high in outcome expectancy and high in efficacy would be motivated to engage in and complete tasks. Whereas, individuals low in outcome expectancy and high in efficacy would try hard, but soon become frustrated and give up. For example, a teacher with high outcome expectancy genuinely believes that as a result of their teaching efforts, the students will make substantial cognitive gains. Whereas a teacher low in outcome expectancy might be viewed as a pessimistic teacher who does not believe students can succeed. The theory and research indicates that outcome expectancy and science efficacy work together to allow teachers to be successful in a science-based classroom. The current research sought to examine both personal science teaching efficacy and science teaching outcome expectancies through the use of the Riggs and Enochs (1989) Science Teaching Efficacy Beliefs Instrument (STEBI).

Purpose and Objectives

The purpose of this study was to explore the effect of the CASE Institute and curriculum on the science teaching efficacy belief of teachers. This purpose aligns with the National

Research Agenda for Agricultural Education and Communication (Doerfert, 2011). The study supports research priority areas for Efficient and Effective Agricultural Education Programs which include “the effective integration of science, technology, engineering and math” (p.10). The following research objectives were developed for the study:

1. Describe the demographic characteristics of the CASE institute participants.2. Describe the mean levels of participant efficacy on the pre, post and post-post

assessments of Personal Science Teaching Efficacy and Science Teaching Outcome Expectancy.

3. Analyze the mean differences between the pre, post, and post-post.

Methods and Procedures

The design for this study was descriptive-correlational. The population for this study included all teachers enrolled in CASE Institutes across the country during the summer of 2010 (N = 88). The population frame for this study was obtained from the CASE project staff. Dillman’s (2000) tailored design method for conducting electronic surveys was followed for the data collection process. The instrument used for data collection was originally created by Riggs and Enochs (1990) to measure the self-efficacy of science teachers, called the Science Teaching Efficacy Belief Instrument (STEBI).

The STEBI consisted of 25 questions scaled from 1 (strongly disagree) to 6 (strongly agree). Riggs and Enochs (1990) found that their instrument measured two separate constructs which align with Bandura’s (1997) two dimensions of self-efficacy. The first factor measured the construct of Personal Science Teaching Efficacy (PSTE) using 13 questions. Example questions include “I am not very confident in managing science experiments,” and “When teaching science, I usually welcome student questions.” Previous research reported reliabilities of .92 (Riggs & Enochs, 1990).

The second construct of Science Teaching Outcome Expectancy (STOE) consisted of 12 questions similarly scaled from 1 (strongly disagree) to 6 (strongly agree). Example questions include “The low science achievement of some students cannot generally be blamed on their teachers,” and, “Effectiveness in science teaching has little influence on the achievement of students with low motivation.” Previous research reported reliabilities of .77 (Riggs & Enochs, 1990). The instrument consisted of 11 demographic questions and the 25-item STEBI with terminology adjusted by the researchers to accommodate for high school teachers. Researchers created an on-line form of the instrument using Qualtrics, a web-based survey tool.

Data collection for this study occurred in two phases. The first phase occurred during the summer of 2010, directly following each CASE Institute. Coordinators of each institute were emailed the instrument URL and instructions for distribution to each of the institute participants. This phase of the study utilized a post-then-pre design (Colosi & Dunifon, 2006). Participants were asked to respond twice to each of the 25 STEBI items to indicate their level of agreement before the CASE Institute and their level of agreement after the CASE Institute.

The post-then-pre method is primarily used to reduce response shift bias among participants. Colosi & Dunifon (2006) describe response shift bias as a change in the way participants respond due to the effect of the treatment. Klatt and Taylor-Powell (as cited in Colosi & Dunifon, 2006) described response shift bias as a “change in the participant’s metric for answering questions from the pre test to the post test due to a new understanding of a concept being taught.” One advantage to this model is that participants are able to respond to your pre-test questions using their newly acquired frame of reference.

Another reason for post-then-pre design is that it reduces the requirements and strain on the participants. The CASE Institute is an intensive professional development requiring the teachers to spend ten days on site with 80 hours of in-service training. The researchers were concerned with asking too much of the participants thereby causing them to drop out of the study. By only asking the participants to respond at one point in time, this strain is reduced. However, there remain threats to validity when using the post-then-pre method. Recall, social desirability bias, effort justification bias, and cognitive dissonance are all threats to validity that should be considered when using the method (Hill & Betz, 2005).

The second phase of the study occurred approximately nine months after participants attended their respective CASE Institute during the summer of 2010. This allowed participants to implement the curriculum in their classroom for the academic year. All of the participants that responded to the survey during phase one (n = 71) were contacted in April of 2011. Teachers were again sent an email requesting their participation with a link to the online instrument. During phase two, the same modified STEBI was used. Qualtrics was used to collect data during this phase of the study.

Results

The first objective of this study was to describe the CASE Institute participants from the summer of 2010. The response rate to the first phase of the study was 80.68%, with 71 teachers responding to the instrument. The mean age for teachers enrolled in the 2010 CASE Institutes was 33.90 (SD = 10.99) with a range from 21-62 years. Teachers averaged 7.25 (SD = 8.09) years of experience, with teachers ranging from 0-35 years in the classroom. The participants that had teaching experience reported an average enrollment of 154.45 (SD = 103.28) students in their agriculture education programs (see Table 1).

Table 1Demographic characteristics of 2010 CASE institute participants (n = 71)Characteristic M SD RangeAge (in years) 33.90 10.99 21-62Years of Teaching Experience 7.25 8.08 0-35Students Enrolled in Ag Ed (2009-2010) 154.45 103.28 15-460

Teachers were also asked to describe their involvement with the CASE Institute (see Table 2). At the current time there are three courses developed by CASE in which teachers could become certified. Participants in this study were asked which course they were certified in. The Principles of Agricultural Science – Animal course had the largest enrollment with

39.44% (f = 28) of teachers. This was followed by the Introduction to Agriculture, Food, and Natural Resource (AFNR) course with 30.99% (f = 22) and the Principles of Agricultural Science – Plant course with 29.58% (f = 21). Teachers indicated who made the decision for them to attend the CASE Institute as well. The majority of teachers (f = 45) reported that it was their decision to attend the institute, while 26.76% (n = 19) reported that their administrator made the decision. Two teachers chose not to respond to this question. Five teachers reported that they and their administrator made a mutual decision to have the teacher attend the CASE Institute. The majority of CASE Institute participants (f = 36, 50.7 %) reported that they had earned a master’s degree, with the remaining 49.3% (f = 35) earning a bachelor’s degree. When asked about their certification areas, only 25.35% (f = 18) of institute participants were certified to teach science.

Table 2Demographic Characteristics of Institute Participants (n = 71)Characteristic f %Institute Attended

Animal 28 39.44AFNR 22 30.99Plant 21 29.58

Why did you attend CASEI wanted to 45 63.38Administrations decision 19 26.76Mutual decision 5 7.04

Highest Level of EducationMasters 36 50.70Bachelors 35 49.30

Certified to teach scienceYes 18 25.35No 53 74.65

Objective two sought to determine the level of science teaching efficacy of the CASE participants at three different points throughout the study (see Table 3). During the first phase of the study teachers reported that before the institute they had a mean personal science teaching efficacy (PSTE) score of 4.01 (SD = 1.02) and a science teaching outcome expectancy (STOE) of 4.14 (SD = 0.51). After the institute teachers reported an increase in both areas with a mean PSTE of 4.81 (SD = 0.69) and a STOE of 4.58 (SD = 0.58). The second phase of the study, conducted after a year of teaching, had a response rate of 42.05% with 37 teachers completing the instrument. When participants were asked about their science teaching efficacy after a year of teaching they reported little change. Teachers PSTE mean score was 4.84 (SD = 0.67) and their STOE mean score was 4.17 (SD = 0.53).

Table 3Mean PSTE and STOE values for 2010 CASE Institute participants

Pre (n = 71) Post (n = 71) Post-Post (n = 37)STEBI measure M SD M SD M SDPSTE 4.01 1.02 4.81 0.69 4.84 0.68STOE 4.14 0.51 4.58 0.58 4.17 0.53

The third research objective was to analyze the mean differences between the pre, post, and post-post teaching efficacy scores. For this objective, a one-way repeated measures analysis of variance (ANOVA) was conducted for both PSTE and STOE, with the factor being the point at which the STEBI was administered and the dependent variables the measure of teacher efficacy. When examining the means of PSTE, researchers found that Mauchly’s test indicated that the assumption of sphericity was not violated, X2(2) = 0.82, p < 0.05. The results show that the PSTE was significantly affected by the point at which the STEBI was administered, F(2, 72) = 33.08, p < 0.05 (see Table 4).

Table 4ANOVA Personal Science Teaching Efficacy

SS df MS F pPSTE 18.67 2 9.34 33.08 0.01*Error 20.32 72 0.28* p < .05

Post-hoc tests using the Bonferroni correction revealed that PSTE increased significantly between the pre test and the post test, p < .05 (see Table 5). However, there was no change in PSTE between the post-test and the post-post after one year of implementing CASE, p = 0.44.

Table 5 Post-Hoc Bonferroni for Personal Science Teaching Efficacy

Mean Difference p - valuePre test / Post test -0.82 0.01*Pre test / Post-Post test -0.91 0.01*Post test / Post-Post test -0.09 0.44* p < .05

Teachers’ science teaching outcome expectancy was analyzed in the same way. Mauchly’s test indicated that the assumption of sphericity was not violated for STOE, X2(2) = 3.59, p < 0.05. The repeated measures ANOVA indicated STOE was also significantly affected by the point at which the STEBI was administered, F(2, 72) = 15.69, p < .05 (see Table 6).

Table 6ANOVA Science Teaching Outcome Expectancy

SS df MS F pSTOE 4.46 2 2.23 15.69 .01*Error 10.24 72 0.14* p < .05

Post-hoc tests using the Bonferroni correction revealed that Science Teaching Outcome Expectancy increased significantly between the pre test and the post test, p < .05. The test also indicated that teachers STOE decreased significantly between the post test after the institute and the post-post after a year of teaching, p > 0.05 (see Table 7).

Table 7Post-Hoc Bonferroni for STOE

Mean Difference p - valuePre-test / Post test -0.43 0.01*Pre-test / Post-Post test 0.01 0.99Post test / Post-Post test 0.42 0.01** p < .05

Conclusions, Implications, and Recommendations

The purpose of this study was to explore the impact of the CASE Institute and curriculum on the science teaching efficacy and expectancy beliefs of teachers. Seventy-one teachers participated in the study, ranging in ages from 21 to 62, with 0 to 35 years of teaching experience. The participating teachers reported agriculture education enrollment at their schools from 15-460, averaging around 150 students. Age and years of teaching experience of the 2010 CASE participants are similar to demographics of teachers involved in the 2007 National Agriscience Teacher Ambassador Academy (Myers, Thoron, & Thompson, 2009).

Participants were asked why they attended the 2010 CASE Institute. Of the 69 respondents, over 60% indicated it was their decision to attend the CASE Institute and one quarter (26.76%) of the participants indicated they attended the institute because of an administrator decision to send them. Although a majority of the participants attended CASE as a personal/professional choice, the fact that one fourth of the participants indicated it was an administrator’s decision to send them is interesting.

The implication of this finding is that many administrators see the value of enhancing the agriculture curriculum through science integration. Educating administrators about CASE is an important component to enrolling teachers in CASE. Research has shown the important leadership role that principals play in implementing new programs or curriculum (Hipp & Huffman, 2000; Nanus, 1992; Nwanne, 1987; Rogers, 2007). In fact, Nanus (1992) argued that principals directly control the factors that “determine what shall and shall not be done by the organization” (p.142). With this in mind, it is important to be cognizant of the pivotal agency that principals have with regard to new programs such as CASE. The CASE Institute Lead Teachers should be aware that not all teachers in the CASE Institute may be attending on their own accord. It may be important to continue marketing the CASE model and the advantages of CASE to teachers who are present at the workshop and to design learning activities that emphasize the benefits of CASE, as they may be reluctant participators.

Science teacher efficacy increased from pre and posttest through the post-post (approximately one year after the Institute) scores. CASE teachers began the CASE Institute

slightly agreeing about their science efficacy (M = 4.01), increased more toward agreeing about their science efficacy after the institute (M = 4.81), and then slightly increased toward agreeing in science efficacy approximately one year later (M = 4.84). The pre and posttest mean scores showed a statistically significant difference, while the post-post test scores showed a slight increase. Implications of this finding indicate the CASE Institute significantly impacts science teaching efficacy. The measurements used in this research validate the impact of the CASE Institute on teachers’ sense of science efficacy. To further increase science efficacy change after the CASE Institute, it is recommended that teachers be encouraged to engage in communities of practice following their attendance at a CASE Institute.

The researchers recommend that CASE consider providing additional support after the conclusion of the institute. Specifically, the self-efficacy of teachers can be maintained and increased through their exposure to both mastery and vicarious experiences (Bandura, 1997). Bandura (1997) believed that mastery experiences provide the greatest and most influential source of self-efficacy information by, “organizing mastery experiences in ways that are especially conducive to the acquisition of generative skills” (p. 80) CASE project staff can enable this process by engaging with institute participants after the institute and continuing to break down complex curriculum or skills into more easily mastered sub-skills that allow teachers to experience small frequent successes (Bandura, 1997). CASE can provide vicarious experiences for institute participants by webinars, videos, and other multimedia opportunities which allow successful CASE teachers to model their experiences. The self-efficacy of the teachers will increase if they are allowed to observe the successful experiences of their peers (Bandura, 1986). This effect may partially account for the significant increase in self-efficacy during the actual institute. Participants were able to gain mastery through application and observe the success of their peers, thereby enhancing their vicarious experiences.

Science teacher outcome expectancy also changed during the CASE Institute. CASE teachers began the CASE Institute slightly agreeing about student outcome expectancy (M = 4.14) and then increased more toward agreeing (M = 4.58) about their outcome expectancy after the institute. Teachers then decreased back to slightly agreeing in science outcome expectancy approximately one year after their involvement in the CASE Institute (M = 4.17). The pre and post-test mean scores showed a statistically significant difference from slightly agree toward agree, while the post-post test scores decreased to slightly above pre-institute levels.

The implications of this finding indicate the CASE Institute appears to significantly impact science outcomes expectancy. However, this effect is short lived. Long term educational interventions have historically evidenced a decrease and the constructs in this research decrease accordingly. A study by Posnanski (2010) found that professional development on the nature of science may have been short lived; indicating that efficacy prior to workshops and professional development tends to decrease over time following the training. Neuman and Cunningham (2008) found similar results in a study on literacy instructional practices. There is an intuitive reason for this dip. Following an intervention (training, inservice, etc.) participants tend to feel empowered and ready to take on a new task. However, as time passes the participants assume a more realistic or practical viewpoint. As teachers exit the CASE institute, they feel confident about science teaching and the new curriculum. However, as the year progresses, they encounter realities such as increased class sizes, end of the year procedural requirements, and shrinking

budgets. As a result, teachers may evidence a decrease in outcome expectancy. The good news is that, despite the decrease, they do not decrease below the pretest level and, thus, the results still reveal net gains. The results of this research provide evidence that the CASE institute is impacting the science efficacy and outcome expectancy of the participants.

Recommendations for Future Research

CASE has provided one answer to the National Research Agenda's call to provide uniquely qualified and motivated teachers in agricultural education.  Additional research to determine the effect of the CASE curriculum on student achievement in agriculture as well as math and science content areas should also be conducted. Future research should consider the actual cognitive gains in science during a CASE institute as measured through a content-specific pre and post-test. Perception studies should be conducted to understand what teacher and administrators know and perceive about science integration and curriculum. Understanding the support of integration can support the more widespread adoption of programs such as CASE.

Further research relating to teachers’ concerns and challenges related to science efficacy following a CASE Institute may help to determine how teachers can be better supported upon completion of the CASE institute. While this study did show an impact from the institute, further investigation and longitudinal studies may determine why and at what point during the year teachers experience the decline in their science outcomes efficacy. It would also benefit the researchers to know more about the level of implementation these teachers have been able to achieve with CASE and what kind of financial and fiscal support they are receiving. The level at which the teachers are able to implement CASE could be greatly influencing the impact on their efficacy with teaching science.

References

Ashton, P., Webb, R., & Doda, C. (1983). A study of teachers’ sense of efficacy (Final Report, Executive Summary). Gainesville: University of Florida.

Boone, H. N., Gartin, S. A., Boone, D. A., & Hughes, J. E. (2006). Modernizing the agricultural education curriculum: An analysis of agricultural education teachers’ attitudes, knowledge, and understanding of biotechnology. Journal of Agricultural Education, 47(1), 36-45. doi:10.5032/jae.2006.01078

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44, 1175-1184.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Bandura, A. (2004). Swimming against the mainstream: The early years from chilly tributary to transformative mainstream. Behavior Research and Therapy, 42(6), 613-630. doi:10.1016/j.brat.2004.02.001

Bandura, A. (2006). Adolescent development from an agentic perspective. In F. Pajares & T. Urdan (Eds.). Self-efficacy beliefs of adolescents, (Vol. 5., pp. 1-43). Greenwich, CT: Information Age Publishing.

Colosi, L., & Dunifon, R. (2006). What’s the difference? “Post then pre” & “pre then post”. Cornell Cooperative Extension.

Curriculum for Agricultural Science Education. (2011). CASE Program Description. Retrieved from http://www.case4learning.org/about-case/promotional-tools.html.

Dailey, A. L., Conroy, C. A., & Shelley-Tolbert, C. A. (2001). Using agricultural education as the context to teach life skills. Journal of Agricultural Education 42(1), 11-20. doi: 10.5032/jae.2001.01011

Darling-Hammond, L. (1996). What matters most: A competent teacher for every child. Phi Delta Kappan, 78(3), 193-202.

Dembo, M. H., & Gibson, S. (1985). Teachers’ sense of efficacy: An important factor in school improvement. The Elementary School Journal 86(2), 173-184.

Dixon, P. J. (1999). A case study of teachers using innovative curriculum materials (Doctoral dissertation). Retrieved from ProQuest. (Document Number: 730206321)

Dillman, D. A. (2000). Mail and internet surveys: The tailored design method. New York: Wiley.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Enochs, L. G., & Riggs, I. M. (1990). Further development of an elementary science teaching efficacy belief instrument: A preservice elementary scale. School Science and Mathematics, 90(8), 694-706. doi: 10.1111/j.1949-8594.1990.tb12048.x

Garet, M. S., Porter, A. C., Desimone, L., Birman, B. F., & Yoon, K. S. (2001). What makes professional development effective? Results from a national sample of teachers. American Educational Research Journal, 38(4), 915-945. doi:10.3102/00028312038004915

Gibson, S., & Dembo, M. H. (1984). Teacher efficacy: A construct validation. Journal of Educational Psychology, 76(4), 569-582. doi:10.1037/0022-0663.76.4.569

Gill, B. E. (2009). Incorporating science, technology, engineering and mathematics (S.T.E.M.) into the preservice teachers’ teaching agricultural mechanics curriculum. Poster presented at the Annual American Association of Agricultural Education Conference, Louisville, KY. Abstract retrieved from http://www.aaaeonline.org/uploads/allconferences/AAAE_conf_2009/Posters/Incorporating%20Science.pdf

Hamilton, R. L., & Swortzel, K. A. (2007). Assessing Mississippi AEST teachers’ capacity for teaching science integrated process skills. Journal of Southern Agricultural Education Research 57(1).

Hill, L., & Betz, D. (2005). Revisiting the Retrospective Pretest. American Journal of Evaluation, 26(4), 501-517.

Hipp, K. A., & Huffman, J. B. (2000). How leadership is shared and visions emerge in the creation of learning communities. Paper presented at the 81st Annual meeting of the American Educational Research Association, New Orleans, LA.

Kent, A. M. (2004). Improving teacher quality through professional development. Education, 124(3), 427-435.

Myers, B. E., Thoron, A. C., & Thompson, G. W. (2009). Perceptions of the national agriscience teacher ambassador academy toward integrating science into school-based agricultural education curriculum. Journal of Agricultural Education, 50(4), 120-133. doi: 10.5032/jae.2009.04120

Nanus, B. (1992). Visionary leadership: Creating a compelling sense of directions for your organization. San Francisco: Jossey-Bass

National Council of Teachers of English. (1996). Standards for the English language arts. Newark, DL: International Reading Association.

National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston, VA: Author.

National Research Council. (1996). National science education standards. Washington, DC: National Academy Press.

Neuman, S. B., & Cunningham, L. (2008). The impact of professional development and coaching on early language and literacy instructional practices. American Educational Research Journal, 46(2), 532-566. doi: 10.3102/0002831208328088

Nwanne, A. I. (1987). The perceptions of public school principals in the state of Texas toward selected United States Supreme Court decisions concerning desegregation issues. (ERIC Document Reproduction Service: ED301921)

Pajares, F. (1997). Current directions in self-efficacy research. In M. Maehr & P. R. Pintrich (Eds.). Advances in motivation and achievement. Volume 10, (pp. 1-49). Greenwich, CT: JAI Press.

Pajares, F. (2002). Self-efficacy beliefs in academic contexts: An outline. Retrieved from: http://des.emory.edu/mfp/efftalk.html

Posnanski, T. J. (2010). Developing understanding of the nature of science within a professional development program for inservice elementary teachers: Project nature of elementary science teaching. Journal of Science Teacher Education, 21(5), 589-621. doi: 10.1007/s10972-009-9145-8

Riggs, I., & Enochs, L. (1989, March). Toward the development of an elementary teacher’s science teaching efficacy belief instrument. Paper presented at the annual meeting of the National Association for Research in Science Teaching, San Francisco. (ERIC Document Reproduction Service No. ED 308 068)

Roberts, T. G., Harlin, J. F., & Ricketts, J. C. (2006). A longitudinal examination of teaching efficacy of agricultural science student teachers. Journal of Agricultural Education, 47(2), 81-92. doi:10.5032/jae.2009.04120

Rogers, G. E. (2007). The perceptions of Indiana high school principals related to project lead the way. Journal of Industrial Teacher Education, 44(1), 49-65.

Scales, J., Terry, R., & Torres, R. M. (2009). Are teachers ready to integrate science concepts into secondary agriculture programs? Journal of Agricultural Education, 50(2), 100-111.doi:10.5032/jae.2009.02100

Sullivan, B. (1999, August). Professional development: The linchpin of teacher quality. ASCD Infobrief. Retrieved from http://www.ascd.org/readingroom/infobrief/9908.html

Team AGED (2007). Unmistakable potential: 2005-2006 Annual report on agricultural education. Retrieved from http://aaaeonline.org/files/07.annualreportaged.pdf

Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783-805. doi:10.1016/S0742-051X(01)00036-1

Warnick, B. K., & Thompson, G. W. (2007). Barriers, support, and collaboration: A comparison of science and agriculture teachers’ perceptions regarding integration of science into the agricultural education curriculum. Journal of Agricultural Education, 48(1), 75-85.doi:10.5032/jae.2007.01075

Wilson, E. B., & Curry, K. W. (2011). Outcomes of integrated agriscience processes: A synthesis of research. Journal of Agricultural Education, 52(3), 136-147. doi: 10.5032/jae.2011.03136

Wolf, K. J., Foster, D. D., & Birkenholz, R. J. (2010). The relationship between teacher self-efficacy and the professional development experiences of agricultural education teacher candidates. Journal of Agricultural Education, 51(4), 38-48. doi: 10.5032/jae.2010.04038

Woolfolk, A. (2007). Educational psychology: Instructor’s copy. Boston, MA: Allyn andBacon.

Woolfolk-Hoy, A. E., & Hoy, W. K. (2009). Instructional leadership: A research-basedguide to learning in schools. Boston, MA: Allyn and Bacon.

CASE’s Impacts on Students

Jonathan J. Velez, Oregon State UniversityMisty D. Lambert, Oregon State University

Kristopher M. Elliott, Oregon State University

Abstract

The purpose of this qualitative study was to examine teachers’ thoughts relating to the impact of implementing the Curriculum for Agricultural Science Education (CASE) on students enrolled in the course. Grounded in the Social Cognitive Learning Theory (Bandura, 1986), the researchers employed a phenomenological approach to examine the perceived impacts of the new curriculum on students. Five practicing teachers, who were currently instructing 353 students in Introduction to Agriculture, Food, and Natural Resources (AFNR), and/or Principles of Agricultural Science- both Animal and Plant, were the participants. Data was collected through weekly reflections, semi-structured individual interviews, and a focus group. Results revealed themes relating to the curriculum meeting the needs of students differently, the routine nature of the curriculum, the high reading requirements of the curriculum and the delicate balance between curriculum content and the greenhouse/shop aspects of the agriculture program. The participants recommended modifying the pacing and reading requirements of the curriculum to meet the needs of individual students, as well as adjusting the curriculum to maintain other aspects of the agriculture program. Conclusions are discussed and recommendations are made for future research.

Introduction

In 1983, the National Commission on Excellence in Education stated that “Our nation is at risk. Our once unchallenged preeminence in commerce, industry, science, and technological innovation is being overtaken by competitors throughout the world” (p. 112). The commission was not alone in calling attention to the need for reform. Within agricultural education the calls were similar. Beginning with the publication of Understanding Agriculture: New Directions for Education, the National Research Council (1988) recognized the need and called for the integration of science standards into the agricultural classroom. Since 1988, reform efforts around agricultural education have focused on the integration of Science, Technology, Engineering, and Math (STEM) into the agricultural education program.

With the United States lagging behind other nations on international assessments, there have also been several national efforts to increase scientific literacy for all students and focus on STEM to increase the field of qualified students in the pipeline (Achieve, 2010). In fact, a new framework is being developed by the National Research Council (NRC) to create and align the next generation science standards, arguing that science standards as a whole need to be revitalized and standardized nation-wide.

More recent reform efforts, which have influenced agricultural education, include The School to Work Opportunities Act, signed by President Bill Clinton on May 14, 1994. The act directly addressed the need to prepare and recruit more of our nation’s youth into the workforce,

providing the states with funding and some discretion over how they would set up their program (Recesso, 1999). More recently, the No Child Left Behind Act put more pressure on Career and Technical Education in general as schools began to face more high stakes assessments of the academic core (Martin, Fritzsche, & Ball, 2006).

Heading the calls for reform, the National Council for Agricultural Education established eight initiatives to facilitate the development of quality Career and Technical Education programs. The third initiative, which called for a sequence of courses to enhance the delivery model of Agricultural Education, eventually led to the Curriculum for Agricultural Science Education (CASE, 2011). CASE, as it is commonly referred to, approaches learning in a broad sense. In addition to building on some of the sound principles of Project Lead The Way and integration of STEM, CASE also requires 80 hours of professional development for each CASE course a teacher wants to offer in his or her program (CASE, 2011). CASE provides rigor in agriculture curriculum through the alignment of national agriculture, science, math, and English language arts standards, while delivering curriculum “utilizing activity-, project-, and problem-based instructional strategies” (CASE, 2011, p. 1). CASE has all the components necessary to produce a well-educated, highly skilled workforce for current and future careers in food, agriculture, and natural resources system (CASE, 2011).

As with all new changes, the Curriculum for Agricultural Sciences Education has both proponents and antagonists. Some states already have standardized curriculum and do not recognize a need for the curriculum. Still other states have no statewide curriculum and welcome the opportunity to adopt a standardized curriculum. Furthermore, the CASE curriculum focuses heavily on science integration, the subject of which stirs the emotions of traditionalists and raises questions concerning the level to which agricultural educators should incorporate more academic notions of science – specifically, the incorporation of science concepts that address core science standards in an agriculture classroom. Despite the myriad of opinions concerning science integration and the CASE curriculum, to date, there is limited published research which actually examines the impacts of the CASE curriculum. This present study focuses on the curricular impact of CASE on students and seeks to add information to the profession which will allow practicing teachers and teacher educators to make informed decisions concerning the potential adoption and implementation of CASE.

The 2011-2015 National Research Agenda calls for research to examine the “design, development, and assessment of the meaningful learning environments which produce positive learner outcomes” (Doerfert, 2011, p. 9). Specifically, priority four seeks research which can promote “meaningful, engaged learning in all environments” (p. 21), and priority five explains “Agriculture education has the obligation to show that its curriculum can be used to meet the academic challenges of today’s school system while preparing students for a career in the agriculture industry” (p. 26). With many agriculture programs implementing CASE the current study seeks to add to the knowledge base regarding this relatively new curriculum and examine teacher perceptions of its impact on students.

Theoretical Foundation

Given the nature of this research, and the many aspects that work collectively to impact student experiences (curriculum design, teacher differences, contextual factors, student characteristics, etc.) the Social Cognitive Theory (SCT) was used to provide a theoretical foundation for this study (Bandura, 1986). The SCT grew out of Albert Bandura’s discomfort with the prevailing Psychodynamic, Trait, and Behaviorist theories. Bandura felt that these theories did not take into account the role of cognitive influences in human development. Bandura believed that, “A theory that denies that thoughts can regulate actions does not lend itself readily to the explanation of complex human behavior (1986, p. 15). Instead, Bandura believed that the locus of agency was interactive and reciprocal in nature. He termed this concept reciprocal determinism and identified three major factors which shape the learning of others, namely: a) personal factors in the form of cognition, affect, and biological events; b) behavior; and c) environmental influences create a dynamic interplay which alters the rate and products of learning. These three determinants are reciprocal in nature and exert influences on each other which further shape the learning of an individual (Pajares, 2002).

An understanding of the importance and nature of the relationship between these three factors served to guide the researchers’ broad examination of the student impacts of the CASE curriculum. Personal factors highlight what the students bring to the learning environment. These personal factors can include the cognitive ability of the student and the affective feelings that students have towards the course, teacher or peers. Teachers have the ability to change student behavior by promoting academic study skills, self-regulated learning and self-directed learning (Pajares, 2002). Lastly, teachers can recognize and alter the learning environment to identify classroom structures that limit student success while focusing on environmental aspects that enhance student learning (Pajares, 2002). Bandura (1997) recognized the ability of situational factors to influence learning and stated, “A host of personal, social, and situational factors affect how direct and socially mediated experiences are cognitively interpreted” (p. 19).

The curriculum itself, and the students’ lived experiences with the curriculum, may relate to all three areas of the SCT. The researchers speculated that personal factors (cognitive, affective, and biological events) might be impacted by the CASE experience as students would be challenged cognitively by the curriculum and affectively stimulated by the focus on student-centered learning. Similarly, the curriculum seeks to alter student behavior through incorporating “activity-, project-, and problem-based instructional strategies” (CASE, 2011, p.1). The curriculum also claims to focus broadly on experiential education which could also be perceived to impact both personal and behavioral factors.

In addition to the personal and behavioral factors, the CASE curriculum also seeks to alter the class environment which in turn may promote a change in students; both through the curriculum design and pedagogical training of participating teachers. According to CASE, “The philosophy behind a CASE lesson is to empower the student by providing students an active role in their learning rather than learning being a product of teacher led instruction” (CASE, 2011, p.4). CASE lessons are focused around student-directed learning and inquiry-based instruction which has the capability to alter the student learning environment.

The SCT and the corresponding personal, behavioral, and environmental factors all shape the way students interact with their learning environment. The CASE curriculum has the potential to impact all three aspects and thus the SCT served as the theory which guided the researchers in the exploration of the teachers’ perceptions of CASE’s impact on students.

Purpose and Objectives

Scant research exists concerning the impacts of the new CASE curriculum on students. Therefore, the objective of this study was to determine the perceived impact of CASE on students in the classroom from the teachers’ point of view.

Methods and Procedures

Qualitative methods were chosen to investigate this problem because these methods allow the researcher to understand how people make sense of their world (Merriam, 2009). This type of research is more concerned with meaning than frequency (Van Manen, 1979). This phenomenological study fits Creswell’s (1998) definition of describing “the meaning of the lived experiences for several individuals about a concept or the phenomenon” (Creswell, 1998, p. 51). Furthermore, it should be noted that this study was part of a larger comprehensive study on CASE in Oregon.

Participants

Criterion-based selection techniques involve determining participants based upon the goals of the study (Creswell, 1998). The participants were selected because they each met the selection criteria as teachers implementing CASE for the first year. They were all teachers located within the Educational Consortium funding the research. Qualitative researchers make use of non-probabilistic sampling procedures to focus the study from its inception, identifying cases demonstrating the specific characteristics of interest (Patton, 2002). Permission to conduct this study was granted through the participants’ signed consent and approval by the Institutional Review Board. Pseudonyms have been used. It is important to note that Oregon is connected to CASE in a number of ways that other states may not be. Because of the tight knit Oregon Agricultural Education community, there are teachers in this study who have done early field experiences with CASE authors or had them as professors while at Oregon University. One of the teachers in the study is a former teaching partner of a CASE curriculum developer.

The size of qualitative studies is usually quite small, averaging between one and twenty participants (Creswell, 1998). Using criterion sampling, five teachers were selected as the focus of the study in order to fully explore the information-rich findings.

Doug teaches in a suburban school with a multi-teacher agriculture department. He has taught for 12 years. One of his former teaching partners is a curriculum developer for CASE. Doug

attended CASE institutes in the Principles of Agricultural Science – Plant and the Principles of Agricultural Science – Animal. He holds a masters degree.

Annie teaches with Doug. She has taught for 3 years. She attended the CASE Institute to certify in the Introduction to Agriculture, Food and Natural Resources. She holds a masters degree.

Jane teaches at a large, urban school. She works in a single teacher department and has taught for 8 years. She is certified in Principles of Agricultural Science – Plant. She was traditionally certified with a bachelor’s degree.

Heather teaches in a small, rural school. She works in a single teacher department and has taught for 4 years. She is certified to teach Principles of Agricultural Science – Plant. She holds a masters degree.

Claire is in a multi-teacher department in a suburban area. She has taught for 5 years. She is certified in three areas of CASE. At the completion of the study, she had been selected to serve as a lead teacher for CASE trainings. She holds a masters degree.

Procedures

Data were collected through two semi-structured interviews, a focus group and weekly journals. Questions were planned ahead and aimed to capture the participants’ experiences with the CASE curriculum as well as how it was impacting their students and their program. Specific questions were also asked to understand how the teacher was being impacted. Both interviews lasted approximately 30 minutes per teacher while the focus group lasted just over an hour. Weekly journal prompts were sent every Thursday morning with teachers emailing their responses.

Bracketing

Bracketing the experiences and biases of the researchers which could have potentially influenced the interpretation of the results helped ensure the objectivity and confirmability. The researchers in this study are former high school teachers and are all presently involved in teacher education. One researcher taught in Oregon, one in North Carolina, while a third taught in California. These experiences influenced how the researchers interacted with and received responses from the agriculture teachers, but every attempt was made to minimize this influence by triangulating data and being aware of these possible influences.

Data Analysis

The coding process began with a review and re-read of all transcripts and journal entries. The next step was an attempt at open-coding where each transcript was reviewed and highlighted to show all significant comments and for possible themes or connections to the goals of the

study. Those themes were compiled and analyzed for overlapping information. The researchers performed the coding individually, and then met as a group to confirm their individual codes.

Trustworthiness

Qualitative researchers use measures of validation formed from the credibility, transferability, dependability, and confirmability achieved through the methods (Lincoln & Guba, 1985). Credibility relates to the level of confidence in the researcher, design, and findings, to accurately represent and interpret the data (Ary, Jacobs, & Sorensen, 2010). Credibility of the data was established through the use of reference materials, peer debriefing, and member checks. First, interviews were audio recorded, and transcribed word for word. According to Kvale (1996), transcripts are translations of the lived interview experience into the text format and are interpreted differently as a result. Therefore, transcripts were submitted to participants to allow them to check for the accuracy of statements. Throughout the data collection, individual coding, and group coding process, the lead researcher consulted an outside peer in order to debrief the process as well, and further ensure through an outside perspective that the results could hold true (or be considered credible). To establish transferability, participants were purposively selected for the study based upon their level of experience with the phenomenon. Thick descriptions were also utilized to further support the transferability of the results. Finally, to ensure the dependability and confirmability of the results, the raw interview protocol, records of the audio transcripts, raw individual and group codes, and researcher reflections have been maintained, so that future researchers could feasibly conduct the study with other participants.

Limitations of the study

Qualitative research, by purpose and design, focuses on a smaller number of participants in greater depth. While potentially transferable to other settings, the findings from this study are limited to the context of the five teachers in Oregon who participated. Qualitative research is not intended to be generalized, and the findings should not be interpreted beyond the scope of the participants in this study.

Results

The following themes emerged concerning how the teachers saw CASE impacting their students: CASE appeared to serve students of different levels differently. CASE appeared to create routine, pattern, consistency, organization, structure and rhythm

in the classroom. CASE emphasized reading and some students struggled. Balancing CASE with the greenhouse and/or shop was a challenge for both students and

teachers.

CASE appeared to serve students of different levels differently.

Doug thought “because this curriculum is so hands on and so activity based, kids of all levels can find success.” While Jane recognized that “there’s no curriculum that’s going to be

perfect for every program,” she did recognize some differences in how the students in their classrooms were served saying “It definitely works for the upper level kids that are kind of the general good kids and good students that do as they are told and follow directions.” Heather noted that “obviously, my really motivated students, the ones that are naturally going to get the assignment done and do it thoroughly, they are ones that do the labs thoroughly.”

There were some groups that appeared to perhaps struggle with CASE. Heather noted that while “the upperclassmen do well with the self directed learning” the younger students struggled a little more with the independence, adding “my class is predominantly sophomores who are in the process of learning personal management and self-guided direction.” Annie talked about knowing that CASE is designed to just turn the students loose, but “I can’t do that, not with the freshmen….I just can’t let them go and do it. So … I hold their hand through every single process.” Some kids who were used to a more traditional classroom setting may have struggled with the CASE model. Doug noted the students who were struggling were:

high end kids that were high end because they were willing to do it over and over and over again or do extra credit or whatever it took to get enough points to get the A, not necessarily learn what it took to get the A.

There was some disagreement about how CASE serves students with special needs. Jane noted “it doesn’t work very well for kids that have attention issues. It doesn’t work with kids with low IEP and reading issues, writing issues” while Claire said “I think that it is laid out in a way for my special needs kids and it’s easy for me to get it to the resource room …that makes it easier for the special needs kids,” but noted that her challenge was in finding a way to make it “more content rich for those higher” level kids because CASE seems to be “trying to find a middle ground.” Claire indicated this meant that, in the end, “unfortunately, for some it is over their heads and for some it is too low” but Jane indicated “I think that you really got to listen to the clientele of your kids.”

CASE appeared to create routine, pattern, consistency, organization, structure and rhythm in the classroom.

Specific words were repeated time and again about CASE and those included routine, pattern, consistency, organization, structure, rhythm. Early in the year, Annie said “I like the pattern that the students are in, the routine that we have every day. I do feel like that works.” Claire said “We are for the most part, into the routine” and Annie indicated she appreciated “the organization and structure it provides to the classroom.”

They also felt like students had settled into this routine with Claire saying “students, I think for the most part, are enjoying it, the structure of it” adding that “the biggest impact has been classroom consistency. The kids always seem to know what to expect as far as classroom procedures.” She thought it was good for students “knowing that it will look the same, knowing what to expect, and how to make it up if they are gone.” She saw one of the major benefits of CASE was that “it helps keep kids on track.”

During a mid-year interview, Doug indicated that “the students are starting to get into the rhythm a little better and learning what the process is.” Annie indicated that students “should

know after the first three or four weeks exactly how those days are going to go.” Claire also indicated “you know the kids know what to expect and when they see it, then they know the format, and while some kids will complain about that, the majority of kids will appreciate that consistency.”

Perhaps some students saw this consistency as just worksheets. Annie pointed out “a couple students also commented that the curriculum is like having to complete worksheets every day, and they were not happy about it.” Annie said she has reservations each time she gives the students another packet “I kind of hesitate every time… ‘Here’s another one. Don’t miss those conclusion questions’.” There was an issue with students being enrolled in multiple CASE courses and the similarity of some of the introductory sections with Claire saying “it seemed like a lot of paperwork and got a little boring. It was also the same for Animal…and Plant Science. This posed a problem since I have several kids who are enrolled in both of those classes.” Heather said:

The CASE setting was all the work packets, so some of them were really long. It just makes everything seem like a lot of work, so there is a little bit more of that ‘another lab,’ but once I get into the lab, it is super easy. It’s just to get the immediate first impression of kids, like, ‘oh no, there is a packet sitting out. We’re doing another lab today’.

CASE emphasized reading and some students struggled.

The CASE teachers talked about the trouble they experienced with the reading required by CASE. These issues ranged from lower level students who struggled with their reading level to average students who were simply not used to being required to read and follow directions.The students were struggling to adjust to being handed materials and having to read and figure them out with Annie stating “the students are also having a tough time reading the directions on the activity sheets.” Jane stated “they are really struggling with having to read it because they have not had to do that.” Jane added “I kinda stand back and they are like, ‘what do I do’ and I’m like ‘well, you have to read it’.” Annie said her students were “not used to reading the directions, I guess. We will be working on that.” In fact, when the teachers were asked to identify which students were struggling the most with CASE, Annie stated “the kids in our program that just won’t take the time to read are the kids that are suffering.”

Jane had a number of second language students in class. She indicated she would be trying to increase their reading requirements over the course of the year, stating “I read it with them now, but then eventually I will let them do it.” Jane stated that right now, she was reading it for them because “you can’t just expect them because they are going to shut down and they don’t do it.” She was excited about using the program with second language students because “this is definitely going to help them in the long run because it is more technical reading.” She had other students who struggled with reading because the reading was “high level for the kids that I have. Most of the kids that I have didn’t pass their science classes and so a lot of them are on an IEP… so the reading is really difficult for them.”

Annie indicated that she thought “they perceive that as busy work because my seniors are like ‘it is just busy work…’, I don’t see this with a lot of kids, but some of my older kids, they

perceive it that way.” Heather went so far as to say “my kids are enjoying my non-CASE classes more.” She went on to say that students could see a difference between her CASE plant science course and the more traditional ag class because CASE is “much more rigorous, detailed, and work-driven as to where my traditional classes are strongly project based and are evaluated through authentic forms of assessment rather than thick lab packets.” This meant that at the semester break a lot of her students “shifted to the different classes, even though they love the program, a lot of them are like, ‘well, yeah I like the other class because I know we don’t do all of these labs.’” By the end of the year, Claire indicated “overall, the kids seem to know what to expect and have done a surprisingly good job of reading directions, working together, and completing activities.”

Balancing CASE with the greenhouse and/or shop was a challenge for both students and teachers.

Of the teachers implementing the plant science curriculum, balancing CASE with the greenhouse was a challenge. Claire was concerned about fitting in the greenhouse time during the spring stating “we have big greenhouses and it takes a lot of time to manage those, so staying on track remotely with the CASE curriculum second semester and managing the greenhouse which I’m sure will be a challenge.” Jane observed that “the kids have gotten a little frustrated because they want to be in the greenhouse more.” Jane was struggling to find a balance. She indicated “we still need to have all of that greenhouse stuff in there and …this is so filled that you are going to have to take things out to be able to get those everyday kinds of things in.”

Doug and Annie’s program dipped heavily into their program’s budget to implement CASE. Doug stated, “our greenhouse and shop have to be self sufficient this year as opposed to buying consumables for the shop, I needed to buy consumables for in here” referring to the classroom. He wasn’t too worried and thought that it was a temporary issue stating “I pretty much had to use the shop money to buy the big ticket items for in here … next year it will be back on course.”

Claire remarked that they wished “the plant science class included more greenhouse based APP’s [activities, projects and problems] in the first part.” Finding that balance for Claire was a challenge because they “have huge greenhouses and a community that expects plant sales, and people who paid a lot of money for those greenhouses so those things have to happen.” However, Claire also saw CASE as helping her to keep a more useful balance stating that “using CASE has kept my lessons more meaningful. For example, instead of spending two weeks making wreaths and centerpieces we spent two days.” Rather than continue to try and work the greenhouse and production items into CASE, Claire and her teaching partner were considering creating new classes and were in the process of “brainstorming ideas for different production, greenhouse production type classes in the future so I can focus more on the CASE stuff.”

Heather struggled with integrating the greenhouse as well. She indicated that “next year I’m going to ask and recommend that it would be only a semester long class as plant science, and then the second semester be greenhouse management.” Heather was doing this for similar reasons as Claire. She had “been sticking to the CASE curriculum” and thought that while the

class was “wonderful and there are lots of projects and I love the curriculum, but I didn’t get any seeds planted. I had to go buy wholesale vegetables and like little seedling plugs.”

Conclusions Implications and Recommendations

In addition to overall programmatic impacts, the teachers did provide several themes focusing on the ability to serve students differently, the routine nature of the curriculum, the high reading requirement and the delicate balance between the classroom and the other program elements. The CASE curriculum, for the teachers in this study, appeared to serve different aged students at different levels. While the teachers acknowledged the many benefits of the curriculum, they recognized that the CASE curriculum worked better for students who were more “self-directed” and who were mature enough to monitor their own pacing. One teacher expressed concern that the effectiveness of CASE was limited with students with attention issues and IEP’s in reading and writing. In addition, two of the teachers struggled with implementing the curriculum with freshmen.

According to the Social Cognitive Theory (SCT), personal factors relating to cognitive and affective state will impact the student learning. Prior research has indicated that older students tend to be more cognitively developed and have an increased mental ability (Rushton & Ankney, 1996; Crone & Ridderinkhof, 2011). Moreover, as children get older, their ability to efficiently process information increases dramatically with regard to working memory (Case, 1992). With this in mind, it is not surprising that younger students struggled with the self-directed learning aspects of the CASE curriculum. Students who struggle to pay attention may be evidence of the affective element to learning supported by the SCT.

The researchers reiterate one of the teacher recommendations that individuals who implement the CASE curriculum must adjust the curriculum according to the “clientele of your students.” Further research should explore how the CASE curriculum meets the needs of students with Individual Education Programs. One teacher in this study indicated that the curriculum did not consistently meet the needs of her IEP students and, in contrast, another teacher indicated that the structure, clarity, and application of the curriculum worked very well with his IEP students. The contrasting results, in this key area, warrant further investigation.

The teachers recognized that the CASE curriculum had many positive additions to the classroom and learning environment, yet, they cautioned that the CASE curriculum serves students differently and the material should be adjusted to meet the diverse educational needs of students. In particular, they emphasized the pacing of the curriculum and the amount of reading as two potential areas for instructor modifications. Two of the teachers addressed this concern by reading the material out loud with the students and by placing students in groups specifically structured to blend levels of reading ability. The SCT supports the importance of the learning environment and the results of this research also indicate that the environment created and supported by the teachers is critical to supporting student learning.

Further research should examine how the CASE curriculum is best integrated in classrooms with a greater percentage of students with lower reading abilities. The researchers

recommend that teachers consider the student characteristics at their individual school and take student reading levels into consideration prior to attending the CASE institute and prior to determining the pacing of the curriculum. According to the SCT, the cognitive abilities of students will have an influence on both the class environment and student behavior. Therefore, teachers need to pay close attention to the cognitive levels of their students and adjust expectations in a manner which will continue to promote cognitive engagement. One teacher, in specific, who had students with lower reading abilities and found that a block (90 minute) period was inadequate for presenting two CASE lessons. Instead, she modified the curriculum so that she covered one lesson and the additional time was spent preparing for the next lesson, defining the key terms, reading, and completing any homework related to the lesson. At this pacing, the researchers were not surprised that this participant reported only completing 45% of the year-long CASE curriculum.

According to the participants in this study, the CASE curriculum promoted structure and rhythm in the classroom. The teachers were able to provide a routine for their students, thus creating behavior patterns which promoted student focus. The SCT indicates that behavioral patterns may aid students in developing academic self-regulation and self-directed learning. The CASE curriculum purports to “. . . empower the student by providing students an active role in their learning rather than learning being a product of teacher led instruction” (CASE, 2011, p. 4). This empowerment, coupled with the routine structure provided by the CASE curriculum may promote more self-regulated learning. Future research should specifically examine student self-regulation and self-directed learning prior to, during, and following involvement with the CASE curriculum. In a broader context, future research should also examine the relationship between structure and routine, and student engagement. Is the structure itself in any way influencing the engagement, retention or learning of the students? The researchers recommend that agriculture teachers recognize the routine nature of the CASE curriculum and, depending on the needs of their particular students and school, adjust accordingly.

Fully incorporating CASE into a program with greenhouse and shop expectations was difficult. The teachers indicated that the students struggled with the number of labs and the older students (ones who had taken agricultural classes prior to the implementation of CASE) lamented the lack of hands-on time in the greenhouse. One teacher commented that her students do not view CASE as “hands-on” in a similar way as they do a traditional agriculture class structure. Her students actually did not identify the CASE labs as hands-on; rather, they identified them as “science labs.” All of the teachers in this study expressed some frustration between the desire to teach CASE as prescribed by the curriculum and the need to maintain the productivity of the greenhouses and shop. Since this was the first year of implementation, further research should explore the extent to which the teachers address this challenge and find balance in the first few years of implementation. The researchers recommend that teachers, who are preparing to implement CASE, analyze the curriculum and determine how to best integrate the many learning opportunities associated with a total agriculture program. The teachers in this study encouraged other teachers to modify and adjust the curriculum to meet individual program needs. As a result, the teachers in this study reported completing between 45-75% of the CASE curriculum during their first year.

A limitation of this study is that it only captures the voice of the teachers and their reflections of the impact on students. While this is vital to exploring the CASE curriculum, it fails to give direct voice to the students. Further research should examine the CASE curriculum through the lived experiences of the students. Qualitative studies including individual interviews, student reflective writings, and focus groups would add vibrancy and color to the portrait which is the CASE curriculum.

References

Achieve. (2010). Taking the lead in science education: Forging next-generation science standards. International science benchmarking report. Achieve, Inc.

Ary, D., Jacobs, L. C., & Sorensen, C. (2010). Introduction to research in education (8th ed.). Belmont, CA: Thomson Wadsworth.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Case, R. (1992). The mind’s staircase: Exploring the conceptual underpinnings of children’s thought and knowledge. Hillsdale, NJ: Psychology Press.

Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage Publications.

Crone, E. A., & Ridderinkhof, R. K. (2011). The developing brain: From theory to neuroimaging and back. Developmental Cognitive Neuroscience, 1(2), 101-109. doi:10.1016/j.dcn.2010.12.001

Curriculum for Agricultural Science Education. (2011). Understanding the CASE model. (.pdf retrieved from http://www.case4learning.org/about-case/vision.html)

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Kvale, S. (1996). InterViews: An introduction to qualitative research interviewing. Thousand Oaks, CA: Sage.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.

Martin, M. J., Fritzsche, J. A., & Ball, A. L. (2006). A Delphi study of teachers’ and professionals’ perceptions regarding the impact of the no child left behind legislation on secondary agricultural education programs. Journal of Agricultural Education, 47(1), 101-109. doi:10.5032/jae.2006.01101

Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA: Jossey Bass.

National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. Washington, DC: US Department of Education.

Pajares (2002). Overview of social cognitive theory and of self-efficacy. Retrieved from http://www.emory.edu/EDUCATION/mfp/eff.html

Patton, M. Q. (2002). Qualitative research & evaluation methods (3rd Ed.). Thousand Oaks, CA: Sage.

Recesso, A. M. (1999). School to work opportunities act policy-An effort at backward mapping. Education Policy Analysis Archives, (7)11, 1-43.

Rushton, J. P. & Ankney, C. D., (1996). Brain size and cognitive ability: Correlations with age, sex, social class, and race. Psychonomic Bulletin & Review, 3(1), 21-36. doi:10.3758/BF03210739

VanMaanen, J. (1979). Reclaiming qualitative methods for organizational research: A preface. Administrative Science Quarterly, 24(4), 520-526.

APA Task Force on Psychology in Education. (1993). Learner-centered psychological principles: Guidelines for school redesign and reform. Washington, D.C.: American Psychological Association and Mid-Continent Regional Educational Laboratory.

Students' Perceptions of Curriculum for Agricultural Science Education (CASE) as Compared to High School Science and Non-Science Courses

Misty D. Lambert, Oregon State UniversityJonathan J. Velez, Oregon State University

Kristopher M. Elliott, Oregon State University

Abstract

The purpose of this descriptive study was to explore student feelings about their CASE courses as compared to their other science and non-science high school courses. Brain-based learning theory served as the framework for the study. The population included 258 students from four Oregon high schools. Student characteristics are also reported including the course in which they were enrolled as well as sex, grade level and GPA. Information is also reported regarding whether students were receiving science and/or college credit for their course. The number of students with an individualized education plan, the number of talented and gifted students, the students who are English language learners and the students who are active in FFA are also reported. Students responded to their level of agreement with statements comparing their CASE course to their other high school classes as well as their high school science courses. Results revealed student areas of agreement regarding the CASE courses and contrasting results reveal areas for future research and potential improvement of the curriculum.

Introduction and Review of Literature

As long ago as 1938, Dewey argued for the integration of vocational training with academics to both reinforce the principles of learning and create transferable skills for students. More recently, Career and Technical Education (CTE), as it is currently known, has faced sharp criticism that it is not rigorous enough, and tends to track lower performing students to labor rather than higher education (Saunders & Chrisman, 2011). Many believe that CTE should pursue a more rigorous academic approach, while maintaining the ability to make learning relevant for students through real world applications (Stone, Alfeld, & Pearson, 2008). In 1991, the SCANS report indicated that for students to be prepared for the jobs of tomorrow, learning must occur in an applied way using real life situations (SCANS, 1991). Connections should be made in education between the learning of knowledge and skills and the practical application of those in the “real world” (Parr, Edwards, & Leising, 2009).

In 1988, Agricultural Education leaders initiated reform efforts which called for the integration of academic standards, and an effort to evolve beyond the traditional production agriculture approach, into a curriculum that included all areas of agricultural sciences (National Research Council, 1988). More recently, this has led to increased efforts to incorporate more national and local science standards into agriculture science curriculum. In fact, numerous studies have suggested that integrating science into the agricultural education curriculum is an effective way to teach science (Chiasson & Burnett, 2001; Dyer & Osborne, 1999; Myers & Thompson, 2009; Roegge & Russell, 1990).

Along with an interest in integrating more science into agriculture curriculum, researchers have advocated for the combined integration of Science, Technology, Engineering, and Mathematics (STEM) into CTE courses. A study by Myers and Thompson (2009) showed that agriculture teachers (n = 26) agreed 100% on 11 recommendations to move Agricultural Education forward in the area of math, science, and reading integration. These recommendations included professional development, integration ideas, lesson plans being made available online, and alignment of agriscience curriculum with state and national standards in math, science, and reading (Myers & Thompson, 2009).

Not all evidence has been positive. Mahoney (2010) found that a STEM-based high school programs did not lead to a significantly more positive attitude among students than a traditional college-preparatory high school program, thus providing cause to question an isolated STEM approach. However, Foutz et al. (2011) argued that STEM should be approached with problem-based learning and supported by adequate professional development.

On a practical level, Project Lead the Way (PLTW) has approached the integration of STEM by including project based learning, rigorous academic content, and sound professional development. Currently, PLTW impacts over 400,000 high school and middle school students in all fifty states (PLTW, n.d.). A study of Indiana high school principals with PLTW programs at their schools, found that the principles noted “very positive impact on their school’s students, teachers, and overall school culture” (Rogers, 2007, p. 64).

Modeled after PLTW, the Curriculum for Agricultural Science Education (CASE) requires teachers to participate in 80 hours of professional development in order to offer the curriculum in their agriculture programs (CASE, 2011a). According to Nancy Trivette, a member of the CASE Advisory Committee, “The CASE program continues to grow and develop nationwide. Eighty-seven (87) teachers in 17 states are implementing foundational courses in plant and /or animal science…Five schools have all three CASE courses and 11 schools are implementing two courses—all this in the first full year of CASE implementation” (N. Trivette Personal E-mail communication, January 21, 2011).

With project-based and inquiry-based learning in mind, CASE was designed to be more student-centered. Student learning increases when students are allowed to go beyond memorizing facts and are allowed to construct their own knowledge, investigate alternatives, use scientific inquiry, and communicate their learning to an audience (Newmann, 1996). In a 2005 study of college science students, Laipply (2005) found that inquiry-based instruction had a positive impact on students’ attitudes regarding science. Kilinc (2007) found that inquiry based laboratory activities, similar to CASE activities, were more enjoyable among students in comparison to traditional labs, and this led to increased positivity toward biology. Collaborating with peers also allows students to learn to work together to solve scientific problems, a valuable skill when they enter the world of work (American Association for the Advancement of Science, 1989). Studies have shown that the opportunity to work with other students while learning creates more significant learning opportunities than may exist for students who work alone (Johnson & Johnson, 1981, 1989).

With inquiry and problem-based learning approaches tending to lead to more positive student engagement, these avenues to learning, infused into CASE, also tend to involve more student/group interaction. Travis and Thomas (2009) found that students working in groups were able to recall the information learned in a laboratory more readily than students working alone. Moreover, a study by Springer, Stanne and Donovan (1997) showed that collaborative learning tasks increased positive attitudes among students and led to retention in STEM programs. As many agriculture courses are electives (not required), student opinions on the implementation of CASE are important for retention and recruitment into agriculture programs.

CASE purports to implement a project, problem, and/or activity-based learning structure (CASE, 2011). Studies have also shown that students who struggle in more traditional environments thrive under these types of learning systems (Boaler, 1997; Meyer, Turner & Spencer, 1997; Rosenfeld & Rosenfeld, 1998). Research has indicated that students who learn in these types of environments are more motivated, have a better attitude towards learning and exhibit positive changes in skill development, work habits, critical thinking skills and problem solving abilities (Bartscher, Gould & Nutter, 1995; Peck, Peck, Sentz, & Zasa, 1998; Tretten & Zachariou, 1995). Student perception of a course can be influential. Teachers who rely only on lecture or other traditional methods of instruction alone are often perceived as less effective in the classroom (Brazen & Clark, 2005).

While scant research has been reported specifically linking student opinions of new curriculum to student achievement, the researchers would suggest that the larger body of research would support the concept that student opinions matter. Understandably, students who participate in and enjoy an elective class are more likely to be retained, and student recruitment and retention are concerns in many agriculture programs (Connors, 1998).

Theoretical Framework

Brain-based or brain-compatible learning theory served as the framework for this study. This theory focuses on concepts that create an opportunity to maximize attainment and retention of information (Duman, 2006). Brain-based learning is student centered learning that utilizes the whole brain and recognizes that not all students learn in the same way. It is also an active process where students are actively engaged in constructing their own knowledge in a variety of learning situations and contexts (Caine & Caine, 1994, 1997; Caine & Caine & Crowell, 1999). Brain-based researchers are interested in how student feelings impact learning as well as how learning is impacted by perception, attention, and memory (Diamond & Hopson, 1998; Goleman, 1997; Greenfield, 1996; LeDoux, 1996; Sprenger, 1999). Students who are active in a learner-centered environment may have increased attention and develop positive classroom feelings which enhance their overall learning.

Twelve basic principles serve as the essential tenets of brain-based learning (Caine & Caine, 1994, 1997; Caine, Caine, & Crowell, 1999). The tenets are: 1) The human brain is a parallel operator; 2) Learning is a physiological event and the brain is an organ which is working according to physiologic rules; 3) The brain tries to give meanings to the data that have arrived there. At the same time, the brain has a perfect curiosity and hunger for novelty, discovery, and challenge; 4) Giving meaning comes by the way of patterning. Imagining, problem solving and

critical thinking are some types of patterning; 5) Senses have an important place in patterning. The learning of the person is affected by attitude, emotions, feelings and social interaction; 6) The brain perceives the parts and the whole at the same time. While teaching a subject, the whole and the parts of the topic that interacting with each other should be given at the same time; 7) Learning includes the information which is taken from both focused direction and additional stimulations; 8) Learning consists of both intentional and inadvertent processes; 9) There are two kinds of memory: spatial and rote memorization. People have a natural spatial mind that can memorize without experiences and rehearsal; 10) Facts and abilities are learned when they are stored in spatial mind; 11) Learning increases with the activities that force the brain; 12) Each brain is unique, and teaching should be done in a way that allows the students to express their visual, auditory and emotional choices (Caine & Caine, 1994, 1997; Caine, Caine, & Crowell, 1999).

As a foundation by which to investigate CASE, this research specifically sought to focus on the third, fifth, sixth and twelfth tenets of brain-based learning. The researchers speculated that the CASE curriculum, and its hands-on claim, may allow students to express a hunger or curiosity for novelty, discovery and challenge (tenet 3). This may enhance student interest and feelings of learning in a CASE course. “CASE is based on solid instructional design that has been proven to have a positive impact on learners” (CASE, 2011b, p. 6), but CASE never claims to create a learning environment that students will enjoy (tenet 5). CASE claims to focus on conceptual understandings and employs a spiraling model of content acquisition (CASE, 2011a) thereby providing students a glimpse of the parts and the whole at the same time (tenet 6). And lastly, the CASE curriculum presents information in a visual, auditory and kinesthetic environment designed to provide students with problem-based tasks which may promote student autonomy and foster more emotionally invested choices (tenet 12).

When analyzing teaching, the term brain-based education is often used. Brain-based education focuses specifically on how brain-based learning theory is best applied to the classroom and how to enable student learning and development in the classroom (Madrazo & Motz, 2005). According to CASE (2011a), the CASE curriculum is intended to be project, problem and/or activity based and thus potentially able to stimulate the brain to retain information. Research has indicated that brain-based educational environments are created around learner-centered materials and instruction that are delivered in a fun, meaningful, and personally enriching way (Lucas, 2004). According to Dunman (2006), brain-based environments are those that allow students to engage in and actively participate in comprehensive learning experiences in a high encouragement, low threat setting. The collaborative, learner-centered approach to teaching, embodied in the CASE curriculum, may allow for low threat settings. Furthermore, the shift from teacher to student focus may allow the teacher to assume a more encouraging and supportive classroom role.

Purpose and Research Objectives

This study addresses the American Association for Agricultural Education’s National Research Agenda priority area 4: Meaningful, Engaged Learning in All Environments (Doerfert, 2011). The purpose of this study was to describe student feelings about their CASE

courses as compared to their other science and non-science high school courses. The following research objectives were addressed in the study:

1) Describe student characteristics (course, sex, grade level, IEP, TAG, ELL, FFA, Science Credit, College Credit and GPA).

2) Determine level of agreement with science comparison statements.3) Determine level of agreement with high school comparison statements.

Methods and Procedures

This study employed a descriptive research design. Descriptive studies are studies used to find out ‘what is’ (Borg & Gall, 1996). The accessible populations were students enrolled at four Oregon high schools implementing CASE during the 2010-2011 Academic year (N = 353). The frames were based on attendance rosters and monitored by the teachers who were providing access to the students during data collection.

The instrument utilized in this research was created based on student responses to an open-ended question in a previous study. Students were asked to identify how their CASE course differed from their other courses. In answer to this question, the students identified 133 ways they felt their CASE courses was different. The researchers collected these differences, analyzed them, and collapsed their responses into a Likert-type instrument. The instrument was then pilot tested with a pilot group of 20 students in a high school agriculture program in Oregon that would not be part of the study. Item reliability was examined and items rating above .50 were considered acceptable since this instrument was a first attempt at measuring students’ feelings and no critical decisions are being made based upon this data (Ary, Jacobs, & Sorensen, 2010). Acceptable coefficients of stabilities ranged from .50 to .94. There were 18 statements “compared to high school science classes” and 14 statements “compared to other high school classes” and the instrument used a six point likert-type scale, ranging from “strongly disagree” to “strongly agree.”

Instruments were mailed to teachers in March. They were given a two week window to find a school day where they could suitably administer the assessment to their students. Data collection occurred during one school day so only those students in attendance that day are included in the study. No attempts were made to collect data from students who were absent. The total number of overall responses was 258 students, or 73%. Some students only partially completed their forms. All forms were returned to the researchers in an anonymous format with a corresponding spreadsheet where teachers had collected demographic data. Because of the fact that the researchers never contacted students and only handled raw, anonymous data, the study was granted Exempted IRB approval. Frequencies, percentages, and measures of central tendency and variability were used to summarize the data. This paper is part of a larger, comprehensive study on CASE.

Findings

As reported in table 1, the majority (f = 138, 53.49%) of the students in the study were completing the Principles of Agricultural Science – Plant CASE course. There were also 74 students (28.68%) in the Introduction to Agriculture, Food and Natural Resources course and 46

students (17.83%) in the Principles of Agricultural Science – Animal course. The group was evenly split between males (f = 129, 51.60%) and females (f = 121, 48.40%). The researchers relied on the teachers to report students’ grade point averages and only 208 were reported. Of those students, the average GPA was 2.63 (SD = 0.93) with a range from 0.38 to 4.00.

There were only slightly more Juniors (f = 78; 31.20%) enrolled in CASE courses than Freshmen (f = 65, 26%), Sophomores (f = 49, 19.60%), or Seniors. (f = 58, 23.20%). The average student was not on an Individualized Education Plan (IEP) for Special Needs (f = 213, 85.20%), was not identified as Talented and Gifted (TAG) (f = 238, 95.20%), and was not an English Language Learner (ELL) (f = 220, 88.00%). Most of the students (f = 176, 70.40%) were receiving science credit, and only a few (f = 26, 10.40%) were receiving college credit.

Table 1Characteristics of students enrolled in CASE coursesCharacteristic f %Course (n = 258 )

Intro to AFNR 74 28.68Principles of Agricultural Science – Plant 138 53.49Principles of Agricultural Science – Animal 46 17.83

Sex (n = 250)Male 129 51.60Female 121 48.40

Grade level (n = 250)Freshman 65 26.00Sophomore 49 19.60Junior 78 31.20Senior 58 23.20

IEP (n = 250)Yes 37 14.80No 213 85.20

TAG (n =250)Yes 12 4.80No 238 95.20

ELL (n = 250)Yes 30 12.00No 220 88.00

FFA participant (n = 250)Yes 94 37.60No 156 62.40

Science credit (n = 250)Yes 176 70.40No 74 29.60

College credit (n =250)Yes 26 10.40No 224 89.60

Table 2 shows the students’ level of agreements with the statements comparing their CASE course to their average science course. The item mode for each of the items are shown in bold. It is important to note that the mode for all statements was at least a Slightly Agree. The two items for which the largest portion of students Strongly Agreed were “This class has less homework than an average science class” (f = 87, 36.40%) and “This class is more fun than an average science class” (f = 80, 33.33%).

The largest portion of the items received an Agree from students. These items include “This class has more activities than an average science class” (f = 101, 42.08%), “This class is easier for me than an average science class” (f = 94, 39.50), “This class lets me to express myself more than an average science class” (f = 91, 38.08), “This class requires more participation than an average science class” (f = 81, 35.00), “This class is more interesting to me than an average science class” (f = 77, 32.08%), “I make friends easier in this class than an average science class” (f = 75, 31.38%), “I am more excited to learn the material in this class than an average science class” (f = 74, 30.96%), “I value the content of this class more than an average science class” (f = 66, 27.85%), “This class takes more field trips than an average science class” (f = 61, 25.63%). There were three items with which the largest portion of students Slightly Agreed. These items were “This class is more focused on me than an average science class” (f = 88, 36.67%), this class is more relevant to me” (f = 78, 32.77), and “This class helps me to be more organized than the average science class” (f = 76, 32.07).

The students compared their CASE course to their average high school class using14 items (see table 3). The mode for each statement is shown in bold. There were no items with which a majority of students strongly agreed. There were five items with which the largest portion of students Agreed. These items were “I work with my class mates more in this class than an average high school class” (f = 97, 42.92%), “This class is easier for me than an average high school class” (f = 97, 42.36%), “This class lets me experience what I am learning more than an average high school class” (f = 87, 37.99%), “This class requires more participation than an average high school class” (f = 85, 37.12%), and “This class takes more field trips than an average high school class” (f = 64, 28.19%).

The remainder of the items had the most responses at the Slightly Agree level. Those items were “I make friends easier in this class than an average high school class” (f =84, 37.00), “I am more excited to learn the material in this class than an average high school class” (f =80, 35.09), “This class is more focused on me than an average high school class” (f =80, 34.93), “This class focuses on careers more than an average high school class” (f =79, 34.96), “This class helps me to be more organized than an average high school class” (f =77, 33.92), “This class is more interesting to me than an average high school class” (f =75, 33.04), “I value the content of this class more than an average high school class” (f =73, 32.02), “This class is more relevant to me than an average high school class” (f =69, 32.26), “This class is more important to me than an average high school class” (f =61, 26.87).

Table 2Statements and level of agreement as compared to science classes

Strongly Disagree Disagree

Slightly Disagree

Slightly Agree Agree

Strongly Agree

Item f % f % f % f % f % f %This class has less homework… 8 3.35 7 2.93 14 5.86 44 18.41 79 33.05 87 36.40This class is more fun… 6 2.50 9 3.75 16 6.67 53 22.08 76 31.67 80 33.33This class has more activities… 6 2.50 7 2.92 20 8.33 41 17.08 101 42.08 65 27.08This class is easier for me… 4 1.60 10 4.20 15 6.30 45 18.91 94 39.50 70 29.41This class lets me to express myself… 9 3.77 12 5.02 25 10.46 75 31.38 91 38.08 27 11.30This class requires more participation… 4 1.67 15 6.25 44 18.33 58 24.17 84 35.00 35 14.58This class lets me to learn at my own pace more… 5 2.09 10 4.18 35 14.64 82 34.31 82 34.31 25 10.46This class is more interesting to me… 5 2.08 19 7.92 26 10.83 58 24.17 77 32.08 55 22.92I make friends easier in this class… 6 2.51 15 6.28 32 13.39 70 29.29 75 31.38 41 17.15I am more excited to learn the material in this class… 11 4.60 15 6.28 30 12.55 66 27.62 74 30.96 43 17.99I value the content of this class more… 8 3.38 17 7.17 36 15.19 65 27.43 66 27.85 45 18.99This class takes more field trips … 32 13.45 46 19.33 23 9.66 37 15.55 61 25.63 39 16.39This class is more focused on me… 14 5.83 20 8.33 51 21.25 88 36.67 55 22.92 12 5.00This class is more relevant to me… 6 2.52 14 5.88 50 21.01 78 32.77 57 23.95 33 13.87This class helps me to be more organized… 9 3.80 22 9.28 45 18.99 76 32.07 66 27.85 19 8.02I learn more about science in this class… 17 7.14 29 12.18 43 18.07 71 29.83 59 24.79 19 7.98This class has less busywork… 11 4.66 31 13.14 37 15.68 70 29.66 62 26.27 25 10.59This class is more important to me… 11 4.60 31 12.97 36 15.06 60 25.10 59 24.69 42 17.57

Note. All items ended with “…than an average science class.” Bold text references the item mode.

Table 3Statements and level of agreement as compared to high school classes

Strongly Disagree Disagree

Slightly Disagree

Slightly Agree Agree

Strongly Agree

Item f % f % f % f % f % f %I work with my classmates more in this class… 0 0.00 10 4.42 17 7.52 51 22.57 97 42.92 51 22.57This class is easier for me… 3 1.31 10 4.37 23 10.04 61 26.64 97 42.36 35 15.28This class lets me experience what I am learning more… 3 1.31 4 1.75 27 11.79 68 29.69 87 37.99 40 17.47This class requires more participation… 0 0.00 10 4.37 38 16.59 63 27.51 85 37.12 33 14.41This class takes more field trips… 30 13.22 30 13.22 20 8.81 53 23.35 64 28.19 30 13.22I make friends easier in this class… 6 2.64 17 7.49 28 12.33 84 37.00 59 25.99 33 14.54I am more excited to learn the material in this class… 6 2.63 18 7.89 43 18.86 80 35.09 54 23.68 27 11.84This class is more focused on me… 13 5.68 19 8.30 51 22.27 80 34.93 47 20.52 19 8.30This class focuses on careers more… 4 1.77 10 4.42 32 14.16 79 34.96 68 30.09 33 14.60This class helps me to be more organized… 5 2.20 25 11.01 37 16.30 77 33.92 71 31.28 12 5.29This class is more interesting to me… 4 1.76 13 5.73 39 17.18 75 33.04 60 26.43 36 15.86I value the content of this class more… 5 2.19 15 6.58 55 24.12 73 32.02 49 21.49 31 13.60This class is more relevant to me… 1 0.44 22 9.65 52 22.81 69 32.26 59 25.88 25 10.96This class is more important to me… 10 4.41 21 9.25 59 25.99 61 26.87 47 20.70 29 12.78

Note. All items ended with “than an average high school class.” Bold text references the item mode.

Conclusions, Implications & Recommendations

The students in this study were enrolled across the three CASE courses being offered in the 2010-2011 school year. They were fairly evenly distributed across grade level and also across the male and female groups. The average student was not on an Individualized Education Plan (IEP), was not identified as Talented and Gifted, and was not an English Language Learner, although there were some students representing those groups. Most of the students were receiving science credit, but only a few were receiving college credit.

The conclusions follow the pattern of the results and are separated into categories based on comparison to the average science class and comparisons to the average high school class. In an effort to add clarity to the assessment questions, the researchers collapsed the strongly disagree, disagree, and slightly disagree columns and did the same with the slightly agree, agree, and strongly agree columns. Difference scores were calculated between the number of student responses in the two aggregate columns and those with the four strongest “agree” differences and those with the four weakest differences were examined. In other words, the questions with the greatest congruity and those with the greatest incongruity were identified.

Comparisons to Science Classes

The students were asked to compare their CASE courses to their science high school courses. Those items with the highest level of student agreement were: 1) This class has less homework; 2) This class is easier for me; 3) This class is more fun; and 4) This class has more activities. The students thought the course had less homework than their other sciences courses. The CASE curriculum is lab based and, many times, facilitated in students groups. It would make sense that much of that work is completed in class. Perhaps this is a finding that would have held true for agriculture courses even without the integration of CASE. Perhaps the reason the students thought the course was easier connects to previous research which shows that science, when embedded in a real-world context, is more accessible to students (Parr, Edwards & Leising, 2009). The fact that students perceived their CASE courses as more fun is important because of the connection to tenet 5 from the brain-based learning framework. Caine and Caine (1990) stated that “teachers must understand that students’ feelings and attitudes will be involved in learning” (p. 67). Students thought their CASE course had more activities. This connects with the claims CASE (2011a) makes about being student-centered and using activities, projects, and problems to engage students. Based on Kilinc (2007), this perception of a high level of activity may have influenced the fact that students found the course fun.

The four items that had the most disparity in student agreement were: 1) This class takes more field trips, 2) I learn more about science in this class; 3) This class is more focused on me, and 4) This class has less busywork. While CASE (2011a) claims to be a lab-heavy curriculum they never claim to integrate field trips into the curriculum so perhaps it should not be a surprise that the students didn’t think their CASE agriculture class took more field trips. Students did not think they learned more about science in their CASE course than in their other science courses. Since the other courses intend to teach science, this finding should not be a surprise either. Perhaps the students are learning just as much science in their CASE course, or since the science is embedded in a contextual way, perhaps the students are not aware how much science they are

learning. Lastly, students did not agree that the class had less busywork. In a parallel study researchers found both students and teachers working with CASE courses were somewhat overwhelmed by the paper-based packets that accompany the curriculum. The researchers recommend that CASE investigate ways to manage the student perception that the curriculum offers busywork. If students perceive the curriculum as busy work, then it is understandable that they may feel that the class does not focus on them.

Comparisons to High School Classes

In the student comparison to a typical high school class, the questions with the highest agreement numbers, in order, were: 1) I work with my class classmates more in this class; 2) This class lets me experience what I am learning more; 3) This class focuses on careers more; and, 4) This class requires more participation. The student responses indicate that compared to a typical high school classroom, they felt that involvement in a CASE course allowed for more collaboration and more hands-on learning. Questions one, two and four directly relate to student involvement and tie back in to tenet 12 of brain-based learning (Caine & Caine, 1994, 1997). Tenet 12 calls for information to be presented in a visual, auditory or kinesthetic environment while promoting student investment and autonomy. According to the level of agreement, a majority of the students agree that their involvement with the CASE curriculum is allowing them to “experience what they are learning” while promoting increased collaboration and participation. Question three connects back to tenet six. A majority of students are in agreement that the CASE course focuses more on careers than there average high school class. Tenet six focuses on allowing the students to glimpse the parts and the whole at the same time. Allowing students to participate in a course which facilitates career connectedness may provide relevance to the students by enabling them to connect current learning with long-term career goals.

The four items with the weakest differences in order of weakness are: 1) This class is more important to me; 2) This class is more focused on me; 3) This class takes more field trips; and 4) This class is more relevant to me. Students showed the most disagreement regarding the importance of the class and the focus of the class. Compared to the average high school class, students enrolled in a CASE course did not feel the class was more important, relevant or focused more on them. These results are in contrast to tenet 12 of brain-based teaching which encourages emotionally invested choices by the students. If students are emotionally invested, it could reasonably be assumed that they would likewise find the class both important and personal to them. The researchers recommend that CASE teachers and curriculum writers be aware of the need for students to find the classes both important and personal. Changes in teaching pedagogy and/or curricular modifications may allow students to recognize the importance and take a more personal interest in the course. The implication of the highly structured CASE curriculum is that it may tend to overwhelm or shadow the voice of the students. Further research should examine the personal investment of students in the course as well as the perceived content importance. Students indicated that they did not agree that the CASE curriculum allowed for more field trips than their average high school class. This student perception appears valid in that the CASE curriculum does not directly promote field trips.

Student responses came from four different high schools, and it is a sound presumption that each of these schools may have varied in the level of CASE implementation. Research has

indicated that teachers do not always have the same definition or vision for terms such as ‘student-centered learning’ or ‘facilitator’ (Pedersen & Liu, 2003). These findings reinforce the need for sound professional development in order to effectively implement a new curriculum such as CASE. Further research should examine the within school differences related to CASE implementation. Some of the student responses to the high school comparison questions are contradictory in nature. For instance, students agreed with the career focus of the class, yet showed a high frequency of disagreement with the importance of the class. Presumably if the students felt the class focused on careers perhaps they would show an increased perception of importance. The results also have the implication that perhaps the CASE curriculum focuses on careers but they may not be the intended careers of the students and thus the incongruity between careers and personal importance. Since this was merely an initial exploratory study designed to capture student perceived differences, future research should examine, in greater detail, the brain-based effectiveness of the CASE curriculum. As CASE continues to grow, researchers will be provided with the chance to purposively select schools based on the level of CASE integration and teacher, student, and school variables. This will add rigor to the research and potentially clarify some of the perplexing student perceptions in this study.

The intent of the researchers in this study was to broadly examine student perceptions of the differences between a CASE course and a typical high school or science course. The breadth of student perceptions encourages future research that controls for more extraneous variables and focus more specifically on constructs directly related to student learning. The authors recognize the generalized nature of this study, yet felt it prudent, due to the lack of research on this new curriculum, to being with a broad examination. All too often, studies jump into analyzing specific, narrow constructs without ever stepping back, assuming a birds-eye view, and asking the participants the basic question, “how does this class differ from your other classes.” Before more in-depth analysis, the authors felt the need to solicit the opinions of those impacted – namely, the students. The hope of the researchers is that this study will provide a foundation for future studies and raise the needed questions to shape and define future research.

References

American Association for the Advancement of Science, (AAAS) (1989). Science for all Americans: Project 2061. New York: Oxford University Press.

Ary, D., Jacobs, L. C., & Sorensen, C. (2010). Introduction to research in education 8th Edition. Belmont, CA: Thompson Wadsworth.

Bartscher, K., Gould, B., & Nutter, S. (1995). Increasing student motivation through project based learning. Master’s Research Project, Saint Xavier and IRI Skylight (ED 392549).

Boaler, J. (1997). Experiencing school mathematics: Teaching styles, sex and settings. Buckingham, UK: Open University Press.

Borg, W. R., & Gall, M. D. (1996). Educational research: An introduction, (6th ed.). NY: Longman.

Brazen, E. F., & Clark, C. D. (2005). Promoting interactive learning with an electronic student response system. NACTA Journal, 49(3), 11–16. Retrieved from http://hdl.handle.net/2123/199

Caine, R. N., & Caine, G. (1990). Understanding a brain-based approach to learning and teaching. Educational Leadership, 48(2), 66-70. Retrieved from http://www.ascd.org/ASCD/pdf/journals/ed_lead/el_199010_caine.pdf

Caine, G., & Caine, R. N. (1994). Making Connections: Teaching and the Human Brain. Menlo Park, CA: Addison-Wesley.

Caine, G., & Caine, R. N. (1997). Education on the Edge of Possibility. Alexandria, VA: ASCD.

Caine, G., Caine, R. N., & Crowell, S. (1999). Mindshifts: A Brain-Based Process for Restructuring Schools and Renewing Education, 2nd edition. Tucson, AZ: Zephyr Press.

Chiasson, T. C., & Burnett, M. F. (2001). The influence of enrollment in agriscience courses on the science achievement of high school students. Journal of Agricultural Education, 42(1), 61–71. doi:10.5032/jae.2001.01061

Connors, J. J. (1998). A regional Delphi study of the perceptions of NVATA, NASAE, and AAAE members on critical issues facing secondary agricultural education programs. Journal of Agricultural Education, 39(1), 37-47. doi:10.5032/jae.1998.01037

Curriculum for Agricultural Science Education (CASE). (2011a). Understanding the CASE model. Retrieved from http://www.case4learning.org/about-case/vision.html

Curriculum for Agricultural Science Education (2011b). CASE lesson development philosophy. Retrieved from http://www.case4learning.org/about-case/vision.html

Dewey, J. (1938). Experience and education. New York, NY: Collier Books.

Diamond, M., & Hopson, J. (1998). Magic trees of the mind: How to nurture your child's intelligence, creativity, and healthy emotions from birth through adolescence. New York: Dutton.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Duman, B. (2006). The effect of brain-based instruction to improve on students’ academic achievement in social studies instruction. 9th International Conference on Engineering Education, San Juan, Puerto Rico.

Dyer, J. E., & Osborne, E. W. (1999). The influence of science applications in agriculture courses on attitudes of Illinois guidance counselors at model student-teaching centers. Journal of Agricultural Education, 40(4), 57–66. doi:10.5032/jae.1999.04057

Foutz, T., Navarro, M., Hill, R. B., Thompson, S. A., Miller, K., & Riddleberger, D. (2011). Using the discipline of agricultural engineering to integrate math and science. Journal of STEM Education: Innovations and Research, 12(1-2), 24-32. Retrieved from http://ojs.jstem.org/index.php?journal=JSTEM&page=article&op=view&path[]=1577&path[]=1347

Goleman, D. (1997). Emotional Intelligence. New York: Bantam Books.

Greenfield, S. (1996). The Human Mind Explained: An owner’s guide to the mysteries of the mind. New York: Henry Holt & Company.

Johnson, D. W., & Johnson, R. T. (1981). Effects of cooperative and individualistic learning experiences on interethnic interaction. Journal of Educational Psychology, 120, 77-82. doi:10.1037/0022-0663.73.3.444

Johnson, D. & Johnson, R. (1989). Cooperation and competition: Theory and research. Edina, MN: Interaction Book Company.

Kilinc, A. (2007). The opinions of Turkish high school pupils on inquiry based laboratory activities. The Turkish Online Journal of Educational Technology, (6)4, 56-71. Retrieved from http://citeseerx.ist.psu.edu

Laipply, R. S. (2005). A case study of self-efficacy and attitudes toward science in an inquiry-based biology laboratory (Doctoral Dissertation). Available from ProQuest Dissertations and Theses database. (UMI NO. 775161821)

LeDoux, J. (1996). The Emotional Brain: The Mysterious Underpinnings of Emotional Life. New York: Simon and Schuster.

Lucas, R. W. (2004). The creative training idea book: Inspired tips and techniques for engaging and effective learning. New York: AMACOM.

Madrazo, G. M., & Motz, L. L. (2005). Brain Research: Implications to Diverse Learners. Science Educator, 14(1), 56-60. Retrieved from http://eric.ed.gov/PDFS/EJ740959.pdf

Mahoney, M.P. (2010). Students’ attitudes towards STEM: Development of an instrument for high school STEM-based programs. Journal of Technology Studies, 36(1), 24-34. Retrieved from http://scholar.lib.vt.edu/ejournals/JOTS/v36/v36n1/pdf/mahoney.pdf

Meyer, D. K., Turner, J. C., & Spencer, C. A. (1997). Challenge in a mathematics classroom: Students’ motivation and strategies in project-based learning. The Elementary School Journal, 97(5), 501-521.

Myers, B. E., & Thompson, G. W. (2009). Integrating academics into agriculture programs: A Delphi study to determine perceptions of the national agriscience teacher ambassador academy participants. Journal of Agricultural Education, 50(2), 77-88, doi: 10.5032/jae.2009.02075

National Research Council. (1988). Understanding agriculture: New directions for education. Danville, IL: The Interstate Printers and Publishers.

Newmann, F. M. (1996). Authentic achievement: Restructuring school for intellectual quality. San Francisco: Jossey Bass.

Parr, B. A., Edwards, M. C., & Leising, J. G. (2009). Selected effects of a curriculum integration intervention on the mathematics performance of secondary students enrolled in an agricultural power and technology course: An experimental study. Journal of Agricultural Education, 50(1), 57–69. doi:10.5032/jae.2009.01057

Peck, J.K., Peck, W., Sentz, J., & Zasa, R. (1998). Students’ perceptions of literacy learning in a project based curriculum. In E. G. Sturtevant, J. A. Dugan, P Linder & W. M. Linek (Eds.), Literacy and community (pp.94-100). Texas A&M University: College Reading Association.

Pedersen, S., & Liu, M. (2003). Teachers’ beliefs about issues in the implementation of a student centered learning environment. Educational Technology Research and Development, 51(2), 57-76. doi:10.1007/BF02504526

PLTW (n.d.). PLTW Who We Are. Retrieved from http://www.pltw.org/about-us/who-we-are

Roegge, C. A., & Russell, E. B. (1990). Teaching applied biology in secondary agriculture: effects on student achievement and attitudes. Journal of Agricultural Education, 31(1), 27–31. doi: 10.5032/jae.1990.01027

Rogers, G. E. (2007). The perceptions of Indiana high school principals related to project lead the way. Journal of Industrial Teacher Education, 44(1), 49-65.

Rosenfeld, M., & Rosenfeld, S. (1998). Understanding the “surprises” in PBL: An exploration into the learning styles of teachers and their students. Paper presented at the European Association for Research in Learning and Instruction (EARLI), Sweden.

Saunders, M., & Chrisman, C. (2011). Linking Learning to the 21st Century: Preparing All Students for College, Career, And Civic Participation. Boulder, CO: National Education Policy Center. Retrieved from http://nepc.colorado.edu/publication/ linking-learning

Secretary’s Commission on Achieving Necessary Skills (SCANS). (1991). What work requires of schools. Report published by the National Technical Information Service (NTIS), US Department of Commerce.

Sprenger, M. (1999). Learning and memory: The brain in action. Alexandria Virginia. ASCD

Springer, L., Stanne, M. E. & Donovan, S. (1997). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A meta-analysis. National Institute of Science Education, University of Wisconsin, Madison. Retrieved from Research monographs, http://archive.wceruw.org/nise/Publications/

Stone, J. R., Alfeld, C., & Pearson, D. (2008). Rigor “and” relevance: Enhancing high school students’ math skills through career and technical education. American Educational Research Journal, 45(3), 767-795. doi:10.3102/0002831208317460

Travis, H. & Thomas, L. (2004). Traditional and constructivist teaching techniques. Journal of College Science Teaching, 34(3), 12-18. Retrieved from http://www.nsta.org/publications/article.aspx?id=Z349URi8cV44E1p4NTsLraiJTQ/lTLQRBW6YtU/9Bmc=

Tretten, R., & Zachariou, P. (1995). Learning about project based learning: Self assessment preliminary report of results. San Rafael, CA: The Autodesk Foundation.

Perceived Levels of Teacher Self-Efficacy among Secondary Arizona Agricultural Education Teachers

Abstract

The purpose of this study was to describe the level of teacher self-efficacy among novice (one through five years teaching) and experienced (more than five years teaching) secondary Arizona Agricultural Education teachers related to classroom, FFA, SAE, and content domains. A mailed questionnaire generated an 80% response rate (n = 74). Arizona Agricultural Education teachers reported high levels of efficacy in all constructs. Experienced teachers were slightly more efficacious in all of the constructs, compared to novice teachers. Opportunities for novice and experienced Agricultural Education teachers should continue to be a primary focus for teacher preparation programs, teacher associations, and state departments of education.

Introduction/Theoretical Framework

The need for educators is evident by the fact that more than 300,000 veteran teachers left the profession between 2004 and 2008 (Carroll & Foster, 2010). New educators were introduced in response to the void left by veteran teachers, only to see first year attrition rates increase since 1994 (Carroll & Foster, 2010). The turnover rate for teachers is substantially higher than other professions (Ingersoll, 2004). Furthermore, the school systems’ “inability to support high quality teaching in many of our schools is driven not by too few teachers coming in, but by too many going out, that is, by a staggering teacher turnover and attrition rate” (National Commission on Teaching and America’s Future [NCTAF], 2002, p. 3).

According to data from the National Center for Education Statistics [NCES] for the 1999-2000 school year, it is estimated that nearly a third of America’s educators leave the profession over the course of their first three years of teaching, and roughly half leave after five years (Ingersoll, 2004). The teacher shortage is due to a combination of the demand for more educators based on the increase of students, tied to the fact that a multitude of teachers are retiring (Ingersoll, 2004). With such an issue taking precedence, the public school system is unable to address its number one priority, the students, by providing them with highly qualified educators at every subject and every grade level.

The tasks of classroom work and FFA advisor responsibilities can overwhelm a new teacher in agricultural education with no experience or structure. Myers, Dyer, & Washburn (2005) ranked the top issues that new agricultural educators face. According to the panel findings, the top five were: organizing an effective alumni chapter, organizing an effective advisory committee, organizing and planning FFA chapter events and activities, the management of student discipline in the classroom, and recruiting and retaining alumni members. In multiple studies it was found that beginning agriculture teachers had issues with student discipline (Talbert, Camp, & Heath-Camp, 1994), and low morale levels in relation to rapport among teachers (Henderson & Nieto, 1991).

As a teacher, the number one goal is to allow the student to learn. With a strong set of skills and self-efficacy, teachers can assist students in the development of their cognitive

capabilities. Teacher self-efficacy or instructional efficacy is, “a teacher’s belief that he or she can reach even difficult students to help them learn” (Woolfolk, 2007, p.334).

Blackburn & Robinson (2008) examined levels of teacher self-efficacy of early agriculture teachers (1- 6 years experience). Results displayed that the group with the most experienced teachers (5-6 years experience) had the highest teacher self-efficacy scores. Blackburn & Robinson discussed that these teachers may have developed their own teaching style over time, allowing them to increase their belief of self-efficacy by mastering difficult situations. Another thought for higher levels of teacher self-efficacy from more experienced teachers included the possibility of the less efficacious teachers from the cohort leaving the profession.

Researchers in education tended to generalize that the reason so many teachers leave the profession during their first five years is due to low levels of teacher self-efficacy. Whittington, McConnell, and Knobloch (2006) looked at the levels of teacher self-efficacy of novice Ohio agriculture teachers at the end of the school year. According to their findings, “first-year, second-year, and third-year teachers are similarly efficacious at the end of the school year, and novice teachers in agricultural education in Ohio were efficacious at the end of the school year” (p.26). Whittington, et al: further concluded, “It is not necessarily experience that effects teacher efficacy, but a variety of factors” (p.35). Epps, Foor, and Cano (2010) reported no significant differences in the level of teacher efficacy between novice and experienced secondary agriculture teachers surveyed in the United States. Wolf (2008) investigated teacher self-efficacy in an analysis among beginning Agricultural Education teachers in Ohio. Teachers indicated they had high capability on most items. Teachers in the study reported the highest levels of efficacy in the Classroom Domain and the lowest levels in the SAE Domain. Swan, Wolf, and Cano (2011) looked at changes in teacher self-efficacy from the student teaching experience through the third year of teaching. The researchers found that individuals reported the lowest levels of teacher self-efficacy at the end of their first year of teaching and the highest levels at the conclusion of their student teaching experience.

The roots of self-efficacy are established as a component within social cognitive theory. Bandura (1997, p.3) stated, “People guide their lives by their beliefs of personal efficacy. Self-efficacy refers to beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments.” Self-efficacy can vary in its influence on life choices people make every day, the amount of effort put into an endeavor, the length of perseverance and resilience when faced with obstacles such as failure, stress, and depression (Bandura). “Among the types of thoughts that affect action, none is more central or persuasive than people’s judgments of their capabilities to exercise control over events that affect their lives” (Bandura, 1986, p.59).

Self-efficacy was further qualified in the literature to include teachers. “Teachers’ sense of efficacy, a teacher’s belief that he or she can reach even difficult students to help them learn, appears to be one of the few personal characteristics of teachers that is correlated with student achievement” (Woolfolk, 2007, p. 334). In other words, teacher self-efficacy revolves around creating environments conducive to learning and cognitive development. Teacher self-efficacy beliefs relate to the structure of curriculum and forming student perceptions of their ability to

learn (Bandura, 1997). Teachers with a high sense of teacher self-efficacy believe that their efforts in the classroom will leave a lasting impact on the student, no matter their background. High efficacy teachers create an atmosphere conducive to student success and learning. The teacher with a high sense of efficacy works diligently with students who struggle with content, spends more time on academic subject matters, and praises students for succeeding and making gains. Teachers with a low sense of teacher self-efficacy feel incapable of teaching or motivating difficult children for a long period of time due to influences from the home and neighborhood environment. Low efficacy teachers spend more time in the classroom on nonacademic material, fail to provide adequate time for students to answer, and constantly criticize the struggling student (Evans & Tribble, 1986).

Bandura (1997) proposed that teacher self-efficacy is built from four principal sources of information: mastery experiences, vicarious experiences, verbal persuasion, and social influences, with mastery experiences being the most influential source. Novice teachers are restricted in the number of mastery experiences due to the lack of time spent in the classroom. Glickman & Tamashiro (1982) found that novice teachers who leave the profession are less efficacious than teachers who remain. Although novice teachers may generally have lower teacher self-efficacy, student teachers may enter the profession with an enlarged level of teacher self-efficacy due to the mastery experiences and other obtained sources during student teaching (Knobloch, 2006).

As teachers grow in experience, studies suggest that a custodial view of classroom control with strict rules and standards to control discipline will take precedence (Bruning, Schraw, Norby, & Ronning, 2004). Bandura (1997) suggested that the mastery of more difficult situations leads to an increase in the level of teacher efficacy. Experienced teachers’ mastery experiences should allow them to perfect their preferred style of learning (Blackburn & Robinson, 2008). Furthermore, experienced teachers may develop a higher level of teacher self-efficacy in that they will have had experienced real success with the students in the classroom (Woolfolk, 2007). Experienced teachers vary in their level of efficacy depending on the level of efficacy for the school they teach at. “We are finding that the longer the teachers teach in a high-efficacy school, the higher their sense of personal efficacy, whereas the longer teachers teach in a low-efficacy school, the lower their sense of instructional efficacy” (Evans & Tribble, 1986, p.67).

The United States faces an educational crisis of retaining educators. This problem can be stated the same for agricultural programs in the state of Arizona, with a high demand for quality teachers. Many teachers remain in the profession because they display a high level of teacher efficacy (Knobloch & Whittington, 2003). If this holds true, it would suggest that agricultural education programs are unable to retain novice teachers due to their low levels of teacher self-efficacy. Knowing this, the problem statement is as follows: Do novice and experienced secondary agriculture teachers in Arizona differ on their level of teacher self-efficacy? The study addresses Priority 5: Efficient and Effective Agricultural Education programs from the National Research Agenda: American Association for Agricultural Education’s Research Priority Areas for 2011 – 2015 (Doerfert, 2011).

Purpose and Objectives

The purpose of the study was to compare the difference in the level of teacher self-efficacy between novice and experienced secondary Arizona Agricultural Education teachers. Specifically, the following research objectives guided the study:

1. Describe the perceived level of teacher self-efficacy of Arizona Agricultural Education teachers.

2. Describe the perceived level of teacher self-efficacy of novice Arizona Agricultural Education teachers.

3. Describe the perceived level of teacher self-efficacy of experienced Arizona Agricultural Education teachers.

4. Compare the perceived level of teacher self-efficacy between novice and experienced Arizona Agricultural Education teachers.

Procedures

The design used for this study was descriptive-correlational research, which “gathers data from individuals on two or more variables and then seeks to determine if the variables are related” (Ary, Jacobs, Razavieh, & Sorensen, 2006, p.27). The study used a census, meaning the entire population was surveyed and, the results will not be generalized beyond the population. Population parameters were used to describe teachers (novice, teachers with five years or less experience, and experienced, teachers with more than five years experience, agriculture teachers) in their level of teacher self-efficacy. The target population was secondary Agricultural Education teachers in the state of Arizona. The population consisted of secondary Agricultural Education teachers (N = 93).

Data were collected using an instrument developed by Wolf, (2008) that incorporated a variety of other sources (Duncan & Ricketts, 2006; Duncan, Ricketts, Peake, & Uesseler, 2005; Garton & Chung, 1996; Joerger & Boettcher, 2000; Myers, Dyer, & Washburn, 2005; Roberts & Dyer, 2004; Tschannen- Moran & Woolfolk Hoy, 2001). The instrument was a booklet questionnaire using a nine-point summated rating scale adapted from the Teacher Sense of Efficacy Scale (Tschannen-Moran & Woolfollk Hoy, 2001).

Validity was determined as a means to ensure quality research through a panel of seven experts in the field of agricultural education and teacher self-efficacy with knowledge of face and content validity. Content validity concerns the extent to which a specific set of activity/ factor items reflects a content domain (DeVellis, 2003).

The panel members were selected based on two criteria, knowledge of Arizona

Agricultural Education or knowledge of teacher self-efficacy research and teacher self-efficacy models. The experts were chosen to evaluate the instrument for appropriateness and clarity.

Modifications were made to the instrument for each item based on the recommendations of the panel of experts.

As the questionnaire used was developed directly from that used by Wolf (2008) whose study population closely approximates the population of this study, the reliability estimates for that instrument is reported. For Wolf (2008) the reliability of the instruments was assessed through a pilot test (N = 13) and a posthoc test (N = 39) using the Cronbach’s alpha internal consistency reliability coefficient. Cronbach’s alpha is concerned with the homogeneity of the items within a scale (DeVellis, 2003). Bandura (2006) recommended the use of Cronbach’s alpha to assess the internal consistency of self-efficacy instruments. The reliability estimates of the domains from the pilot and post-hoc tests were all above .85 reliability and are acceptable for this study. Nunnally and Bernstein (1994) stated: “in the early stages of predictive or construct validation research, time and energy can be saved using instruments that have only modest reliability, e.g., .70, it can be argued that increasing reliabilities much beyond .80 in basic research is often wasteful of time and money” (p.264).

Reliability was determined for the Content domain post-hoc as the items were specific for the study. Post-hoc reliability was computed by the researcher from the data collected from the population for the Content domain. The items related to the Content Capability construct yielded a Cronbach’s alpha coefficient of .81. The reliability analysis asserted the instrument was reliable since all computed coefficients were higher than the 0.7 minimum alpha level.

Teachers responded to their perceived capability of each item from 1= No Capability to 9= A Great Deal of Capability. The instrument also included items to obtain information on the demographic background (gender, age, years of experience as a teacher), and form of teacher certification (traditionally versus non-traditionally) of all agriculture teachers in the state of Arizona.

Data were collected using a mailed questionnaire guided by Dillman, Smyth, and Christian (2009). An initial pre-notice letter was sent in an effort to inform teachers about the study and that a questionnaire would be sent to them in the next week. The following week, the questionnaire, cover letter, and fifty cent piece incentive were mailed to the teachers with a self-addressed stamped envelope and pre-stamped return postcard for anonymous response. Approximately one week following distribution of the questionnaire, a follow-up postcard was mailed to the teachers in order to determine if they had received and taken the questionnaire. If teachers had not returned a return postcard by the designated cut off date, a follow-up questionnaire was sent along with a follow-up letter explaining the teachers’ importance in the study and a University of Arizona keychain incentive. A final contact letter was mailed to those who had not returned a return postcard. This letter emphasized the relevance of the study and importance of having a large response rate. Seventy four individuals returned the instrument for an 80% response rate. The results of this study should not be generalized beyond the respondents.

The data were analyzed using SPSS version 18. Constructs were summated to analyze the data. Cases where individual domains (Classroom, FFA, SAE, Content) had more than 15 percent missing items were excluded from the data set. In cases with less than 15 percent missing

in individual domains (Classroom, FFA, SAE, Content), mean replacement was used. Of the 74 individuals who returned an instrument, 68 (73%) were deemed usable. Descriptive statistics were used to address research objective one: describe the perceived level of teacher self-efficacy of Arizona Agricultural Education teachers; research objective two: describe the perceived level of teacher self-efficacy of novice Arizona Agricultural Education teachers; and research objective three: describe the perceived level of teacher self-efficacy of experienced Arizona Agricultural Education teachers. Effect size calculations (Cohen’s d) were utilized to achieve objective four.

Findings

The Arizona Agricultural Education teachers in this study reported a mean age of 36 years (SD = 10.7) of respondents (n = 68). Female respondents (n = 37) made up 54% and male Arizona Agricultural Education teachers (n = 31) made up 46% of respondents. The average years teaching for Arizona Agricultural Education teachers was 11 years (SD = 9.7) (see Table 1). One (1.5%) of the respondents did not report years of experience.

Table 1

Arizona Agricultural Education Teacher Number of Years TeachingYears n %0-8 36 52.99-16 15 22.117-24 7 10.325-36 9 13.2Missing 1 1.5Total 68 100.0Note: Mean = 10.78; Median = 8; Mode = 1; SD = 9.7; Range = 32.5.

In the Classroom domain, Arizona Agricultural Education teachers (n = 68) reported a mean perceived teacher self-efficacy level of 6.89 (SD = .82). Teachers reported a mean perceived teacher self-efficacy level of 6.88 (SD = 1.35) in the FFA domain. Within the SAE domain, teachers reported a mean level of perceived teacher self-efficacy of 6.46 (SD = 1.17). Arizona Agricultural Education teachers reported a mean level of perceived teacher self-efficacy at 6.23 (SD = 1.18) in the domain of Content (see Table 2).

Table 2

Arizona Agricultural Education Teacher Self-Efficacy

DomainNovice Experienced Effect Size Overall(n = 26) (n = 41) (Cohen’s Index) (n = 68)

M SD M SD d M SDClassroom 6.66 (0.89) 7.01 (0.75) 0.43 6.89 (0.82)FFA 6.65 (1.38) 7.00 (1.35) 0.26 6.88 (1.35)SAE 6.38 (1.29) 6.47 (1.46) 0.07 6.46 (1.17)

Content 6.11 (1.05) 6.26 (1.20) 0.13 6.23 (1.18)Note. 1 = No Capability to 9 = A Great Deal of Capability

In the Classroom domain, novice Arizona Agricultural Education teachers (n = 26) reported a mean perceived teacher self-efficacy of 6.66 (SD = .89). The teachers reported a mean perceived teacher self- efficacy level of 6.65 (SD = 1.38) in the FFA domain. Novice teachers reported a mean level of perceived teacher self-efficacy of 6.38 (SD = 1.29) in the SAE domain. Novice experience level Arizona Agricultural Education teachers reported a mean level of perceived teacher self-efficacy at 6.11 (SD = 1.05) in the domain of Content.

In the Classroom domain, experienced Arizona Agricultural Education teachers (n = 41) reported a mean perceived teacher self-efficacy of 7.01 (SD = .75). The teachers reported a mean perceived teacher self-efficacy level of 7.00 (SD = 1.35) in the FFA domain. Experienced teachers reported a mean level of perceived teacher self-efficacy in the SAE domain of 6.47 (SD = 1.46). Experienced Arizona Agricultural Education teachers reported a mean level of perceived teacher self-efficacy at 6.26 (SD = 1.2) in the domain of Content.

The largest effects were detected between novice and experienced teachers in the Classroom and FFA domains, respectively, and were described as small (Cohen, 1988). Negligible effects were detected between novice and experienced teachers in the SAE and Content domains.

Conclusions/Recommendations/Implications

As a whole, Arizona Agricultural Education teachers fell into the descriptor range of, “Quite a Bit of Capability” when describing their level of efficacy in the Classroom, FFA, and SAE constructs. Teachers reported between the descriptors “Some Capability” and “Quite a Bit of Capability” for Content efficacy. Teachers reported the highest levels of teacher self-efficacy in the FFA construct and the lowest levels in the Content construct. Knowing this, the researcher concluded that Arizona Agricultural Education teachers perceive themselves to be efficacious.

Regarding novice Arizona Agricultural Education teachers, the majority describe their capability in the four domain areas between, “Some Capability” and “Quite a Bit of Capability.” When compared to the entire population, novice teachers display slightly lower levels of teacher self-efficacy, specifically in the Classroom construct. Novice teachers reported the highest levels of teacher self-efficacy in the FFA domain and the lowest levels in the Content construct.

Experienced Arizona Agricultural Education teachers were somewhat diverse in their perceived capability. In the Classroom and SAE constructs, experienced teachers fell into the descriptor of “Quite a Bit of Capability.” In the FFA construct, the highest number of experienced teachers found themselves between the descriptors “Quite a Bit of Capability” and “A Great Deal of Capability.” Experienced teachers feel between the descriptors “Some Capability” and “Quite a Bit of Capability” for the Content construct. Experienced teachers reported the highest levels of teacher self-efficacy in the FFA construct and the lowest levels in the Content construct. When compared to all Arizona Agricultural Education teachers,

experienced teachers were similar in their levels of teacher self-efficacy in the Classroom, SAE, and Content constructs and higher in the FFA construct. Experienced teachers were slightly higher in their levels of efficacy in the Classroom and SAE constructs when compared to novice teachers. Experienced teachers were higher in their FFA efficacy level than novice teachers and the same in the Content construct.

Teachers in this study reported that they were capable in their ability to teach agriculture. Specifically, both novice and experienced teachers reported to be the most efficacious in the FFA domain. All teachers were the least efficacious in the Content domain. As a result, more emphasis in teacher development from the Department of Agricultural Education at the University of Arizona and the Arizona Agriculture Teachers Association (AATA) should stem from content aspects of an Agricultural Education program as opposed to FFA components, which should still find priority in Arizona Association FFA events and meetings.

Novice teachers were efficacious in all areas of the Agricultural Education program. Novice teachers were concentrated in their efficacy in the Classroom and Content domains but displayed a more diverse level of efficacy in the FFA and SAE domains. This may relate to the number of teachers that are limited in knowledge pertaining to the FFA program due to their certification method or non involvement during their youth. Determining levels of knowledge about FFA from novice Arizona Agricultural Education teachers should be a priority for teacher education programs and professional development. If novice teachers are aware of the importance of the FFA program but feel unable to conduct the tasks, induction programs should be implemented by the Arizona Association FFA to gradually guide novice teachers into their responsibilities.

Experienced teachers displayed the lowest level of efficacy in the Content domain. Experienced teachers require more emphasis in updating their content knowledge. With an average experience of 16 years teaching, many experienced teachers are facing a continued change in agricultural practices and science. Priority should be placed on professional development from the AATA to create tools and provide workshops that supplement experienced teachers with up to date content knowledge.

Mastery experiences as indicated by Bandura (1997) are the most powerful source of efficacy information. Successful mastery experiences by an individual raise efficacy and mastery experiences of failure lower efficacy. An increase in efficacy due to these experiences may assist in the retention of novice teachers as many novice teachers who leave the profession are less efficacious than teachers who remain (Glickman & Tamashiro, 1982). The Arizona Department of Education and teacher educators must recognize that a positive experience is crucial for teachers, particularly novice teachers, and these individuals must provide opportunities and environments that will assist teachers in building levels of efficacy. Arizona Agricultural Education teachers reported high levels of efficacy in their programs; this is evidence that programs developed in the state are supplying current and future educators with successful experiences. Depending on perceived levels of efficacy, these practices should be considered by teacher education programs outside of Agricultural Education, including other Career and Technical Education areas as well as general education as a means of properly preparing teachers to increase their teacher self-efficacy and insure they will remain in the profession.

The teacher self-efficacy scores from this study mirror the levels of teacher self-efficacy reported in studies of novice and experienced teachers using Tschannen-Moran, and Woolfolk Hoys’ (2001) Teachers’ Sense of Efficacy Scale (TSES) (Knobloch & Whittington, 2003; Blackburn & Robinson, 2008; Wolf, 2008; Epps, Foor, & Cano, 2010; Swan. Wolf, and Cano, 2011). The utilization of this instrument will allow for clearer and more comprehensive analysis of Agricultural Education teachers’ self-efficacy, as well as future research on Agricultural Education teacher self-efficacy.

While studies (Knobloch & Whittington, 2003; Blackburn & Robinson, 2008) sought to determine levels of teacher self-efficacy, these studies were limited to novice teachers only. Measurement of novice and experienced teachers not only provides two factors to correlate with teacher self-efficacy, but also provides an overview of the capabilities of novice and experienced teachers that may serve as an overview of needed changes in professional development workshops and curriculum structure within a teacher educator program, which may result in a advanced skill sets for novice and experienced teachers, increasing levels of teacher self-efficacy, resulting in higher student achievement within Arizona Agricultural Education programs.

Replication of the study is encouraged with similar populations of secondary Agricultural Education instructors. Specifically, studies should be replicated in Arizona to further substantiate the findings and further validate the instrument. In addition, studies should be done in other states so that results may be compared and further interpretation may be made. A national study of perceived teacher self-efficacy may not be as meaningful as a statewide study since teacher preparation programs and state FFA associations vary from state to state. Furthermore, changes in Agricultural Education teacher self-efficacy should be studied longitudinally to address the possible changes in the domains over time.

References

Ary, D., Jacobs, L.C., Razavieh, A., & Sorensen, C. (2006). Introduction to research in education (7th ed.). Belmont, CA: Thomson Wadsworth.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman.

Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.). Self-Efficacy Beliefs of Adolescents, (Vol. 5., 307 – 337). Greenwich, CT: Information Age Publishing.

Blackburn, J. J., & Robinson, J. S. (2008). Assessing teacher self-efficacy and job satisfaction of early career agriculture teachers in Kentucky. Journal of Agricultural Education, 49(3), 1-11.

Bruning, R. H., Schraw, G. J., Norby, M. M., & Ronning, R. R. (2004). Cognitive psychology and instruction (4th ed). Upper Saddle River, NJ: Pearson Merrill Prentice Hall.

Carroll, T. G., & Foster, E. (2010, January). Who will teach? Experience matters. Washington, DC: National Commission on Teaching and America's Future.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

DeVellis, R. F. (2003). Scale development: Theory and applications (2nd ed.). Thousand Oaks, CA: Sage Publications.

Dillman, D.A., Smyth, J.D., & Christian, L.M. (2009). Internet, Mail, and Mixed-Mode Surveys: The Tailored Design Method (3rd ed.). New Jersey: John Wiley & Sons.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Duncan, D., & Ricketts, J. C. (2006). Total program efficacy: A comparison of traditionally and alternatively certified agriculture teachers. Proceedings of the Southern Region American Association for Agricultural Education Conference, Orlando, FL, 409-419.

Duncan, D., Ricketts, J. C., Peake, J. B., & Uesseler, J. (2005). Identifying teaching and learning in-service need of Georgia Agriculture teachers. Proceedings of the National Agricultural

Education Research Conference, San Antonio, TX, 31, 91 – 102.

Epps, R. B., Foor, R. M, & Cano, J. (2010) Keepers, Stayers, Leavers, and Lovers: Are there Teacher Efficacy and Job Satisfaction Differences between Novice and Experienced Teachers?. Manuscript submitted for publication.

Evans, E. D., & Tribble, M. (1986). Perceived teaching problems, self-efficacy, and commitment to teaching among preservice teachers. Journal of Educational Research, 80(2), 81 – 85.

Garton, B. L., & Chung, N. (1996). The inservice needs of beginning teachers of agriculture as perceived by beginning teachers, teacher educators, and state supervisors. Journal of Agricultural Education, 37(3), 52 – 58.

Glickman, C. D., & Tamashiro, R. T. (1982). A comparison of first year, fifth year, and former teachers on efficacy, ego development and problem solving. Psychology in the Schools 19(4), 558-562.

Henderson, J. L. & Nieto, R. D. (1991). Morale levels of first-year agricultural education teachers in Ohio. Journal of Agricultural Education, 32(1), 54-58.

Ingersoll, R. (2004). Four myths about America’s teacher quality problems. In M. Smylie & D. Miretzky (Eds.), Developing the teacher workforce: The 103rd Yearbook of the National Society

for the Study of Education (pp. 1-33). Chicago: University of Chicago Press.

Joerger, R., & Boettcher, G. (2000). A description of the forms of assistance and the nature of events experienced by beginning secondary agricultural education teachers in Minnesota. Proceedings of the 54th Annual AAAE Central Region Research Conference and Seminar in Agricultural Education, 108-119.

Knobloch, N. A. (2006). Exploring relationships of teachers’ sense of efficacy in two student teaching programs. Journal of Agricultural Education, 47(2), 36 – 47.

Knobloch, N. A., & Whittington, M. S. (2003). Differences in teacher efficacy related to career commitment of novice agriculture teachers. Journal of Vocational Educational Research, 27(3),

331.

Myers, B. E., & Dyer, J. E. (200a). Agricultural teacher education programs: a synthesis of the literature. Journal of Agricultural Education, 48(1), 24-34.

Myers, B. E., Dyer, J. E., & Washburn, S. G. (2005). Problems facing beginning agriculture teachers. Journal of Agricultural Education, 46(3), 47-55.

National Center for Educational Statistics. (n.d.). The condition of education 2005. http://nces.ed.gov/programs/quarterly/vol_7/1_2/9_1.asp

National Commission on Teaching and America’s Future, (2002). Unraveling the “Teacher Shortage” problem: Teacher Retention is the Key. Retrieved September 23, 2010, from http://www.ncsu.edu/mentorjunction/text_files/teacher_retentionsymposium.pdf

Nunnally, J. C., & Bernstein, I. H. (1994), Psychometric Theory. (3rd ed.). New York, NY: McGraw-Hill.

Roberts, T. G., & Dyer, J. E. (2004). Characteristics of effective agriculture teachers. Journal of Agricultural Education, 45(4), 82 – 95.

Swan, B. G., Wolf, K. J., & Cano, J. (2011). Changes in teacher self-efficacy from the student teaching experience through the third year of teaching. Journal of Agricultural Education,

52(2), 128 – 139.

Talbert, B. A., Camp, W. G., & Heath-Camp, B. (1994). A year in the lives of three beginning agriculture teachers. Journal of Agricultural Education, 35(2), 31 – 36.

Tschannen-Moran, M., & Woolfolk Hoy, A. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783-805.

Whittington, M. S., McConnell, E., & Knobloch, N. A. (2006). Teacher efficacy of novice teachers in agricultural education in ohio at the end of the school year. Journal of Agricultural Education, 47(4), 26.

Wolf, K. J., (2008) Agricultural education teacher self-efficacy: A descriptive study of beginning agricultural education teachers in Ohio. Unpublished doctoral dissertation, The Ohio State

University.

Woolfolk, A. (2007). Educational psychology. Boston, MA: Allyn and Bacon.

The Autonomy Trap: Why Highly Successful Agricultural Education Teachers Leave the Profession, A Phenomenological Perspective

Mindi S. Clark, Oklahoma State UniversityNicholas R. Brown, Oklahoma State University

Jon W. Ramsey, Oklahoma State University

Abstract

This phenomenological study examined the essence of highly successful teachers leaving the field of teaching secondary agricultural education. Seven former agricultural education teachers were selected to engage is a semi-structured interview; these educators had been identified as being highly successful by staff members of the agricultural education division of [State] during their teaching career. Participants varied in gender, age, years of experience, and demographic areas. Several themes emerged: (1) Common characteristics exist among successful teachers who exit the profession; (2) In order to remain satisfied in the profession, successful teachers prefer to work under their own self-motivation rather than others’ expectations; (3) Successful teachers who exit the profession blame themselves for leaving; and (4) Successful teachers who decide to exit the profession have invested so much of themselves in the program, at such a high personal cost, that their final decision to leave is irreversible.

Introduction

A former agriculture teacher in [State] who was considered highly successful described his professional experience as follows:

It is the single best profession in the world. It is the exact profession I chose as a kid. Although it did the most damage to my physical, mental, and marriage and family health, it is the thing that I want to be remembered for when I die. It was what I wanted, and it about destroyed me. I still think about it daily. That is how good it is and can be, but it is wrong. There is just simply too much. It eats you alive. [4:473]

The role of a secondary agricultural education teacher is extensive. In fact, Torres, Lambert, and Lawver (2009a) reported that the responsibilities of the instructor would continue to expand until a point of task saturation is reached. Agricultural education teachers are expected to perform the daily tasks of facilitating classroom and laboratory instruction, supervised agricultural experience programs (SAEs), and a variety of activities related to the FFA on a daily basis. In addition, agricultural education teachers are expected to have strong community relationships, meet obligations identified by their school district, and gain the support of parents and administrators. Researchers have identified that agricultural education teachers typically work hours extending well beyond that of a 40-hour work week (Boone & Boone, 2007; Croom, 2003; Joerger & Boettcher, 2000; Torres et al., 2009a; Torres, Ulmer, & Ashenbrener, 2007).

The National Council for Agricultural Education’s Strategies for Quality & Growth in Agricultural Education established a goal of having 10,000 quality agricultural education programs in place by 2015, commonly known as the 10x15 goal (Team Ag Ed, n.d.). Although

the 10x15 goal is not promoted as explicitly as it once was, quality programs are still in demand. In an effort to describe the quality and impact of a secondary agricultural education program, Hall, Briers, and Dooley (2009) identified themes describing a quality program as well-rounded, relevant, and well respected. Hall et al. (2009) acknowledged the need for increased research focusing on the characteristics and behaviors of exemplary professional educators who teach in quality programs in order to increase the overall vitality of the profession. However, a problem exists among retaining quality teachers as the number of beginning teachers who exit the profession is staggering. Heath-Camp and Camp (1990) and Marso and Pigge (1997) reported that over 50% of beginning teachers leave within the first six years resulting in a large turnover. In their pilot study, Barnes, Crow, and Shaefer (2007) found that the turnover costs associated with teachers leaving the profession equated to an average of $8,371 per turnover; thus, the need to retain quality teachers is important, not only for establishing quality programs, but also for economic reasons. This study examined the phenomenon of quality teachers that chose to leave the profession. In an effort to facilitate achievement of the 10 x 15 goal by reducing the loss of superior teachers who operate quality agricultural education programs, the researchers chose to examine the essence of highly successful teachers, including personal characteristics, factors that cause them to leave the profession, and factors that persuade them to remain in the profession.

Review of Literature

A review of literature examined characteristics of quality teachers, reasons that these teachers leave the secondary agricultural education profession, and factors related to agricultural education teachers who stay in the profession in an effort to better understand the underlying principles for attrition and retention.

Regarding characteristics of quality teachers, Roberts and Dyer (2004) identified 40 characteristics commonly associated with high-quality teachers and subsequently arranged them into categories. These categories included FFA, SAE, building community, partnerships, marketing, professional growth/professionalism, and program planning. Also included were personal qualities such as caring for students, belonging to a supportive family, and exhibiting high motivation, enthusiasm, self-confidence, open-mindedness, good organization, resourcefulness, honesty, morality, and ethics (Roberts & Dyer, 2004). According to McLean and Camp (2000), personal qualities of an effective teacher are variables that are inherent to the individual and not learned during teacher preparation. In addition, Miller, Kahler, and Rheault (1989) posit that these qualities do have an effect on the success of the teacher.

Motivation for achievement is a personal characteristic many agricultural education

teachers possess. In fact, Baruch, O’Creevy, Hind, and Vigoda-Gadot (2004) identified a direct correlation, r = 0.43, p < 0.01, between the need for achievement and job performance in other literature. Boone and Boone (2007) identified teachers as being motivated by personal goals, personal satisfaction, and determination. Highly motivated agricultural education teachers find it extremely difficult to decline opportunities for further achievement. Torres, Lambert, and Tummons (2009b) reported that agricultural education teachers are least capable of resisting involvement when discussing their career, indicating that agricultural education teachers are less likely to turn down job-related opportunities. Croom (2003) suggested that quality teachers often experience self-efficacy through personal accomplishment. Accordingly, motivation for

achievement is a personal characteristic that affects the success of agricultural education teachers and the effectiveness of their programs.

It is important to conduct research that can significantly contribute to the profession by investigating former teachers who were highly successful, effective individuals. In the recently published National Research Agenda for Agricultural Education, Research Priority Areas (RPAs) in agricultural education have been established. This study addressed RPA 5: “Efficient and Effective Agricultural Education Programs,” (Doerfert, 2011).

While previous studies (Boone & Boone, 2007; Croom, 2003; McLean & Camp, 2000; Miller, Kahler, & Rheault, 1989; Roberts & Dyer, 2004; Torres et al., 2009b) identified characteristics contributing to the success of teachers, an abundance of literature also focused on secondary teachers experiencing dissatisfaction and leaving the agricultural education profession, which is often given the term burnout. Factors related to time, school leaders, parents, compensation, clerical tasks, and unmotivated students have all been contributors to the dissatisfaction of teachers and could lead to their exit (Boone & Boone, 2009; Chenevey, Ewing, & Whittington, 2008; Torres et al., 2009a; Torres et al., 2009b). Of these factors, time spent on the job has been extensively documented in the literature and identified as a substantial contributor to leaving the profession (Boone & Boone, 2009; Chenevey et al., 2008; Moore & Camp, 1979). However, Lambert, Henry, and Tummons (2011) reported that teachers who became exemplary time managers actually compounded their own workload by developing new goals and activities, thus implying many teachers create their own long work hours.

Conversely, when investigating reasons agricultural education teachers continue to teach, Boone and Boone (2007) identified a host of contributory factors, such as student achievements, personal goals and satisfaction, determination, helping and educating students, financial rewards, enjoyment of teaching agriculture, and professional brotherhood. Walker, Garton, and Kitchel (2004) reported that most teachers were generally satisfied with their beginning teaching experience whether they remained in, or left the profession. Further, Walker et al. (2004) opined that those teachers who stayed in the profession may have reached a plateau in their career and became complacent, implying that some teachers who remain in the profession reduce their workload to be more aligned with basic job expectations. Moore and Camp (1979) asked if we should consider requiring less of agricultural educators and suggested an analysis of teacher workload.

Purpose of the Study

Croom (2003) suggested that further research was necessary to understand teachers who exited the profession. Additionally, Walker et al. (2004) recommended employing qualitative methods to this research topic. In response, the researchers embarked on this phenomenological study to discover the essence of the experiences of highly successful teachers who left the agricultural education profession at the secondary level and create a profile that describes the characteristics of those who are most at risk of leaving the profession early. Further, the researchers sought to develop and recommend mitigation techniques aimed at reducing attrition.

Methodology

Sample

Polkinghorne (1989) recommended interviewing five to 25 individuals who have all experienced the same phenomenon. Therefore, program specialists from the agricultural education division of [State] were asked to identify five to 10 former agriculture teachers who had been highly successful during their teaching tenure. Ultimately, the research team purposefully selected and interviewed seven of the 10 recommended former [State] agriculture teachers. The [State] Agricultural Education Teachers Association and National Association of Agricultural Educators (NAAE) recognized five of the study participants for outstanding teaching accomplishments. The sample of seven people (six males and one female) all exited the profession early in their career. Their teaching experience ranged from three to 15 years.

Research Design

Creswell (2007) posited that, “phenomenological study describes the meaning for several individuals of their lived experiences of a concept or a phenomenon” (p. 57). All of the researchers were former agricultural education teachers. Therefore, Moustakas’s (1994) transcendental phenomenology approach was determined to be the most appropriate design because the research team desired to focus less on interpretation and more on accurately describing the experiences of study participants. According to Moustakas (1994), epoche or bracketing is a process used by the research team to extract the researcher’s experiences and preconceived ideas, as much as possible, in an effort to adopt a clean perspective toward the phenomenon of agricultural education teachers exiting the classroom. Moustakes (1994) explained “epoche requires the elimination of suppositions and the raising of knowledge above every possible doubt” (p. 26).

Procedure

Ethics “constitute a universal end goal” (p. 846) in all quality qualitative inquiry, regardless of method or paradigm (Tracy, 2010). The research team was cognizant of ethical considerations during the entire research process and worked to ensure that all actions were ethically grounded. Upon approval of the institutional review board (IRB), participants were recruited through telephone conversations explaining the purpose of the study and the research procedure. According to Creswell (2007), phenomenological data collection often consists of in-depth interviews with study participants. Consenting research subjects were, therefore, asked to participate in one-hour, semi-structured, personal interviews, which were all conducted by the primary researcher. Each interview consisted of the following two open-ended questions:

1. What have you experienced regarding your decision to leave the agricultural education teaching profession?

2. What do these experiences mean to you?

Due to the semi-structured nature of the interview, participants were provided the latitude to vary the amount of discussion time spent on each question. Document analysis was also used to further understand the context of the phenomenon. Interviews were recorded using a Smartphone

and digital recording application and transcribed verbatim by the researchers. Data collection and ongoing analysis continued until data saturation was achieved.

Data Analysis

Data were analyzed using qualitative data analysis software (ATLAS.ti) to conduct line by line coding and memoing (Creswell, 2007). Horizonalization (Creswell, 2007) was used to identify significant statements resulting in “clusters of meaning” (p. 61), which were arranged into themes through researcher negotiation. A textural description of the phenomenon was developed that outlined what the participants experienced (Moustakas, 1994). Secondly, a structural description was constructed, which served to explain the context or setting of the phenomenon (Moustakas, 1994). Finally, the two reports were combined to create an invariant structure, which focused on the essence of the phenomenon (Moustakas, 1994).

Credibility

Tracy (2010) stressed the importance of credibility or trustworthiness in qualitative research. Furthermore, Lincoln and Guba (1985) argued that dependability is paramount to high-quality, qualitative research. In an effort to thickly describe the essence of the phenomenon, direct quotes from interview transcriptions are presented in the findings. Researchers used triangulation during the theme development process. Triangulation assumes that when two or more sources of data arrive at the same conclusion, then the conclusion is more reliable (Denzin, 1978). All themes that emerged during data analysis were products of information derived from two or more participants.

Reflexivity of the Researchers

Creswell (2007) posited that it is no longer “acceptable to be omniscient, distanced qualitative writers” (p. 178). He further added, “how we write is a reflection of our own interpretation based on the cultural, social, gender, class, and personal politics that we bring to research” (p. 179). Although efforts were made to bracket the researchers’ prior experiences during data analysis, it is impossible to completely avoid researcher bias in qualitative inquiry (Creswell, 2007). Therefore, the researchers determined it important to provide the reader a short synopsis of their prior experiences that pertain to the phenomenon in question.

Researcher one developed an interest for agriculture early in her life. She was a successful student in all areas of agricultural education. She received her state FFA degree and served as a state FFA officer. She later received a Bachelor of Science degree in Agricultural Education at [State] University and a Master of Education in Guidance and Counseling from [State] University. She taught high school agriculture for four years in a rural setting. She left the secondary program to pursue an agricultural education career in higher education while completing a Doctor of Philosophy degree in Agricultural Education at [State] University.

Researcher two was raised on a small family farm in northeastern [State] and was an agricultural education student and active FFA member throughout high school. He earned both his state and American FFA degrees. Upon graduation from [State] University he taught

agricultural education in a suburban school district for six years. During that time he earned a Master of Public Administration from the University of [State]. He is currently a graduate research and teaching assistant while pursuing a Doctor of Philosophy degree in Agricultural Education at [State] University.

Researcher three identified agricultural education as a career choice in high school. He was not raised in an agricultural setting but worked in a production agriculture placement as a high school FFA member, and later raised livestock. These SAE experiences helped prepare him for course work at the post-secondary level. Upon completion of his BS Degree from [State] University, he taught agricultural education and advised FFA chapters for fourteen years. While teaching, he completed his Masters degree in Agricultural Education and served as a cooperating teacher and early field experience mentor for [State] University. In 2009, he completed his doctoral program and currently holds a faculty appointment at [State] University.

Assertions, Conclusions, and Recommendations

Four thematic assertions emerged from findings formulated during data analysis of interview responses. These assertions are reported along with conclusions and appropriate recommendations for each theme. Self-Determination Theory (SDT) emerged after the interviews of the first and second participants and was used as a theoretical lens during data analysis. The foundation to SDT, as described by Gagné and Deci (2005), addresses the difference between autonomous motivation and controlled motivation; in other words, individuals complete tasks based on a want to attitude compared to a have to attitude. SDT proposes that when individuals are intrinsically motivated by internal expectations, they have inherently autonomous motivation, but when individuals are extrinsically motivated by external expectations, they experience controlled motivation. Controlled motivation is likely to yield poorer performance on job-related tasks compared to autonomous motivation (Gagné & Deci, 2005). In addition, autonomy-supportive work climates will lead to positive work outcomes. The assertions, conclusions, and recommendations of this study are framed within the SDT.

Theme 1: Common characteristics exist among successful teachers who exit the profession

Interviews revealed shared individual key characteristics among the participants. As participants shared the essence of their experiences during their tenure as an agricultural education teacher, it became clear that these educators were all self-motivated, ambitious, and competitive; they also exhibited a strong work ethic and commitment to student success. The respondent quotations included in Table 1 are examples of data used to develop this theme.

Table 1 Theme 1: Common characteristics exist among successful teachers who exit the professionThematic Category Response

Motivated,Ambitious,Competitive

“I was a teacher with a competitive drive who wanted people to think well of me as an individual and as a teacher. So, I dove into my work and continued to work hard. I wanted people to brag about

my accolades and the awards won.” [5:88]

“I had the best kids, with the best talent, and I knew I could coach them, so I might as well go win.” [4:174]

“I put the pressure on myself because I am a pretty competitive individual, and I do not like to lose.” [7:72]

Work Ethic “Some teachers have a basic agricultural education program and leave school at 3:00 p.m. I am not satisfied with the success that brought them, so I worked longer hours.” [3:26]

“I did not want to be seen as the teacher who was drawing a paycheck and not working.” [2:201]

“I did not take a day off without a good reason. You will find that across the board with all competitive, hard-working people, the kind of people you want teaching.” [4:318]

Commitment to Student Success

“I was a teacher because I was making a difference in the lives of students.” [3:56]

“Most agricultural education teachers pursue something if they see value in it, and I saw value in opportunities for students.” [4:445]

“I worked to develop a well-rounded program, and extra time is exerted when you try to be well rounded for students.” [5:188]

The researchers identified five personal and professional characteristics related to successful agriculture teachers. Successful teachers are competitive, ambitious, and motivated, leading to the two professional characteristics of employing a strong work ethic and being committed to student success (see Figure 1). These educators are competitive in that they want to be successful among their peers. They fuel their ambitions by achieving personal goals and pursuing new opportunities for themselves and their students, and they are motivated intrinsically to succeed. No matter their interest, be it classroom and/or laboratory instruction, FFA, or SAE, these teachers attack tasks with passion and energy. In addition, successful teachers are committed to students who want to succeed. When a student shows interest in an area, this type of teacher learns new concepts to meet the needs of the student. Finally, work ethic is a critical identifier of a highly successful teacher. Teachers who possess a strong work ethic may excessively extend their hours worked beyond the average, which is well over 40 hours (Boone & Boone, 2007; Croom, 2003; Joerger & Boettcher, 2000; Torres et al, 2009a; Torres et al, 2009b).

In terms of the National Council’s 10x15 goal, these characteristics are important to know when attempting to identify agricultural education teachers that might exit the profession early, which compounds the national teacher shortage. Therefore, the profile (see Figure 1) should be used to help identify those “at risk” teachers and intervene before dissatisfaction is reached. The personal and professional characteristics possessed by the highly successful teachers in this study should be used to identify quality teachers as encouraged by the National Research Agenda’s RPA 5 (Doerfert, 2011).

Figure 1. Profile of an “at risk” highly successful agriculture teacher

Theme 2: In order to remain satisfied in the profession, successful teachers prefer to work under their own self-motivation rather than others’ expectations.

Theme two focused on experiences leading to agricultural education teachers’ decision to leave the profession. Teachers adopted additional tasks if students showed interest in an area regardless of time required for those tasks. In addition, teachers participated in new, competitive opportunities, regardless of the nature, as they were presented, aligning with the findings of Torres et al. (2009b). However, if the teacher did not have autonomy in the situation, these tasks were perceived by the teacher as extra time restrictions imposed by parents, administrators, and state staff (Table 2).

Table 2

Theme 2: In order to remain satisfied in the profession, successful teachers prefer to work under their own self-motivation rather than others’ expectations Thematic Category Response

Time SpentAutonomously

“I know what a quality program should look like, that is why I chose to invest so many hours.” [5:164]

“For me, it was a personal choice because you want students to be successful. I went through the program so I know what it takes to assist students in being successful and it takes extra time.” [3:38]

“The time I invested outside of contracted time is probably the time when I was being most effective.” [2:74]

Autonomy Removed “I put 110% of my time into the program. I can remember working on speeches at 1:00 a.m. to help my students. Once you do that then you are expected to continue working hours like that.” [7:76]

“I often had a struggle with the demands that were placed on me by the parents.” [6:57]

“I was getting increasingly upset at the state office for coming up with other activities and responsibilities and consistently telling me I needed to do it.” [4:457]

When participants were are able to attack tasks autonomously, meaning they had latitude in deciding what they wanted to do, they were more likely to enjoy their work and succeed. However, when their autonomy was removed and they had to complete tasks, they began to experience job dissatisfaction. These findings considerably align with Self-Determination Theory (Gagné & Deci, 2005). Simply stated, when intrinsic motivation and autonomy are reduced due to increased demands from other sources, such as administrators, parents, and state agricultural education staff, the agricultural education teacher begins to consider leaving the profession due to job dissatisfaction.

Personal characteristics of the teacher hinder many teachers from saying no to a variety of increased demands due to their competitive nature as noted by Torres et al. (2009b). Thus, it is recommended that state staff members critically examine the relevance of additional activities before implementing those activities because the teachers reflecting the characteristics identified in this study will be more likely to participate. Those who do not possess the identified characteristics typically do not add additional competitive tasks to their workload; therefore, adding irrelevant activities does not help the profession. Further, current activities and responsibilities should be re-evaluated to determine their importance in accomplishing the goals of agricultural education and eliminate items if needed. Attention should focus on relevance and the level of learning accomplished rather than quantity of student activities.

School administrator awareness of functions and responsibilities of agricultural educators’ could aid administrators and stakeholders when determining the need to assign additional duties that negatively affect the autonomy of the teacher. For example, an interview participant stated that, “Administrators as a whole need to understand an agriculture teacher’s role in the school and the fact that it does take more time...” [4:284] Thus, it is recommended, as a mitigation technique, that state agricultural staff provide educational opportunities to secondary school administrators in an effort to better communicate the role of a secondary agricultural education teacher.

Theme 3: Successful teachers who exit the profession blame themselves for leaving.

None of the agricultural education teachers who participated in this study had ill feelings toward the profession. Furthermore, many of the participants indicated that their negative experiences were a result of the monumental program they created. When asked to further explain their experience, some participants described their agricultural education programs as “monsters” [2:213; 5:40; 7:9] and indicated that the only way to remedy the problem was to leave (Table 3).

Table 3

Theme 3: Successful teachers who exit the profession blame themselves for leavingThematic Category Response

Individual Blame “I created that monster so I am to blame.” [5:40]

“Creating a monster cannot be controlled by anybody because that is due to my ambition as a teacher. That was on me.” [4:298]

“Physically, I hurt now as a result of teaching and everything that hurts on me, I can relate back to either clipping livestock, constructing shop projects, no sleep, no time off, no weekends off, etc. That was my fault, nobody else’s.” [4:9]

Exiting as a Result “The only two ways for me to get away from it, when I started reprioritizing my life, was to quit or move, and moving to another program was not even an option because my success would be expected at another school.” [5:92]

“The same thing that made me a good teacher also caused me to leave the profession.” [2:299]

“The only way I could cut down was to leave. It was not worth the headaches I would go through with administration or the parents because I was not doing what I once was.” [7:116]

Agricultural education teachers who possess characteristics identified in this study blame themselves for exiting the profession. They acknowledge that the time spent on the job was their choice and accept responsibility for their decision to leave. One participant said, “Administrators and parents will watch you burn yourself out for their own selfish reasons.” [4:104] As a result, successful teachers will continue to exhaust themselves in their work unless intervention occurs (Hughes, 2001).

It is recommended that fellow agricultural educators, administrators, and state agricultural education staff members familiarize themselves with the characteristics of autonomous, highly successful teachers and identify those who are at risk of prematurely leaving the profession. Once these individuals are recognized, it is important to communicate the concern with these teachers and their risk of attrition. Croom (2003) stated, “Teachers are highly susceptible to burnout when their perception of personal accomplishment is diminished by organizational and social factors” (p. 2). Further, Gagné & Deci (2005) posit it is important to help individuals maintain their autonomy by not increasing their levels of controlled motivation. In other words, reduction of demands placed on teachers is critical to help them maintain their autonomy, preventing dissatisfaction and subsequent exit from the profession. Other mitigation techniques are recommended to help retain quality teachers, such as utilizing an advisory committee and encouraging the teacher to maintain a student-centered program. An advisory committee should be utilized to help diffuse internal and external pressures placed on the agricultural education teacher when determining the most appropriate means to reduce their workload. An advisory committee should also help the teacher evaluate the relevance of current activities and set benchmarks for student learning outcomes. When interacting with at-risk teachers, state staff and teacher educators should stress the need to develop student-centered programs guided by student interests that focus on useful learning activities. Further research should be conducted to help identify additional mitigation techniques that will reduce teacher attrition.

Theme 4: Successful teachers who decide to exit the profession have invested so much of themselves in the program, at such a high cost, that their final decision to leave is irreversible.

The fourth theme was developed as teachers expanded on their experience. All of the participants were still supportive of agricultural education and believed the program was valuable. In addition, most participants would not rule out the thought of teaching agriculture again but stated that the conditions would have to be different for them to return. These statements endorse the findings of Walker et al. (2004) who found that those who stay often experience a reduction in workload. It is important to note however, none of the teachers indicated they would have stayed in the profession at the time they left (Table 4).

Table 4

Theme 4: Successful teachers who decide to exit the profession have invested so much of themselves in the program, at such a high cost, that their final decision to leave is irreversibleThematic Category Response

Reasons to Stay “I cannot say there would be something that could have been done to make me stay.” [1:16]

“No, I do not know what else there would have been to keep me there.” [3:57]

“I am not going to sit and say that there was anything that really would have made me stay, there were too many demands. It takes long hours, it was time consuming, and I had a family that suffered because of it.” [7:20]

Factors Preventing Return

“There is only one level of agricultural education teacher, no matter which way you slice it. There is not a higher position of teaching agriculture where, what I am doing now, I can move into a higher position, and I can step up that way and I can continue to grow myself and get to where I see my career going in the end.” [2:129]

“Because I was successful with what I was doing, other opportunities presented themselves.” [3:114]

“I was unable to advance as an agriculture teacher because the thing the agricultural education profession does not have that other industries do, and it is tied to education, they do not have any opportunity for advancement.” [1:128]

Participants revealed there is nothing that can be done for teachers to remain in the profession after a certain threshold has been reached. It is recommended to promptly identify and intervene among teachers who are at risk of becoming dissatisfied to the point of exiting the profession. After a threshold is reached, teachers who fit the “at risk” profile (see Figure 1) will likely leave the profession without returning. Therefore, those who have influence over the teacher should limit controlled motivation and autonomous activity should be encouraged.

The personal and professional characteristics identified in this study were qualities that also contribute to success in career areas outside of the agricultural education profession. As a

result, many teachers who leave do not return because of increased salaries and more balance regarding personal time. Thus, it is important to intervene in situations where quality teachers experience dissatisfaction because it is unlikely they will return to the profession once they exit.

Many factors lead to teachers exiting the profession as identified in the literature (Boone & Boone, 2009; Chenevey et al, 2008; Torres et al., 2009a; Torres et al., 2009b). While self-autonomy cannot be considered the only reason teachers leave, it is a very large contributor and it was the reason participants in this study exited the profession. These agricultural education teachers fell into a self-created autonomy trap (see Figure 2) and the only way to escape it, for them, was to exit the profession.

Many teachers who possess the personal and professional characteristics, identified from Theme 1, complete tasks because they want to. As a result, they achieve success from their autonomous motivation as indicated by SDT (Gagné & Deci, 2005). The teachers in this study all experienced dissatisfaction due to an increase in controlled motivation as identified by SDT (Gagné & Deci, 2005). When teachers were forced to approach tasks because they had to, caused by demands and expectations of others, they lost their autonomy and became dissatisfied. In their case, no intervention occurred and they reached a level of dissatisfaction that was irreversible.

This phenomenological study investigated the experiences of highly successful agricultural education teachers who left the profession. These findings will contribute to the National Research Agenda’s initiative to identify characteristics of effective teachers. Additional research is needed to investigate personal characteristics of successful teachers and to develop methods of maintaining their level of autonomy to reduce excessive occupational demands that lead to attrition. The agricultural education profession has recognized a demand for high-quality teachers, not simply qualified teachers; therefore, the information in this study should be used to help retain quality teachers and ensure the future success and growth of agricultural education.

Figure 2: Autonomy trap model for highly successful agriculture teachers. Personal and professional characteristics lead to autonomous success as teachers are driven by a want to attitude. Autonomous success transitions to a successful career for some but others experience dissatisfaction as controlled motivation increases and teachers are driven by have to motivation. Unless intervention occurs, dissatisfied teachers reach a threshold thus exiting.

ReferencesBarnes, G., Growe, E., & Shaefer, B. (2007). The cost of teacher turnover in five school districts:

A pilot study. The National Commission on Teaching America’s Future. 1-97. Retrieved from ERIC database. (ED 497176)

Baruch, Y., O’Creevy, M. F., Hind, P., & Vigoda-Gadot, E. (2004). Prosocial behavior and job performance: does the need for control and the need for achievement make a difference? Social Behavior and Personality, 32(4), 399-412.

Boone, H. N., Jr., & Boone, D. A. (2007). Why do agricultural education teachers continue to teach? A qualitative analysis. Proceedings of the AAAE Research Conference, (34), 561-570. Retrieved from http://aaaeonline.org/allconferences.php?show_shat=National

Boone, H. N., Jr., & Boone, D. A. (2009). An assessment of problems faced by high school agricultural education teachers. Journal of Agricultural Education, 50(1), 21-32. doi:10.5032/jae.2009.01021

Chenevey, J. L., Ewing, J. C., & Whittington, M. S. (2008). Teacher burnout and job satisfaction among agricultural education teachers. Journal of Agricultural Education, 49(3), 12-22. doi:10.5032/jae.2008.03012

Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, CA: Sage Publications.

Croom, D. B. (2003). Teacher burnout in agricultural education. Journal of Agricultural Education, 44(2), 1-13. doi:10.5032/jae.2003.02001

Denzin, N. K. (1978). Sociological methods: A sourcebook (2nd ed.). New York: McGraw Hill.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Eductaion’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26, 331-362. doi: 10.1002/job.322

Hall, J. L., Briers, G. E., & Dooley, K. E. (2009). Examining the quality of a secondary agricultural education program at the local level: A qualitative study. Proceedings of the NC AAAE Research Conference, 29-41. Retrieved from http://aaaeonline.org/ uploads/allconferences/29902009-NCAERC-Links2-CLB.pdf

Heath-Camp, B., & Camp, W. G. (1990). Induction experiences and needs of beginning vocational teachers without teacher education backgrounds. Occupational Education Forum, 19(1), 6-16.

Hughes, R. E. (2001). Deciding to leave but staying: Teacher burnout, precursors andturnover. International Journal of Human Resource Management, 12(2), 288-298. doi: 10.1080/09585190010015097

Joerger, R. M., & Boettcher, G. (2000). A description of the nature and impact of teaching events and forms of beginning teacher assistance as experienced by Minnesota agricultural education teachers. Journal of Agricultural Education, 41(4), 106-117. doi:10.5032/jae.2000.04104

Lambert, M. D., Henry, A. L., & Tummons, J. D. (2011). How do early career agriculture teachers talk about their time? Journal of Agricultural Education, 52(3), 50-63. doi:10.5032/jae.2011.03050

Lincoln, Y. S. & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.

Marso, R. N., & Pigge, F. L. (1997). A longitudinal study of persisting and nonpersisting teachers’ academic and personal characteristics. The Journal of Experimental Education.

65 (3), 243-254.

McLean, R. C. & Camp, W. G. (2000). An examination of selected preservice agricultural teacher education programs in the United States. Journal of Agricultural Education, 41(2), 25-35. doi:10.5032/jae.2000.02025

Miller, W. W, Kahler, A. A., & Rheault, K. (1989). Profile of the effective vocational agriculture teacher. Journal of Agricultural Education, 30(2), 33-40. doi:10.5032/jae.1989.02033

Moore, G. E. & Camp, W. G. (1979). Why vocational agriculture teachers leave the profession: A comparison of perceptions. Journal of Agricultural Education, 20(3), 11-18. doi:10.5032/jaetea.1979.03011

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage Publications.

Polkinghorne, D. E. (1989). Phenomenological research methods. In R. S. Valle & S. Halling (Eds.), Existential-phenomenological perspectives in psychology (pp. 41-60). New York:

Plenum Press.

Roberts, T. G., & Dyer, J. E. (2004). Characteristics of effective agriculture teachers. Journal of Agricultural Education, 45(4), 82-95. doi:10.5032/jae.2004.04082

Team Ag Ed. (n.d.). Unmistakable potential: 2005-2006 Annual report on agricultural education. Alexandria, VA: Author.

Torres, R. M., Lambert, M. D., & Lawver, R. G. (2009a). Job stress among secondary ag teachers: An explanatory study. Proceedings of the AAAE Research Conference, 587-600. Retrieved from http://www.aaaeonline.org/uploads/allconferences/AAAE_conf_2009/papers/41.pdf

Torres, R. M., Lambert, M. D., & Tummons, J. D. (2009b). Stress levels of first year teachers as influenced by their perceived ability to manage time. Proceedings of the NC AAAE Research Conference, 272-282. Retrieved from http://aaaeonline.org/uploads/ allconferences/29902009-NCAERC-Links2-CLB.pdf

Torres, R. M., Ulmer, J., & Aschenbrener, M. (2007). Distribution of time usage among agriculture education teachers: A comparison of workloads. Proceedings of the AAAE Research Conference, (34), 571-584. Retrieved from http://aaaeonline.org/ allconferences.php?show_shat=National

Tracy, S. J. (2010). Qualitative quality: Eight “Big-Tent” criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837-851. doi:10.1177/1077800410383121

Walker, W. D., Garton, B. L., & Kitchel, T. J. (2004). Job satisfaction and retention of secondary agriculture teachers. Journal of Agricultural Education, 45(2), 28-38. doi:10.5032/jae.2004.02028

Toward a Framework for Effective Teaching in Agricultural Education: A Multi-State Factor-Analytic and Psychometric Analysis of Effective Teaching

Rebecca G. Lawver, Utah State UniversityBilly R. McKim, Texas A & M University

Amy M. Smith, South Dakota State UniversityMollie S. Aschenbrener, California State University, Chico

Kellie Enns, Colorado State University

AbstractResearch on effective teaching has been conducted in a variety of settings for more than 40 years. A valuable component of a larger sequential mixed method study addressing effective teaching in formal and non-formal agricultural education, this study provides direction for future effective teaching research in secondary agricultural education. Specifically, the 142 behaviors, characteristics, and techniques considered indicative of effective teaching were reassessed to reduce the number of competencies and identify constructs of effective teaching in agricultural education. A total of 1631 secondary agriculture teachers from 37 states surveyed in the fall of 2011 served as the population for this study. Fifty effective teaching competencies in 10 constructs were identified to evaluate effective teaching. The psychometric evaluation of the 10 constructs resulted in Cronbach’s alpha coefficients ranging from .83 to .93, supporting the reliability of the identified constructions. An expert panel then named the constructs that emerged; many of the constructs aligned with those identified through previous teaching effectiveness research. Numerous implications for practice and research resulted from this study, including a proposed framework for assessing effective teaching in agricultural education, which includes the utilization of self-evaluation, formal observation, and student achievement data.

Introduction and FrameworksWithout a doubt, the educational system in the United States has faced tremendous scrutiny in recent years. At all levels—local, state, and national—it appears heightened efforts currently focus on reforming and improving the educational system, particularly in the area of elementary and secondary education. Educational programs targeting reform include initiatives such as Race to the Top, the reauthorization of the Elementary and Secondary Education Act, and No Child Left Behind. Each initiative calls for an increased focus on accountability, assessment, and data collection. While these initiatives may indeed have positive implications for the educational system, Wong and Wong (2010) suggested, “assessment and data will not improve student learning and achievement. All assessment and data do is inform. Effective teaching drives and determines the data to show improvement in the quality of student learning and achievement” (p. 2). According to Hershberg (2005), good instruction has between 15 and 20 times more of an impact on student achievement than other explanatory factors, including family background, income, race and gender. Wong and Wong (2010) stated, “the difference in teacher effectiveness is the single largest factor affecting academic growth of populations of students” (p. 1). As a result, perhaps increased efforts should be made to produce and prepare effective teachers to improve student achievement. Certainly the concept of effective teaching is not new among teachers, administrators, and those involved in teacher education. Cruickshank (1996) stated that renewed interest among researchers to study and understand what constitutes effective teaching began more than 40 years ago. In fact, substantial research has investigated teaching effectiveness (Buchanan, 1997;

Feldman, 1976; Nicholls, 2002; Reid & Johnstone, 1999; Rosenshine & Furst, 1971; Scheeler, 2008; Walls, Nardi, von Minden & Hoffman, 2002; Westmeyer, 1988; Westwood, 2003). However, while effective teaching has been linked to student achievement, student engagement and motivation, and teacher efficacy, it is often difficult to define (Young & Shaw, 1999). Though one may recognize effective teaching when it is observed, it is often difficult to measure and challenging to critically define due to great variation found in the effective teaching literature. One frequently cited attempt to define and evaluate effective teaching occurred nearly four decades ago. Rosenshine and Furst (1971) sought to identify variables related to effective teaching. Their findings revealed five variables which have the greatest impact on student learning. These variables included clarity, variability, enthusiasm, task-oriented and/or businesslike behavior, and student opportunity to learn criterion material (Rosenshine & Furst, 1971). Feldman (1976) found clarity and stimulating student interest highly related to good teaching. Feldman also suggested effective instructors were knowledgeable about their content, prepared and organized for class, and enthusiastic. Furthermore, Reid and Johnstone (1999) identified six components to good teaching, including approachability, clarity, depth of knowledge, interaction, interest, and organization. More recently, Westwood (2003) found effective teachers manage classrooms, provide students with opportunity to learn, maintain academic focus, establish high expectations, demonstrate business-like and work-oriented behaviors, show enthusiasm, maintain task oriented behaviors, are organized and teach in sequential steps, use direct and explicit instructional procedures, provide clear instructions and explanations, employ task-approach strategies, monitor students and adjust instruction to individual needs, re-teach content when necessary, provide frequent student feedback, use a variety of resources, and interact with students. Notwithstanding, Wong and Wong (2010) suggested that many years of research on effective teaching could be summarized in three characteristics: 1) good classroom management; 2) knowledge of how to teach a lesson for student learning and mastery; and 3) positive expectations for student success. Further, they proposed these effective teacher characteristics could be used to form the framework of an effective professional development program to train teachers (Wong & Wong, 2010). Danielson (1996) also developed a framework for teaching, based upon research and experience in the area of teaching and learning. According to Danielson, such a framework answers the following questions: "What does an effective teacher know?" and "What does an accomplished teacher do in the performance of her duties?" (p. 6). Now widely adopted by school districts, teacher preparation programs, and state departments of education, the Danielson framework suggests effective teaching can be categorized into four domains: planning and preparation, classroom environment, instruction, and professional responsibilities.

Specific research in agricultural education has also focused on effective teaching (Dyer, & Osborne, 1996; Johnston & Roberts, 2011; Miller, Kahler, & Rheault, 1989; Newcomb, McCracken, & Warmbrod, 1993; Roberts, Dooley, Harlin, & Murphrey, 2007; Roberts & Dyer, 2004). Miller, Kahler, and Rheault (1988) identified five frequent performance areas demonstrated by effective teachers, including productive teaching behaviors, organized and structured classroom management, positive interpersonal relationships, professional responsibilities, and personal characteristics. Newcomb et al. (1993) identified 13 principles of teaching and learning, including that subject matter to be learned must possess meaning,

organization, and structure, student readiness is a prerequisite for learning, students must be motivated to learn, success is a motivating factor, students are motivated when attempting challenging tasks, students should have knowledge of their learning process, reinforced behaviors are most likely to be learned, reinforcement must immediately follow desired behaviors, directed learning is more effective than undirected learning, students should inquire into the subject matter, problem-oriented approaches to teaching improve learning, student learn what they practice, and effective supervised practice occurs in a functional educational experiment. Additionally, Roberts and Dyer (2004) identified a model of effective teaching for agricultural education, which included instruction, FFA, SAE, building community partnerships, marketing, professional growth/professionalism, program planning, and personal qualities. This model highlights the unique nature of agricultural education. As a result of this “uniqueness”, the evaluation of teacher effectiveness in agricultural education may offer different measurement challenges. In their Handbook on Agricultural Education in Public Schools, Phipps, Osborne, Dyer, and Ball (2008) suggested many practices or elements educational researchers believed to be associated with effective teaching in agricultural education. However, they also noted the need for agriculture teachers to develop additional expertise, potentially due to the various roles and responsibilities expected of agriculture teachers. Others have noted that teachers of agricultural education work in a unique environment, when compared to other secondary teachers (Harper, Weiser, & Armstrong, 1990). The criterion developed to evaluate teacher effectiveness in one setting cannot be assumed to be accurate or appropriate in another when differences in the work environment exist (Borman & Vallon, 1974). Despite the research within the profession, Rosenshine and Furst (1971) have often been credited by agriculture teacher educators as the foremost authority on principle characteristics of effective teachers. While their study is widely cited across many disciplines, it is based upon observations and research conducted in elementary school settings. What implications might this have on the applicability of this research for secondary education, or more specifically, secondary agricultural education? Even in recent work on effective teaching, contextual differences are recognized as a variable of concern. Danielson (1996) offered the following with regard to her framework for teaching; “As educators study the components and consider them within individual context, they can determine which components and elements are applicable and which are not. …Only educators in that setting can make those determinations” (p. 5). Even Rosenshine and Furst (1971) noted the complexities of conducting research on effective teaching, most of which focused on measures of behaviors, characteristics, or techniques used by of effective teachers. In fact, their meta-analysis of effective teaching was introduced by stating, "...we know very little about the relationship between classroom behavior and student gains. [This] is a plea for more research on teaching" (Rosenshine & Furst, 1971, p. 37). With regard to their findings, Rosenshine and Furst (1971) stated,

The results of these studies provide hypotheses upon which to build teacher training models. However, these are not variables, which can be placed in teacher education programs with the assurance that training teachers in these behaviors will enhance student performance. Much more study is needed before these behaviors and their effects will be clarified (p. 43).

Although additional research on effective teaching in general is certainly warranted, the need for a framework of effective teaching specifically focused on agricultural education is even more critical given state and federal attempts to create standards based testing, performance measures,

and accreditation programs to measure and document effectiveness, efficiency, and impact in education (Doerfert, 2011).

Theoretical FrameworksThe theory of psychometrics provided guidance for this study. With the goal of developing a model of effective teaching leading to a self-assessment and observational instrument, efforts were made to establish psychometric soundness with as few items as possible (Ferketich, 1991). “The crux of any measuring instrument or questionnaire is the theoretical definition of the concept that it measures…. Measurement involves explicating specific operations that make a concept quantifiable so that scores reflect the concept’s theoretical definition or conceptual meaning” (Strickland, 2001, p. 3). Psychometrics allows researchers to objectively measure concepts through indirect means, rather than physical characteristics (Nunnally, 1967). Measurements must include rules for assigning numbers to objects to represent quantities of attributes “…to objectify the recording of impressions (e.g., rating scales) and to objectify the analysis of the results” (Nunnally, 1967, p. 486). When proposing a new measure (or revising an existing measure), it is important to clearly qualify and quantify the properties of the concept, thereby, providing the rules of the measure and the mechanism to establish validity and reliability. Empirical analyses are used to create the rules of the measure, i.e., legitimate or standardized measure of a concept or unitary attribute (Nunnally, 1967). Measures of several unitary attributes are then combined to form an overall objective appraisal (Nunnally, 1967). To illustrate this concept, one may form an overall objective appraisal of an individual’s basic math ability by assessing the unitary attributes of his or her ability to add, subtract, multiply, and divide—the sum of the pieces are then used to assess the whole.Appraisals are often guided by two assessment methods commonly noted in the literature, observational assessment and self-assessment, both of which have their strengths and weaknesses namely the objectivity of the assessment protocol. Objectivity is directly related to accurate measures, which require a great deal of construct validation. Construct validation begins with establishing functional relations among important variables or test items (Nunnally, 1967). This study focused on behaviors, characteristics, and techniques associated with effective teaching, largely rooted in teachers’ belief in their ability to create desired outcomes (Tschannen-Moran & Hoy, 2001), because “teachers’ efficacy beliefs also relate to their behavior in the classroom” (p. 783). Hence, the development of variables or test items was guided by Bandura’s theory of self-efficacy (Bandura, 1986). Self-efficacy is believed to influence thought patterns and emotions that drive actions (Bandura, 1986; 1993; 1997). Although teacher efficacy may be difficult to measure (Tschannen-Moran and Hoy, 2001), efficacy studies include, but are not limited to, references of characteristics, beliefs, behaviors, knowledge or competence in specific content areas, and techniques demonstrated by efficacious teachers (Allinder, 1994; Bandura, 1986; 1993; 1997; Berman, McLaughlin, Bass, Pauly & Zellman, 1977; Tschannen-Moran and Hoy, 2001). Such characteristics, beliefs, behaviors, knowledge, and techniques are often referenced when effective teaching is described and/or are listed as components of a framework of teaching.

Purpose and ObjectivesTheory cannot be tested until constructs are clearly identified and operationalized (Gorsuch, 1983). In some cases, theory is not explicit regarding the constructs actually needed; instead they may refer only to an area of interest (Gorsuch, 1983). In such cases, the area itself should be analyzed for appropriate constructs before the research proceeds (Gorsuch, 1983). This task is

often accomplished through factor-analytic and psychometric analyses (Field, 2009). The purpose of this study was to identify and describe behaviors, characteristics, and techniques associated with effective teaching to develop a model of effective teaching, through factor-analytic and psychometric analyses. The results of this study may lead to self-assessment and observational instruments for use in future studies.

The following objectives guided this study:1. Assess the factor-analytic and psychometric properties of effective teaching, based on the

perceptions of secondary agriculture teachers.2. Using the construct outcomes from research objective one, describe secondary agriculture

teachers’ self-perceived ability to perform behaviors, characteristics, and techniques associated with effective teaching.

Method and ResultsThis study is part of a larger study on effective teaching in formal and non-formal environments in agricultural education. Specifically, this study is the quantitative strand of a sequential mixed method study (QUAL → QUAN), as defined by Morse (2003). In sequential mixed designs, “…mixing occurs across chronological phases (QUAL, QUAN) of the study; questions or procedures of one strand emerge from or depend on the previous strand…” (Teddlie & Tashakkori, 2008, p.151). Mixed method developmental studies in the QUAL → QUAN configuration often identify statements or themes through qualitative analysis, followed by statistical analyses (Teddlie & Tashakkori, 2008).The qualitative strand yielded 142 unique competencies identified by 67 in-service agriculture teachers and 51 extension agents, who were asked to describe the behaviors, characteristics, and techniques related to effective teaching in formal and non-formal settings. Following the recommendations of Teddlie and Tashakkori (2008), a closed-ended survey instrument was developed using the 142 competencies to collect quantitative data for the purposes of factor analysis and construct validation.Nunnally (1967) noted the importance of distinguishing between “…statistics concerning the sampling of people and statistics concerning the sampling of items (test items). After measures are developed and then employed in empirical investigations, it is important to employ inferential statistics concerning the sampling of people” (Nunnally, 1967, p. 9). Hence, this study used factor analytic procedures to empirically investigate the behaviors, characteristics, and techniques associated with effective teaching for use in future hypothesis-testing studies.InstrumentationA three-section web-based survey instrument was researcher-developed following the recommendations of Dillman, Smyth, and Christian (2009) and administered using Qualtrics. The first section of the survey instrument asked respondents how many years they had been an educator, how many hours they teach each week (not including preparation time), and how many hours per week they spent preparing to teach. The second section of the survey instrument included 142 statements representing the behaviors, characteristics, and techniques related to effective teaching in formal and non-formal settings, as identified in the qualitative strand of the larger sequential mixed method study. Respondents used a five-point sliding scale (1 = Strongly Disagree to 5 = Strongly Agree) to respond to each statement (see Figure 1). Bipolar anchors were used based on the recommendations of Lam and Klockars (1982), “The researcher interested in obtaining an interval scale may thus be able to eliminate the effort of labeling all points on the scale in favor of labeling only the endpoints” (p. 321). Additionally, the sliding

scale allowed respondents to indicate their level of agreement between points, to the hundredth of a point, providing a more finite response. The third section asked respondents to indicate their gender, year of birth, highest level of education completed, number of hours worked in a typical week, and number of hours working with youth development activities in a typical week.

Figure 1. Qualtrics five-point sliding scale.Content validity of the instrument was assessed in the qualitative strand of the larger sequential mixed method study. Prior to data collection, a panel of 10 experts was asked to assess face validity of the survey instrument. Each member of the panel was considered to be an expert in agriculture teacher education, instrument development, and/or research methods. Because an outcome of this study was to establish a valid and reliable instrument, both were assessed in objective one of this study. To address the concern that respondents will seldom complete a lengthy survey instrument, resulting in item-response bias (Dillman, Sinclair, & Clark, 1993; Galesic & Bosnjak, 2009), the 142 items included in the second section were presented in random order to each respondent using the randomize function in the Qualtrics software. Additionally, data collected in the first section provided a basis of comparison between respondents who started the questionnaire, but did not finish (n = 220), and those who completed the entire questionnaire (n = 1248). Hours typically spent teaching each week (not including preparation time) and hours per week typically spent preparing to teach served as the dependent variables.

A multivariate analysis of variance (MANOVA) was used to compare the variables of interest. A MANOVA is the appropriate analysis when…

…multiple independent and/or dependent variables and the measured variables are likely to be dependent on each other (i.e., to correlate)…. Thus, multivariate analysis allows for the examination of two variables while simultaneously controlling for the influence of the other variables on each of them (Newton & Rudestam, 1999, p. 137).

Box’s test of equality of covariance was not significant (p = .19), indicating that the assumption of equality of covariance was not violated (Field, 2009). The result of the MANOVA was interpreted using Wilks’ lambda (Λ). There was not a significant effect of item-response bias on the dependent variables Λ = .999, F(2, 1465) = .536, p = .585, ηp

2 = .001. PopulationTwo sampling problems are associated with psychometric development, one related to sampling of content and the other related to sampling of people (Nunnally, 1967). Sampling of people is concerned with the generality of findings to populations of persons; whereas, sampling of content is related to the generality of findings to populations of test items (Nunnally, 1967). Because this study was exploratory in nature, focus was placed on the development of psychological measures—the generality of findings to populations of test items—rather than the

ability to infer the results to a population. Therefore, the objectives of this study were not inferential in nature. To maximize response rate, the data collection schedule suggested by Dillman et al. (2009) was followed. Teacher educators and/or state FFA advisors in each state were contacted, requesting a list of names and e-mail contacts of agriculture teachers in their respective states. Twenty-two states provided lists while 15 lists were secured from state websites. Teacher educators from two states responded that they were not willing or able to share the information. Data were collected from current agriculture teachers representing 37 states, between September and November 2011. The accuracy of the lists obtained and the inclusiveness of the lists were unknown; therefore, it was not reasonably possible to access an accurate national frame of agriculture teachers or determine the extent of frame error. Moreover, because the purpose of this study was focused on instrument development and assessing internal validity of the instrument, the 1,631 individuals who provided useable responses were considered the population for this study; thus, all findings are restricted to that population and cannot be inferred beyond.After five points of attempted contact, 1,631 responses were received. A summary of this study’s populations (N = 1,631) characteristics, including number of teachers per state and years of teaching experience, is provided in Table 1.Table 1Characteristics of Secondary Agriculture Teachers

Yrs. Exper.a Yrs. Exper.a

State n M SD State n M SDAlaska 14 12.40 10.50 Nebraska 80 16.37 10.68Arizona 31 13.43 10.99 Nevada 11 13.60 9.73Arkansas 53 14.53 11.10 New Hampshire 7 16.40 14.76California 210 13.27 9.55 New Jersey 11 13.90 10.87Colorado 44 11.44 7.60 North Carolina 83 13.66 11.93Connecticut 18 14.19 10.88 North Dakota 24 17.91 10.85Delaware 25 13.36 8.47 Ohio 121 14.65 10.26Florida 27 19.07 11.88 Oklahoma 27 15.67 12.11Georgia 62 10.48 8.46 Oregon 45 14.33 9.89Hawaii 3 9.67 10.97 Pennsylvania 59 15.95 11.65Idaho 33 15.76 10.13 South Carolina 18 13.29 11.18Illinois 63 14.11 10.38 South Dakota 42 15.10 11.17Indiana 40 12.86 11.66 Texas 195 16.59 11.03Iowa 30 18.13 12.09 Utah 67 11.80 9.55Maine 2 27.00 5.66 Vermont 6 22.83 16.46Maryland 21 19.06 13.13 West Virginia 18 13.38 8.24Michigan 17 13.65 8.91 Wisconsin 35 15.14 9.09Minnesota 45 15.34 8.74 Wyoming 20 16.32 10.49Montana 24 12.80 9.83 Total 1,631 14.59 10.53Note. aMean years of teaching experience.

Data AnalysesData were analyzed using SPSS® version 20.0 for Windows™ platform computers. Respondents who completed less than 50% of the instrument and who completed fewer than 50% of the items composing any factor were eliminated resulting in 1,366 useable responses.

The purpose of research objective one was to assess the factor-analytic and psychometric properties of behaviors, characteristics, and techniques associated with effective teaching based on the perceptions of secondary agriculture teachers. The 142 competencies identified in the qualitative strand of the larger sequential mixed method study were included in the principal component analysis using a varimax rotation. Coefficients with an absolute value less than .45 were suppressed to eliminate double-loadings. Bartlett test of sphericity was significant (p < .001), and Kaiser-Meyer-Olkin’s (KMO) measure of sampling adequacy was .978—values above .90 are considered to be superb (Field, 2009).After removing components of less than three items and components with Cronbach’s alpha coefficients less than .80 (Field, 2009), the remaining 50 items composed the 10-component solution that accounted for 68.99% of the total variance. The 10 components were then treated as independent constructs and served as the dependent variables for the study. Eigenvalues, percentages of variance, cumulative percentages, and Cronbach’s alpha coefficients for each construct are reported in Table 2. Construct loadings from the principal component analysis of the items are reported in Table 3. Table 2Number of Items, Eigenvalues, Percentages of Variance, Cumulative Percentages for Constructs, and Estimates of Reliability

Items Eigenvalue % of variance Cumulative % n Cronbach'sConstruct 1 12 7.179 14.076 14.076 1290 .925Construct 2 8 5.472 10.728 24.804 1325 .913Construct 3 6 3.500 6.862 31.666 1320 .830Construct 4 5 3.242 6.356 38.022 1374 .861Construct 5 4 3.065 6.009 44.031 1369 .875Construct 6 3 2.628 5.154 49.185 1369 .916Construct 7 3 2.593 5.085 54.270 1367 .896Construct 8 3 2.548 4.996 59.266 1369 .881Construct 9 3 2.510 4.922 64.188 1368 .884Construct 10 3 2.450 4.804 68.992 1375 .861

Table 3Construct Loadings from Principal Component Analysis with Varimax RotationItem Loading

Construct 1: Planning & Organizing the Learning EnvironmentI keep lessons organized to help learners learn information .779I provide clear objectives for each lesson .763I keep lessons organized to help learners retain information .760I use objectives to organize lessons .747I present clear objectives .700I follow instructional plans (e.g., lesson or workshop plans) .687I establish goals that include desired outcomes .682I establish a scope for curriculum .663I establish a daily routine .661I create a timeline for curriculum – amount of time for each component .655I provide a clear process for notes .586I define expectations for learning .543

Construct 2: Respect & RapportI show an apparent interest in learners’ lives .809I am concerned about learners’ well-being .806I am compassionate .770I care about learners .744I give attention to all learners .660I care for learners beyond the classroom .594I show compassion toward learners .572I am concerned about learners' success .528

Construct 3: Professional & Ethical ConductI have integrity .808I am trustworthy .773I am honorable .631I dress appropriately .595I demonstrate humility .522I honor the individuality of each learner .483

Construct 4: Instructional FlexibilityI use experiential learning .725I appeal to a variety of learning styles .670I take advantage of opportunities to learn .670I provide a variety of opportunities to learn .619I encourage learner inquiry .616

Construct 5: CollegialityI collaborate with colleagues .806I consider advice from colleagues .775I share resources with colleagues .771I consider constructive criticism from colleagues .743

(Continues)Item Loading

Construct 6: Commitment & Desire to TeachI enjoy teaching .846I love to teach .838I want to teach .830

Construct 7: Student EngagementI allow learners to ask questions .771I encourage learners to ask questions .764I encourage active participation .762

Construct 8: Subject Matter MeaningI make real-life connections to the subject matter .744I help learners understand application of the material in the real-world .697I provide learners with an opportunity to apply subject matter in a practical way .680

Construct 9: Knowledge & ExperienceI have experience with the topic .840

I am knowledgeable of the topic .806I know how to apply topics to the real-world .701

Construct 10: Learner AccommodationsI teach material that matches the learners' ability .801I teach at the learners' level .800I pay attention to learners’ concerns .526I connect with learners .513

Field (2009) noted that individual items should measure the same underlying dimension. In this case, the underlying dimensions are behaviors, characteristics, and techniques associated with effective teaching. Intercorrelations should range between “about .30” to no higher than .80 (Field, 2009, p. 648). “If any variables have lots of correlations below.30 then consider excluding them” (Field, 2009, p. 648). Intercorrelations greater than .80 could indicate issues related to multicolinearity; thus, those items should be removed as well. All 50 items included in the 10 constructs revealed associated correlation scores greater than .30 and less than .80 (see Table 4). Additionally, all constructs should correlate, as they each measure different aspects of the same thing. One bivariate correlation score of .29 existed between constructs 5 and 9. It was determined that one low correlation among 45 acceptable bivariate correlations was not sufficient cause to remove a construct. The associated constructs were then named through a collaborative process, utilizing experts from the previously established panel.

Table 4

Bivariate Correlations Between Constructs

Construct 1 2 3 4 5 6 7 8 9 101 —2 .474 —3 .546 .631 —4 .559 .555 .544 —5 .442 .509 .452 .473 —6 .391 .535 .416 .441 .375 —7 .407 .553 .533 .520 .394 .335 —8 .447 .472 .469 .602 .361 .381 .487 —9 .439 .375 .446 .518 .285 .345 .399 .533 —10 .530 .614 .556 .582 .426 .420 .480 .492 .421 —

The purpose of research objective two was to describe secondary agriculture teachers’ self-perceived ability to perform behaviors, characteristics, and techniques associated with effective teaching. Ability scores of the 1,631 secondary agriculture teachers in this study are proposed as multi-state benchmarks for comparing ability levels in future studies of effective teaching. Summated mean and standard deviation for each construct are reported in Table 5.

Table 5Proposed Benchmark Scores for Comparison in Studies of Agriculture Teachers’ Ability LevelsConstruct M SDPlanning & Organizing the Learning Environment 4.12 0.57Respect & Rapport 4.60 0.43Professional & Ethical Conduct 4.70 0.36Instructional Flexibility 4.41 0.48Collegiality 4.37 0.57Commitment & Desire to Teach 4.58 0.57Student Engagement 4.72 0.39Subject Matter Meaning 4.56 0.47Knowledge & Experience 4.54 0.49Learner Accommodations 4.30 0.51Note. 1 = Strongly Disagree, 5 = Strongly Agree

Conclusions/Implications/RecommendationsAs a result of this study, the 142 effective agriculture teacher competencies identified from the qualitative strand of the sequential mixed method study were reduced through factor-analytical procedures to 50 competencies, representing 10 constructs. Thus, 10 newly identified agriculture teacher effectiveness constructs were generated through psychometric evaluation. Earlier studies focusing on agriculture teacher effectiveness were primarily based upon existing effectiveness research, often rooted in elementary and secondary education. The constructs identified in this study will provide a launching point for future needs assessment studies in teacher effectiveness in agricultural education. Teaching agricultural education is different than teaching other content areas (Harper, Weiser & Armstrong, 1990; Roberts & Dyer, 2004); as a

result of such differences, should agricultural education instructors be evaluated through a different lens (Phipps et al., 2008; Danielson, 1996)? The present research builds on the previous findings of variables related to effective teaching offered by Rosenshine and Furst (1971) which have served agricultural education well, providing the foundation on which teacher-training models could be based. However, this study provides the first step in validating the effective teaching within agricultural education. Additionally, the 50 competencies identified in this study will serve as a comparison for future needs assessments and evaluation of agricultural education teachers. Several implications for current practices in agricultural education are recommended. Following in the footsteps of Rosenshine and Furst (1971), next steps could include the development of a self-evaluation/observation protocol and assessment measures to determine whether or not these competencies truly impact student learning. While this study does not take into account the impact of these teaching characteristics on student learning, an additional recommendation for practice is to measure student learning through the use of end of course exams in agricultural education courses. Utilizing “standardized tests,” or standard measures would enable agricultural educators and proponents of agricultural education to show quantitative evidence of the links between effective teaching and student achievement and/or academic progress. The agricultural education profession would enable holistically measures of effective teaching in agricultural education if agricultural educators were to embrace the use of these three evaluative measures—self-evaluation, formal observations, and student achievement data results.. Thus, a new model for the evaluation of teaching effectiveness in agricultural education is proposed. This proposed triangulation of teacher effectiveness in agricultural education would provide more credible evaluation data for agriculture teachers, local school districts, and national stakeholders in agricultural education. Additionally, there are recommendations for further related research. While the purpose of this study did not include identifying the professional characteristics of the most efficacious agriculture teachers, this is a potential area for further exploration. Future studies should investigate the relationships between effective teaching and teachers’ experience (Huberman, 1989), level of education, and amount of time preparing to teach or lesson planning (Ball, Knobloch, & Hoop, 2007). Additional studies of regional or national scope would certainly help to substantiate the findings of this study. However, as found in this study, obtaining current, accurate frames of secondary agriculture teachers for all states is virtually impossible. Without a formalized system in place to establish and maintain a national database of agriculture teachers, it is unlikely that researchers will be able to conduct national studies without concern of excessive frame and sampling error. It is recommended that an agreement be reached between the AAAE, NAAE, and the National FFA Organization to allow for the development of a national agricultural education directory. Such a directory would allow AAAE members to select valid simple-random samples of secondary agriculture teachers, which is necessary to empirically investigate many of the priorities included in the National Research Agenda (Doerfert, 2011).

ReferencesAllinder, R. M. (1994). The relationship between efficacy and the instructional practices of

special education teachers and consultants. Teacher Education and Special Education, 17, 86–95.

Ball, A. L., Knobloch, N. A., & Hoop, S. (2007). The instructional planning experiences of beginning teachers. Journal of Agricultural Education, 48(2), 56-65. doi: 10.5032/jae.2007.02056

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory.Englewood Cliffs, NJ: Prentice-Hall.

Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning.Educational Psychologist, 28(2), 117–148.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman andCompany.

Berman, P., McLaughlin, M., Bass, G., Pauly, E., & Zellman, G. (1977). Federal programs supporting educational change. Vol. VII: Factors affecting implementation and continuation (Report No. R-1589/7-HEW). Santa Monica, CA: The Rand Corporation (ERIC Document Reproduction Service No.140 432).

Borman, W. C., & Vallon, W. R. (1974). A view of what can happen when behavioral expectation scales are developed in one setting and used in another. Journal of Applied Psychology, 59(2), 197-201.

Buchanan, P. (1997). Inspiring teaching. (J. Roth, Ed.) Bolton, MA: Anker Publishing Company,Inc.

Cruickshank, D.R., (1996). Preparing America’s teachers. Bloomington, IN: Phi Delta Kappa Educational Foundation.

Danielson, C. (1996). Enhancing professional practice: A framework for teaching. Alexandria, VA: Association for Supervision and Curriculum Development.

Dillman, D. A., Sinclair, M. D., & Clark, J. R. (1993). Effects of questionnaire length, respondent-friendly design and a difficult question on response rates for occupant-addressed census mail surveys. Public Opinion Quarterly, 57(3), 289-304. doi: 10.1086/269376

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, Mail, and Mixed-Mode Surveys: The Tailored Design Method (3rd ed.). Hoboken, NJ: Wiley and Sons.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Dyer, J. E., & Osborne, E. W. (1996). Effects of teaching approach on problem solving

abilities of agricultural education students with varying learning styles. Journal ofAgricultural Education, 37(4), 36-43. doi: 10.5032/jae.1996.0403

Feldman, K. (1976). The superior college teacher from the student's view. Research in Higher Education, 5, 243-288.

Ferketich, S. (1991). Focus on psychometrics: Aspects of item analysis. Research in Nursing and Health, 14(2), 165-168.

Field, A. (2009). Discovering statistics using SPSS: And sex and drugs and rock 'n' roll (3rd ed.). Los Angeles: Sage.

Galesic, M., & Bosnjak, M. (2009). Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opinion Quarterly, 73(2), 349-360. doi:10.1093/poq/nfp031

Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates Publishers.

Harper, J.C, Weiser, R., & Armstrong, R. (1990). Factors associated with western region agriculture teachers’ perceptions of teaching effectiveness. Journal of Agricultural Education. 31(4), 22-26. doi: 10.5032/jae.1990.04022

Hershberg, T. (2005). Value-added assessment and systemic reform: A response to the challenges of human capital development value-added assessment and systemic reform. Phi Delta Kappan. 87(4), 276-283.

Huberman, M. (1989). The professional cycle of teachers. Teachers College Record, 91(1), 31-57.

Johnston, T., & Roberts, T.G. (2011). The effect of an interest approach on knowledge, attitudesand engagement of high school agricultural science students. Journal of Agricultural Education, 52 (1), 143-154. doi: 10.5032/jae.2011.01143

Lam, T. C. M., & Klockars, A. J. (1982). Anchor point effects on the equivelence of questionnaire items. Journal of Educational Measurement, 19(4), 317-322.

Miller, W.W., Kahler, A. A., & Rheault, K. (1989). Profile of the effective vocational agriculture teacher. Journal of Agricultural Education, 30(2), 33-40. doi: 10.5032/jae.1989.02033

Morse, J. M. (2003). Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavior research (pp. 189-208). Thousand Oaks, CA: Sage.

Newcomb, L. H., McCracken, J. D., & Warmbrod, J. R. (1993). Methods of teaching agriculture (3rd ed.). Danville, Illinois: Interstate Publishers.

Newton, R. R., & Rudestam, K. E. (1999). Your statistical consultant. Thousand Oaks, CA: Sage.

Nicholls, G. (2002). Developing teaching and learning in higher education. New York: Routledge Falmer.

Nunnally, J. C. (1967). Psychometric theory. New York, NY: McGraw-Hill.

Phipps, L.J., Osborne, E.W., Dyer, J.E., & Ball, A. (2008). Handbook on agricultural education in public schools, sixth edition. Clifton Park, NY: Thomson Delmar Learning.

Reid, D. J.,& Johnstone, M. (1999). Improving teaching in higher education: Student and teacher perspectives. Educational Studies, 269-281.

Roberts, T. G., Dooley, K., Harlin, J., & Murphrey, T. (2007). Competencies and traits ofsuccessful agricultural science teachers, Journal of Career and Technical Education,22(2) 6-17.

Roberts, T. G & Dyer, J. E. (2004). Characteristics of effective agriculture teachers. Journal of Agricultural Education, 45(4), 82-95. doi: 10.5032/jae.2004.04082

Rosenshine, B. & Furst, N. (1971). Research on teacher performance criteria. In O. S. (Ed.), Research in Teacher Education (pp. 37-72). Englewood Cliffs, NJ: Prentice Hall.

Scheeler, M. (2008). Generalizing effective teaching skills: The missing link in teacher preparation. Journal of Behavioral Education, 17, 145-159.

Strickland, O.L. (2001). Editorial: An instrument’s conceptual base: Its link to theory. Journal ofNursing Measurement, 9, 3-4.

Teddlie, C., & Tashakkori, A. (2008). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences. Thousand Oaks, CA: Sage Publications.

Tschannen-Moran, M. & Woolfolk Hoy, A. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17, 783-805.

Walls, R., Nardi, A., von Minden, A. & Hoffman, N. (2002). The characteristics of effective and ineffective teachers. Teacher Education Quarterly, 29(1), 30-48.

Westmeyer, P. (1988). Effective teaching in adult and higher education. Springfield, Illinois: Charles C. Thomas.

Westwood, P. (2003) Commonsense methods for children with special educationalneeds: Strategies for the regular classroom. New York, NY: Routledge Falmer.

Wong, H. K. & Wong, R. T. (2010). Developing and retaining effective teachers and principals. Retrieved from: http://www.oswego.edu/Documents/project_smart/Summer%20Institute/Developing%20and%20Retaining.pdf

Young, S., & Shaw, D. G. (1999). Profiles of effective college and university teachers. The Journal of Higher Education, 70(6), 670-686.

An Experimental Study of Critical Reflection

Misty D. Lambert, Oregon State UniversityRobert M. Torres, University of Arizona

Abstract

The purpose of this experimental study was to describe the reflective thinking level of students both overall as well as to compare the effect of reflective feedback conference on students’ reflective thought by experimental group. All students enrolled in a Methods course participated in the study and were assigned to either a treatment or placebo group. All students had an instructor-led feedback conference with the treatment group receiving a reflective conference and the placebo group receiving feedback without the opportunity to reflect. Both groups then completed an instrument with 3 reflective questions. This experience was repeated three times during the semester. Most of the students’ responses were either technical or descriptive in nature. None of the students were critically reflective. Overall, students who received the treatment showed no difference during the first two rounds, but gave a more critical answer during their third and final reflection. Cohen’s d showed a small effect size.

Introduction and Review of Literature

Teaching is a process that requires constant decision making before, during and after class (Colton & Sparks-Langer, 1993; Costa, 1995). One theory proposed to help teachers solve classroom problems that has emerged over the last century is reflective thought (Rodgers, 2002). Dewey (1910) is credited as the originator of the theory, but his work has been built upon by Perry (1970), VanManen (1977), followed by Schön (1983), King and Kitchener (1994), Brookfield (1995) and, most recently, Mezirow (1998).

Collier (1999) suggested reflectivity in novice teachers became a focus in the 1970s. However, it wasn’t until the late 1980’s that teacher education programs began making reflection one of the program’s educational goals (Zeichner & Liston, 1987). In order to write about reflection in a scholarly way, a definition must be chosen as the foundation of the study. Reflection, as it relates to teaching, is being defined in this study as “deliberate thinking about action with a view to its improvement” (Hatton & Smith, 1995, p. 40).

According to National Council for Accreditation of Teacher Education’s (NCATE) vision, the institution is responsible for encouraging “reflective practice and continuous improvement” while a teacher who graduates from an accredited teacher education program should be able to “reflect on practice and act on feedback” (Professional Standards, 2008, p. 4). Reflective thinking is important because teachers in the classroom need to solve their own pedagogical issues. True reflection is not an innate skill, but rather, must be learned.

Cruickshank (1984) stated that in preservice teacher education programs, an “opportunity should be provided for controlled teaching with subsequent examination of it in order to help teachers to develop good habits of thought and to become students of teaching” (p. 108). These opportunities are called by various names including clinical teaching experiences, mini-

teachings, and microteachings. According to McIntyre (1991), 91% of teacher education programs use some variation of microteaching in the curriculum. While the exact protocols may have changed from Allen and Eve’s (1968) definition, the basic idea is to give students a chance to connect theory to practice before entering the public school classroom (Cruickshank, 1996). Many times teaching experiences come with self-reflection or peer feedback and occasionally with a feedback conference (Brent, Wheatley, & Thomson, 1996).

Hatton and Smith (1995) reported that “a powerful strategy for fostering reflective thinking is to engage with another person in a way which encourages talking with, questioning, even confronting, the trusted other, in order to examine planning for teaching, implementation and its evaluation” (p. 41). When teachers reflect, either with a supervisor or their peers, they have deeper thoughts about the art and science of teaching, often making changes to their knowledge and practice (Parsons & Stephenson, 2005).

The idea behind one-on-one conferencing with teachers is not new. Usually the conferences are conducted by a supervisor. For a classroom teacher that person might be a principal while, at the preservice level, the course instructor, and/or designated teaching assistant serve in this role. Brent et al. (1996) made the case that one-on-one feedback conferences are time consuming for students and instructors and, therefore, many programs are tempted to allow students to microteach and then rely solely on peer feedback and a self-evaluation of their teaching. However, evidence to suggest that these feedback conferences actually accomplish the desired outcomes is basically non-existent.

Simmons and Schuette (1988) caution that a feedback conference intended to promote reflection should not to be used as an assessment of the students. Many programs require students to view a videotape of their teaching. However, Van Es and Sharin (2002) noted that teachers who observed video did not always notice what was important and could not necessarily link what they were seeing to larger principles and concepts of teaching and learning.

There has been some previous research. In an analysis of reflective thinking at the University of Sydney, Hatton and Smith (1995) analyzed students over four years in a secondary teacher education program. Almost everyone was reflective on some level, with 60-70% able to be descriptive, often using those statements to set up higher levels later in their reflections. They also had 50% of their students show multiple perspectives. Within agricultural education, research on reflective thinking has been limited. In a qualitative study of the level of reflective thought demonstrated by preservice teachers in a methods course, many students were shown to reflect mostly on the students as well as planning for change while reflecting in the lower levels especially early in the study (DeLay, Washburn, & Ball, 2008).

Bates, Ramirez, and Drits (2009) emphasized that little is understood about how reflective thinking is fostered in preservice teachers. This is a result of the fact that there is not a clear definition of critical reflection and the concept is difficult to operationalize into quantitative questionnaires and research instruments (Hatton & Smith, 1995). Further complicating the challenge, no widely accepted questionnaire exists for measuring reflective thinking (Kember et al., 2000). Ward and McCotter (2004) warned that we are at risk of devaluing reflective skills in teachers simply because it is a process difficult to measure, synthesize, and report.

Framework for the Study

The work of Hatton and Smith is combined with Kolb’s (1984) learning cycle and the principles of cognitive coaching created by Costa and Garmston (2002) to serve as the framework for this study. Kolb (1984) established the learning cycle describing how individuals have a direct experience upon which they can reflect. From these reflections, they draw rational conclusions and gain insight into whether or not what they did is, or is not, consistent with what they know which, in turn, leads to new knowledge. Using this information, a person moves forward acting upon experience and integrating the new knowledge gained, beginning the process again (Kolb, 1984). A student in a teaching methods course who is given the opportunity to teach, receive feedback, and self-evaluate three lessons may complete this cycle numerous times. Reflection is cyclical in nature because a successful solution often reveals another problem and the process begins again (Copeland, Birmingham, De la Cruz, & Lewin, 1993).

Instructor-led feedback conferences provided students with information on their performance and progress. Cognitive Coaching, designed by Costa and Garmston (2002), offers a guide to this process. Cognitive Coaching enhances a teacher’s perceptions, decision-making skills, and intellectual abilities, and in turn, improve student learning. In practice, instructors asked questions of the students to guide their thoughts through Costa and Garmston’s process. A pre-conference offered the chance to clarify goals and select teaching strategies. The second phase is teaching where the student delivers their lesson and the coaches observe and gather data for feedback. The third phase, reflecting, involves a post-conference where coaches summarize their impressions, and compare plans with performance. The final phase, applying, allows for the teacher to prescribe application and refinements as well as allows for a reflection on the coaching received (Glanz & Sullivan, 2000).

Researchers interpreted students’ responses to questions using four of Hatton and Smith’s (1995) five hierarchical levels of reflection: Technical, Descriptive, Dialogic and Critical. Students can move most easily from listing their actions – technical – to explaining reasons why, or descriptive reflection, and later developing the ability to examine why things occurred, or dialogic reflection (Hatton & Smith, 1995). Within these two levels, subgroups can be made based on whether the students reach that level with one topic or can reflect at that level in a multifocal way. The fourth level is critical reflection, considered to be the highest form of reflection (Hatton & Smith, 1995; Raelin, 2001). The last level was not used in this study because it involves using the other levels of reflection while acting and was not observable from post-activity written reflections. One level of this hierarchy is no more desirable than another, but rather, a teacher should be able to use many levels (DeLay et al., 2008).

Purpose and Research Objectives

This research fits the 2011-2015 National Research Agenda’s Priority Area 4: Meaningful Engaged Learning in all Environments (Doerfert, 2011). The literature indicates it is important for preservice teachers to begin developing their reflective ability in order to persist in their future classrooms. Many preservice programs attempt to facilitate reflective thought among their students through the use of reflective feedback conferences following a microteaching

experience. Feedback conferences require a notably large time investment by both instructors and students. The question remains, does this one-on-one conference facilitate reflective thinking among preservice teachers? The purpose of the study was to describe the impact of instructor-led reflective feedback conferences on the level of reflective thought among senior-level students enrolled in a teaching methods course in Agricultural Education. The following research questions and hypotheses guided this study:

1. What is the reflective thinking level of students across the three Clinical Teaching Experiences?

2. What is the effect of the reflective feedback conference on students’ reflective thought by experimental group?

Null Hypotheses:H0A-C: There will be no difference in composite reflective scores for Clinical Teaching

Experiences (1-3) between the students who had a reflective feedback conference and those who have not.

H0D-F: There will be no difference in critical reflective scores for Clinical Teaching Experiences (1-3) between the students who had a reflective feedback conference and those who have not.

Methods and Procedures

The design for this study was post-test only comparison group design. The term comparison group was chosen instead of control group because the non-treatment group was receiving a placebo feedback conference and the term control group implies that the non-treatment group was receiving no treatment at all. Campbell and Stanley (1963) stated that the pre-test is not essential to a true experimental design.

The accessible population was senior level students enrolled in a teaching methods course in Agricultural Education during fall of 2009 (n = 28) at University of Missouri. The course required completion of three 25-minute Clinical Teaching Experiences, or CTEs. The first CTE required the students to complete a demonstration. The second CTE followed a “stand and deliver” format starting with an interest approach followed by a presentation of learning content. For the third CTE, students facilitated an application of learning. Twenty five minutes was the selected time frame to equate length of teaching experience among all students given the time allocated to the lab sessions.

The dependent variable in this study was the level of reflective thinking exhibited by students. Reflective thinking was measured by coding student responses to open-ended questions using the Hatton and Smith (1995) framework. The independent variable in this study was the instructor-led feedback conference. Students were randomly assigned to either the treatment or placebo group. An additional randomization determined the course instructor who would facilitate the feedback conference. A normalizing session was held among the three feedback conference facilitators prior to each round. Strict protocols were developed and practiced to maximize consistency and inter/intra-rater reliability between and among the three conference sessions.

Internal threats to the experimental design were addressed through the use of control groups and random assignment (Ary, Jacobs, & Sorensen, 2010). The experimenter effect was minimized in this study by having multiple trained individuals facilitate both the treatment and placebo feedback conferences. To control for possible subject effects, the placebo conference was put into place and held for 30 minutes similar to the treatment conference. This kept both groups engaging in a perceived equal treatment while also allowing the researchers to deemphasizing the fact that an experiment was being performed (Ary et al., 2010).

InstrumentationBasic descriptive data (age, sex, cumulative GPA, and program emphasis) were collected

from student academic records. An instrument was used to collect the reflective data. This paper instrument was developed from questions compiled using Costa and Garmston (2002) and Pultorak (1993). The questions were narrowed down by a panel of experts (N = 5) who also ensured face and content validity. The instrument contained three structured, open ended questions which were identified to generate various levels of reflection. Several studies have determined that writing can be analyzed to determine level of reflectivity (Kember et al., 1999; Litke, 2002; Wong, Kember, Chung & Yan, 1995).

Due to the open-ended nature of this instrument, no measures of item reliability were required. A scoring rubric was developed from a synthesis of the literature to guide the scoring process. To establish reliability, all instruments were coded after data collection was completed. Two months later, all instruments were re-coded by the same researcher and data were then compared using a Pearson Product Moment Correlation. The intra-rater reliability for the answers to Question One was .96, for Question Two was .97, and for Question Three was .99.

Data CollectionThe data collection process was consistent for all participants, except as it pertained to

the treatment during the feedback conference. First, students completed their assigned lesson plans and submitted them to an instructor one week prior to the presentation. This was graded and returned to the students. Students taught the lesson while their peers role-played as secondary students. The lesson was also video recorded and provided to the student for purposes of self-reflection. Within days of completion, each student scheduled a follow-up feedback conference with their assigned instructor.

Students brought to the conference a self-critique containing a list of perceived teaching positives and negatives from their lesson. The conference lasted approximately 30 minutes and followed either the facilitation protocol for the reflective instructor-led feedback conference or the facilitation protocol for the instructor-led placebo feedback conference. The reflective protocol (treatment) was created to spiral the thought process of the participants into higher levels of reflective thought. Interviews, discussions and dialogues were carefully constructed to force participants into higher levels of reflection (Whipp, 2003). The placebo group received a non-reflective instructor-led feedback conference which focused on a technical aspect of the course objectives (i.e., lesson planning, interest approaches, choosing instructional methods), but did not address teaching ability.

Students were given the paper copy data collection instrument upon completion of their feedback conference. Students were informed to hand write their response to the open ended questions. The instructions also indicated that students should perform their reflections in close proximity to the time of the instructor-led feedback conference. To convey the importance of completing the reflection form, students were told their final grade for each CTE would be withheld until their forms were returned. Their responses did not impact their grade, but submitting the form was the final step in receiving their grade. This resulted in all forms being submitted within a week of the conference so non-response error was not a concern.

Question One was “Do you think your lesson was successful? Why or why not?” and Question Two was “What alternative teaching methods could you have used on this lesson and how might these have improved the learning process for students, collectively or individually?” For each response to Question One and Question Two, the researcher carefully read to determine the level of reflection expressed. Each response was rated according to the highest level of reflection achieved for that question, using a method established by Kember et al. (2001). At the conclusion of the scoring process, each student received four data points for reflection. Each student received a reflection score for Question One and Question Two, and a composite score (Question One plus Question Two). Additionally, each student received a critical reflection score for Question Three. Question Three was “What moral and/or ethical concerns occurred / could occur as a result of the lesson. Justify your answer.”

The scoring process for Questions One and Two were the same. The rubric contained seven levels of reflection that were hierarchical in nature. Students’ level of reflection was scored on a scale from 0-7; 0 = no response, 1 = non-reflective technical response, 3 = descriptive reflection with a singular focus, 4 = descriptive response with multiple perspectives, 5 = dialogic with a singular focus, 6 = dialogic with multiple perspectives, and 7 = critically reflective. A score of 2 was intentionally left out of the initial scoring process to simplify the calculation of composite scores. The researcher used categories of yes, no, or no response to score students ability to be critically reflective. Composite scores were calculated by summing the scores received for questions 1 and 2. A sum of 0 meant data were missing for both questions. A sum of 1 or 2 meant the subject was overall non-reflective in his/her responses. A composite score between 3 and 12 rated reflective, but not critically reflective. Critical reflection was represented only by achieving a composite score of 13 or 14. For data analysis, alpha level was established a priori at .10 to achieve the desired power with the small sample size.

Findings

Objective One: Reflective ThinkingObjective One sought to describe the reflective thinking level of students. Table 1

reports the reflective thinking scores given on Question One across the three Clinical Teaching Experiences (CTE) and overall. The first question stated “Do you think your lesson was successful? Why or why not?” CTE 1 had 16 (57.14%) responses that scored a 1 and were, therefore, technical in their reflection. Twelve (42.86%) students gave a descriptive reflection earning a score of 3 or 4. CTE 2 had 13 (46.43%) responses that scored in the technical category and 15 students (53.57%) who scored in the descriptive category. CTE 3 had 15 (53.57%) responses that were technically reflective and 17 (47.62%) responses that were descriptive in

nature. Overall, there were 44 (52.38%) technical responses, 32 (38.10%) singular descriptive responses, and eight (9.52%) multifaceted descriptive responses. There were no students who earned a reflective thinking score in the dialogic or critical categories.

Table 1 Reflective Thinking Scores on Question One by CTE and Overall (n = 28)Reflective Thinking Scorea

Clinical Teaching ExperienceOverall1 2 3

f % f % f % f %0 0 0.00 0 0.00 0 0.00 0 0.001 16 57.14 13 46.43 15 53.57 44 52.383 11 39.29 10 35.71 11 39.29 32 38.104 1 3.57 5 17.86 2 7.14 8 9.525 0 0.00 0 0.00 0 0.00 0 0.006 0 0.00 0 0.00 0 0.00 0 0.007 0 0.00 0 0.00 0 0.00 0 0.00

a 0 = no response, 1 = technical, 3 = descriptive (singular), 4 = descriptive (multifaceted), 5 = dialogic (singular), 6 = dialogic (multifaceted), 7 = critical

Table 2 reports the reflective thinking scores for Question Two. Question Two stated “What alternative teaching methods could you have used on this lesson and how might these have improved the learning process for students, collectively or individually?” CTE 1 had 12 (42.86%) responses that scored a 1, making them non-reflective. There were 16 (57.14%) descriptive responses. Clinical Teaching Experience 2 had 11 (39.29%) technical responses, nine (32.14%) singular descriptive responses that scored a 3, and eight (28.57%) multifocal descriptive responses. CTE 3 had 11 (39.29%) technical responses, 12 (42.86%) responses that scored a 3, and five (17.86%) responses that scored a 4, making those students descriptive in their reflection. Overall, there were 34 (40.48%) technical responses, 30 (35.71%) singular descriptive responses, and 20 (23.81%) multifocal descriptive responses. No responses were scored as dialogic or critically reflective.

Table 2Reflective Thinking Scores on Question Two by CTE and Overall (n = 28)Reflective Thinking Scorea

Clinical Teaching ExperienceOverall1 2 3

f % f % f % f %0 0 0.00 0 0.00 0 0.00 0 0.001 12 42.86 11 39.29 11 39.29 34 40.483 9 32.14 9 32.14 12 42.86 30 35.714 7 25.00 8 28.57 5 17.86 20 23.815 0 0.00 0 0.00 0 0.00 0 0.006 0 0.00 0 0.00 0 0.00 0 0.007 0 0.00 0 0.00 0 0.00 0 0.00

a 0 = no response, 1 = technical, 3 = descriptive (singular), 4 = descriptive (multifaceted), 5 = dialogic (singular), 6 = dialogic (multifaceted), 7 = critical

The reflective thinking score for Question One were added to the reflective thinking score for the Question Two to create a composite reflection score for each student. For purposes of creating categories, the highest level of reflection attained in answering either question was used. For example, a student with a technical response on Question One and a descriptive response on Question Two would have scored a 4 and would be categorized as descriptive. These data and are reported by CTE and overall (see Table 3). Overall, there were 22 (26.19%) students who were non-reflective, and 56 (73.81%) of the students who were reflective, but not critically reflective.

Table 3Composite Reflection Scores by Clinical Teaching Experience and Overall (n = 28)Reflective Thinking Scorea

Clinical Teaching ExperienceOverall1 2 3

f % f % f % f %0 0 0.00 0 0.00 0 0.00 0 0.001 0 0.00 0 0.00 0 0.00 0 0.002 9 32.14 5 17.86 8 28.57 22 26.193 0 0.00 0 0.00 0 0.00 0 0.004 8 28.57 10 35.71 9 32.14 27 32.145 2 7.14 4 14.29 1 3.57 7 8.336 4 14.29 4 14.29 5 17.86 13 15.487 4 14.29 1 3.57 4 14.29 9 10.718 1 3.57 4 14.29 1 3.57 6 7.149 0 0.00 0 0.00 0 0.00 0 0.0010 0 0.00 0 0.00 0 0.00 0 0.0011 0 0.00 0 0.00 0 0.00 0 0.0012 0 0.00 0 0.00 0 0.00 0 0.0013 0 0.00 0 0.00 0 0.00 0 0.0014 0 0.00 0 0.00 0 0.00 0 0.00

a 0 = missing, 1-2 = non-reflective, 3- 12 = reflective, 13-14 = critically reflective

The composite reflection scores across CTE are displayed in Table 4. For CTE 1, the average score was 4.29 (SD = 1.96). For CTE 2, the average composite reflection score was 4.75 (SD = 1.90), and for CTE 3 the average score was 4.39 (SD = 1.93).

Table 4Composite Reflection Scores by Clinical Teaching Experience (n = 28)

Reflective Thinking Score

Clinical Teaching Experience1 2 3

M SD M SD M SDComposite 4.29 1.96 4.75 1.90 4.39 1.93

Table 5 reports the critical reflection scores by CTE and overall. Question Three stated “What moral and/or ethical concerns occurred / could occur as a result of the lesson. Justify your answer.” These answers could be placed into one of two categories: either the student was critically reflective or not. There was also a category for no response. For CTE 1, every student (f = 28, 100%) received a score of 1, placing them in the not critically reflective category. This

was true for CTE 2 and CTE 3, except that two (7.14%) students did not respond during CTE 2 and one (3.57%) student did not respond during CTE 3. Overall, every student who responded (f = 81, 96.43%) scored as being not critically reflective.

Table 5Categorical Critical Reflective Score by Clinical Teaching Experience (n = 28)

ReflectiveThinking Level

Clinical Teaching ExperienceOverall1 2 3

f % f % f % f %No response 0 0.00 2 7.14 1 3.57 3 3.57Not critically reflective 28 100.00 26 92.86 27 96.43 81 96.43Critically reflective 0 0.00 0 0.00 0 0.00 0 0.00

Some students responded to the Question Three, but were not reflective while other students gave responses which were much more complex yet not still critically reflective. Table 6 displays the scoring of the same responses to Question Three with the addition of a category for responses judged as “approaching critically reflective”. There were eight (28.57%) responses during CTE 1, six (21.43%) responses during CTE 2, and five (17.85%) responses during CTE 3 categorized as approaching critically reflective. Overall, 19 (22.62%) of the responses were approaching critically reflective.

Table 6Categorical Composite Reflection Scores with Addition of “Approaching Critically Reflective” Category across Clinical Teaching Experiences (n = 28)

ReflectiveThinking Level

Clinical Teaching ExperienceOverall1 2 3

f % f % f % f %No response 0 0.00 2 7.14 1 3.57 3 3.57Not Critically Reflective 20 71.43 20 71.43 22 78.57 62 73.81Approaching Critically Reflective 8 28.57 6 21.43 5 17.86 19 22.62Critically Reflective 0 0.00 0 0.00 0 0.00 0 0.00

Objective two sought to compare the effect of reflective feedback conference on students’ reflective thought by experimental group (placebo and treatment). Data for the first three hypotheses appears in Table 7. For CTE 1, the placebo group averaged a 4.43 (SD = 1.99) and the treatment group averaged a 4.14 (SD = 1.99). An independent samples t-test indicated this difference was not statistically significant (t = 0.38, p >.10). A Cohen’s d was calculated and showed a negligible effect size. Null hypothesis A which stated, “There will be no difference in composite reflective scores for Clinical Teaching Experience 1 between the students who have had a reflective feedback conference and those who have not” was retained.

Table 7Composite Reflective Scores by Experimental Group across Clinical Teaching Experiences (n = 28)Clinical Placebo (n = 14) Treatment (n = 14) t-value p-value Cohen’s d

Teaching Experience

M SD M SD

1 4.43 1.99 4.14 1.99 0.38 .71 0.152 5.14 1.46 4.36 2.24 1.10 .28 0.433 4.21 1.89 4.57 2.03 -0.48 .63 0.19

*p < .10

For CTE 2, the placebo group had an average score of 5.14 (SD = 1.46) while the treatment group had a 4.36 (SD = 2.24). An independent samples t-test indicated that this difference was not statistically significant (t = 1.10, p >.10). Cohen’s d showed this held a small effect size. Therefore, null hypothesis B which stated “There will be no difference in composite reflective scores for CTE 2 between the students who have had a reflective feedback conference and those who have not” was retained (Table 7).

For CTE 3, the placebo group had an average score of 4.21 (SD = 1.89) while the treatment group had an average score of 4.57 (SD = 2.03). An independent samples t-test indicated that this difference was not statistically significant (t = -0.48, p >.10) and Cohen’s d showed a negligible effect size. Therefore, null hypothesis C which stated “There will be no difference in composite reflective scores for CTE 3 between the students who have had a reflective feedback conference and those who have not” was retained. Data for hypotheses D-F are displayed in Table 8. For both treatment and placebo, every student scored a one during CTE 1. There was no variance, and therefore, no t-value was calculated. Null hypothesis D which stated “There will be no difference in critical reflective scores for CTE 1 between the students who have had a reflective feedback conference and those who have not” must be retained.

Table 8Critical Reflective Scores by Experimental Group across Clinical Teaching Experiences (n = 28)Clinical Teaching Experience

Placebo (n =14) Treatment (n = 14)

t-value p-value Cohen’s dM SD M SD

1 1.00 0.00 1.00 0.00 - - -2 0.93 0.27 0.93 0.27 0.00 1.00 0.003 0.93 0.27 1.00 0.00 -1.00 -.07* 0.38

*p < .10

For CTE 2, the placebo group had an average score of 0.93 (SD = 0.27) as did the treatment group. This lack of variance meant that the t-value is O. Therefore null hypothesis E which stated “There will be no difference in critical reflective scores for CTE 2 between the students who have had a reflective feedback conference and those who have not” was retained. For CTE 3, the placebo group had an average score of 0.93 (SD = 0.27) while the treatment group had an average score of 1.00 (SD = 0.00). An independent samples t-test was calculated and found a statistically significant difference (t = -1.00, p < .10). Cohen’s d showed a small effect size. Therefore, the null was rejected in favor of the alternative which stated “There will be a difference in critical reflective scores for CTE 3 between students who have had a reflective feedback conference and those who have not” (Table 8).

Conclusions, Implications & Recommendations

Students are reflecting at low levels. Overall for Question One, more than half of the responses are technical in nature and less than 10% are descriptive in a multifocal way. The results for Question Two mirror the results from Question One. Overall, the largest portion of responses are technical in nature while just over one third of responses were singular descriptive in focus. Should teacher educators expect senior level students to be reflecting above the technical level? Hatton and Smith (1995) contend that technical reflection is a crucial aspect for any novice professional as it provides the foundation for the other forms of reflection.

Students were most reflective during the second Clinical Teaching Experience. Students were less technical and more multifocal descriptive. Logic would dictate that the highest levels of reflection would occur in the last round because the students would be developing their reflective skills over time. Why would the second round show the highest level of reflection and not the last round? Perhaps the answer lies in nature of the Clinical Teaching Experiences themselves. CTE 1 had students performing a demonstration while CTE 3 had students facilitating an activity. CTE 2 was the most the classic version of teaching beginning with an interest approach and then delivering instruction. This is the experience which would most clearly connect to the rest of their traditional teacher preparation courses. Perhaps it was easier for students to connect to prior knowledge, thus more conducive to generating reflection.

None of the students in this study were able to give a dialogic or critically reflective response. Is this surprising? The literature is mixed on the length of time it takes for a person to reach the various levels of experience, but many researchers indicate a large amount of time is involved (Calderhead & Gates, 1993). Students in the experimental group had only three half-hour session of practice while students in the placebo group had no practice in reflection. Wildman and Niles (1987) indicated students need about 60 hours to be able to reflect. Hatton and Smith (1995) made the argument that while reflection may take a long time to develop and may not fully develop during a teacher development program, the demanding world of teacher work may prevent reflection from developing during practice unless the basic techniques are provided during teacher preparation.

Isn’t this where teacher educators should expect these students to be based on the literature? What are the implications? Teacher educators must meet the students where they are. These senior level students were at the end of their teacher preparation program, with only student teaching remaining. At this point, one-quarter of the students were still only reflecting at the technical level. Teacher educators must be aware of this as they work with these students. Perhaps it is developmental (Copeland et al., 1993). Are they not cognitively able to reflect at a higher level at this age? Perhaps some of students in the room are better able to visualize themselves less as students and more as a teacher, thus reflecting in a deeper way.

When the question was set up to generate a critical response, students could not provide one. Some students were closer than others, but, overall, no student received a critically reflective score. Again, this is not surprising. Hatton and Smith (1995) found that college students reaching critical reflection was not a common occurrence. What is surprising is that

students just stopped attempting to answer the question. Why? Perhaps thinking is a novel experience for some students. Perhaps students decided that because their responses were not graded, they did not need to generate the effort. Perhaps it was a question of relevance. If the students did not care about the answer to the question they may not answer the question. Research indicates that students need to see reflection modeled and perhaps they were not seeing critical reflection modeled. Since they did not get a grade or even feedback on their written responses, perhaps the students did not see the instrument as offering an opportunity for growth.

Both Calderhead (1989) and MacKinnon (1987) argued that novice teachers cannot be expected to reflect on abstract concepts not yet experienced. To get students to reflect, there must be experiences to which they can connect their reflections. While this study attempted to get students to reflect on their 25-minute classroom teaching experience, perhaps this was not enough experience in which to imbed their reflection. Students need to receive some form of feedback on their reflections. Feedback is critical in the process of knowledge acquisition (Mory, 2004). This feedback should not be in the form of grades (Simmons & Schuette, 1988). The feedback should affirm students in their progress and suggest questions to take their reflection to a higher level, and possibly attempt to answer, or affirm the validity of, questions that are raised. While there would be benefits to self-reflection and/or peer feedback alone, the instructors’ expertise is needed to maximize the benefit of the experience (Cruickshank & Metcalf, 1993).

The treatment made no difference in students’ composite reflection score. Even with a more lenient alpha level, independent samples t-tests find no significant difference. Perhaps there are truly no differences or perhaps the instrument is not sensitive enough to detect the differences. There is no variance for critical reflection during the first or second clinical teaching experiences, but a significant difference is found between the groups on critical reflection scores during CTE 3 with the treatment group being more reflective. A Cohen’s d shows a small effect size. Perhaps the experiment was not long enough to detect differences as they just started to show in the third round. Will this small amount of success continue across student teaching if reflection is not regularly supported by the cooperating teacher?

For 5 of the 6 areas examined, no differences were detected. Korthagen (1988) argued that some students are ready to learn in a reflective program while others are not. Perhaps the differences are less about the treatment and more about student differences. It is expected that, based on the literature, students who are receiving cognitive coaching from their reflective feedback conferences should be reflecting at higher levels each round. The fact that they are able to reflect verbally on the question during the instructor-led feedback conference should allow them to reflect on paper at a higher level than those students who did not have that experience. Bates et al. (2009) had a study where some students had supervisory interviews and others did not but that study found no difference found between the group with the intensive involvement of a reflective supervisor and the group which journaled on their own. Bates et al. (2009) proposed that this may have occurred because students in both groups of the study reported talking to others about their teaching and therefore, no one was truly reflecting in isolation. Perhaps this occurred in the present study as well. While the results of this study suggest that feedback conferences fail to contribute toward developing reflective thought the researchers reserve judgment.

References

Allen, D. W., & Eve, A. W. (1968). Microteaching. Theory into Practice, 7(5), 181-185. doi: 10.1080/00405846809542153

Ary, D., Jacobs, L. C., & Sorensen, C. (2010). Introduction to research in education (8th Ed.). Belmont, CA: Wadsworth Cengage Learning.

Bates, A. J., Ramirez, L., & Drits, D. (2009). Connecting university supervision and critical reflection: Mentoring and modeling. The Teacher Educator, 44(2), 90-112. doi:10. 1080/08878730902751993

Brent, R., Wheatley, E., & Thomson, W.S. (1996). Videotaped microteaching: Bridging the gap from the university to the classroom. The Teacher Educator, 31(3), 238-247 doi: 10.1080/08878739609555115

Brookfield, S. (1995). Becoming a critically reflective teacher. San Francisco: Jossey-Bass.

Calderhead, J. (1989). Reflective teaching and teacher education. Teaching and Teacher

Education, 5(1), 43-51. doi:10.1016/0742-051X(89)90018-8

Calderhead, J., & Gates, P. (1993). Introduction. In J. Calderhead, & P. Gates (Eds.), Conceptualizing reflection in teacher development (pp. 1-10). London: Falmer Press.

Campbell, D. T., & Stanley J. C. (1963). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally College Publishing Co.

Collier, S. T. (1999). Characteristics of reflective thought during the student teaching experience. Journal of Teacher Education, 50(3), 173-181. doi: 10.1177/ 002248719905000303

Colton, A. B., & Sparks-Langer, G. M. (1993). A conceptual framework to guide the development of teacher reflection and decision making. Journal of Teacher Education, 44(1), 45-54. doi:10.1177/0022487193044001007

Copeland, W. D., Birmingham, C., De la Cruz, E., & Lewin, B. (1993). The reflective practitioner in teaching: Toward a research agenda. Teaching and Teacher Education, 9

(4), 347-359. doi:10.1016/0742-051X(93)90002-X

Costa, A. (1995). New psychology of supervision. In G. Slick (Ed.), Emerging trends in teacher preparation: The future of field experiences (pp. 10-24). Thousand Oaks, CA: Corwin.

Costa, A. & Garmston, R. (2002). Cognitive coaching: A foundation for renaissance schools. Norwood, Massachusetts: Christopher-Gordon, Inc.

Cruickshank, D. R. (1984). Models for the preparation of America’s teachers. Bloomington, IN: The Phi Delta Kappa Educational Foundation.

Cruickshank, D. R. (1996). Preparing America’s teachers. Bloomington, IN: The Phi Delta Kappa Educational Foundation.

Cruickshank, D. R., & Metcalf, K. (1993). Improving preservice teacher assessment through on

campus laboratory experiences. Theory into Practice, 32(2), 86-92. doi: 10.1080/00405849309543580

DeLay, A. M., Washburn, S. G., & Ball, A. L. (2008). Enhancing preservice agriculture teachers’ reflective practice using the structured field experience. Paper presented at the 35th American Association for Agricultural Education Research Conference, Reno, NV

Dewey, J. (1910). How we think. Boston: D. C. Heath and Co.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Glanz, J., & Sullivan, S. (2000). Supervision in practice: 3 steps to improving teaching and learning. Thousand Oaks, CA: Corwin Press, Inc.

Hatton, N., & Smith, D. (1995). Reflection in teacher education: Towards definition and implementation. Teaching and Teacher Education, 11(1), 33-49. doi:10.1016/0742-051X(94)00012-U

Kember, D., Jones, A., Loke, A., McKay, J., Sinclair, K., Tse, H., … Yueng, E. (1999). Determining the level of reflective thinking from students’ written journals using a coding scheme based on the work of Mezirow. International Journal of Lifelong Education, 18(1), 18-30. Doi: 10.1080/026013799293928

Kember, D., Jones, A., Loke, A. Y., McKay, J., Sinclair, K., Tse, H.,…Yeung, E. (2001). Reflective teaching and learning in the health professions: Action research in professional education. Oxford: Blackwell Science Ltd.

Kember, D., Leung, D. Y. P., Jones, A., Loke, A. Y., McKay, J., Sinclair, K., … Yeung, E. (2000). Development of a questionnaire to measure level of reflective thinking.

Assessment and Evaluation in Higher Education, 25(4), 381-395. DOI: 10.1080/713611442

King, P., & Kitchener, K. (1994). Developing reflective judgment. San Francisco: Jossey Bass.

Kolb, D. A. (1984). Experiential learning. Englewood Cliffs, NJ: Prentice-Hall.

Korthagen, F. A. J. (1988). The influence of learning orientations on the development of reflective teaching. In J. Calderhead (Ed.), Teachers’ professional learning (pp. 35-50). Lewes: Falmer Press.

Litke, R. A. (2002). Do all students “get it?” Comparing students’ reflections to course performance. Michigan Journal of Community Service Learning, 8(2), 27-34.

MacKinnon, A. (1987). Detecting reflection-in-action among preservice elementary science teachers. Teaching and Teacher Education, 3(2), 135-145. doi:10.1016/0742-051X(87)90014-X

Marton, F., Dall’Alba, G., Beaty, E. (1993). Conceptions of learning. International Journal of Educational Research, 19(3), 277-300.

Mezirow, J. (1998). On critical reflection. Adult Education Quarterly, 48(3), 185-198.

McIntyre, D. (1993). Theory, theorizing, and reflection in initial teacher education. In J. Calderhead, & P. Gates (Eds.), Conceptualizing reflection in teacher development (pp. 39-52). London: Falmer Press.

Mory, E. (2004). Feedback research revisited. In D. J. Jonassen (Ed.), Handbook of research on educational communications and technology. Mahwah, NJ: Lawrence Erlbaum.

Parsons, M., & Stephenson, M. (2005). Developing reflective practice in student teachers: Collaborative and critical partnerships. Teachers and Teaching: Theory and Practice, 11(1), 95-116. doi: 10.1080/1354060042000337110

Perry, W. G. (1970). Forms of intellectual and ethical development in the college years: A scheme. New York: Holt, Rinehart and Winston.

Professional Standards for the Accreditation of Teacher Education Institutions. (2008). Washington, D.C.: National Center for Accreditation of Teacher Education. Retrieved on July 13, 2009, from http://www.ncate.org/documents/standards/ NCATE%20 Standards%202008.pdf

Pultorak, E. G. (1993). Facilitating reflective thought in novice teachers. Journal of Teacher Education, 44(4), 288-295. doi:10.1177/0022487193044004007

Raelin, J. (2001). Public reflection as the basis of learning. Management Learning, 32(1), 11-30.

Rodgers, C. (2002). Defining reflection: Another look at John Dewey and reflective thinking. Teachers College Record, 104(4), 842-866. doi:10.1111/1467-9620.00181

Schön, D. A. (1983). The reflective practitioner: How professionals think in action. New York: Basic Books.

Simmons, J. M., & Schuette, M. K. (1988). Strengthening teacher reflective decision-making. Journal of Staff Development, 9(3), 18-27.

Van Es, E., & Sharin, M. (2002). Learning to notice: Scaffolding new teachers’ interpretations of classroom interactions. Journal of Technology and Teacher Education, 10(4), 571-596.

Van Manen, M. (1977). Linking way of knowing with ways of being practical. Curriculum Inquiry, 6(3), 205-228.

Ward, J. R., & McCotter, S. S. (2004). Reflection as a visible outcome for pre-service teachers. Teaching and Teacher Education, 20, 243-257. doi:10.1016/j.tate. 2004.02.004

Whipp, J. L. (2003). Scaffolding critical reflection in online discussions: Helping prospective teachers think deeply about field experiences in urban schools. Journal of Teacher Education, 54, 321-333. doi:10.1177/0022487103255010

Wildman, T. M., & Niles, J. A. (1987). Reflective teachers: Tensions between abstractions and realities. Journal of Teacher Education, 38(4), 25-31. doi:10.1177/002248718703800405

Wong, F. K.Y., Kember, D., Chung, L.Y.F., & Yan, L. (1995). Assessing the level of reflection

from reflective journals. Journal of Advanced Nursing, 22(1), 48-57. doi: 10.1046/j.1365-2648.1995.22010048.x

Zeichner, K. M., & Liston, D. P. (1987). Teaching student teachers to reflect. Harvard Educational Review, 57(1), 23-48.

Agriscience Teachers’ Confidence Levels to Teach Advanced Animal Science for Science CreditSteven “Boot” Chumbley, Eastern New Mexico University

Rudy Ritz, Texas Tech UniversityScott Burris, Texas Tech UniversitySteve Fraze, Texas Tech University

Abstract

The primary purpose of this study was to determine the confidence levels of agricultural science teachers offering credit in the advanced animal science. It is important to describe what types of programs are providing this educational opportunity to their students. This study utilized a descriptive-correlational research design. Teachers were asked to identify their confidence levels to teach the state standards of the advanced animal science course. Most participants were found to be teachers within the ages of 21-30 years old. The majority of teachers in the study did not have the secondary science teacher certification. Teachers felt the least confident to teach students the appropriate use of scientific methods and equipment use during field and laboratory investigations. Teachers felt the most confident teaching how to develop supervised agriculture experience programs as they relate to agriculture, food, and natural resources. Teachers with the secondary science certification felt more confident than teachers without the certification. There were relationships found between teacher confidence levels prior to and after teaching the course. The findings suggest that there are some relationships between teachers’ years of experience, school size and participant age to their confidence levels in integrating science in the advanced animal science course.

Introduction-Theoretical Framework

Since their beginnings agriculture education programs have utilized the integration of science skills into their curriculum. Legislation like the Hatch Act of 1887 broadened the scope of agricultural education by establishing experiment stations, with many of these serving as schools. These educational programs focused on practical and scientific applications of agriculture principles. John Dewey, 20th century educational reformer, believed strongly in the importance of curriculum integration. Dewey (1944) also felt that to separate the core curriculum subjects and vocational academic programs was a detriment to student success. Vocational agriculture programs helped citizens of Texas and the United States to become the world leaders in agriculture production (Cepica, M., Dillingham, J., Eggenberger, L., & Stockton, J., 1988). These successes could not have been accomplished without an established background in science. The 1988 publication of Understanding Agriculture: New Directions for Education by the National Research Council encouraged agricultural educators to incorporate more science-based instruction (NRC, 1988). Two areas within the secondary agricultural education program, Supervised Agricultural Experience (SAE) and classroom instruction, hold high potential for student’s learning of science concepts, both formally and informally (Ramsey & Edwards, 2004). The need for increased science skills in agriculture programs is apparent when only 2% of those employed in the field of agriculture work specifically in production farming.

Many states have endeavored to include science in their agriculture classes (Thompson & Schumacher, 1998). The Texas State Board of Education (SBOE) approved the “4x4” high school graduation plan in the Fall of 2006 (SBOE, 2006). The “4x4” plan required specific standards for high school graduation. The new plan required students to earn four credits in four of the core subjects: English, math, social studies and science. This new law made up the largest part of House Bill 1, the education reform bill that the Texas Legislature passed in 2006 (Wentworth, 2006). Under the "4 x 4"

plan the science credits must be earned in biology, chemistry, physics or other lab-based sciences of the student's choice, such as agriculture, engineering, anatomy and physiology of human systems or earth and space science (SBOE, 2006). This can have a negative effect on program enrollment and program success (Balschweid & Thompson, 2002). Under these new guidelines teachers must work harder to ensure their students have the room in their schedules to take agricultural science courses and other electives. A solution to this involves finding ways to get core credit in elective courses, like agriculture. If students cannot receive core credit in their elective courses, then they may be less likely to enroll in such courses (Johnson, 1996). As many states adopt this practice, Texas agricultural science programs are in a critical position to prove their value. As these programs gain popularity, caution, must be taken to ensure teachers are confident in their ability to offer such courses (Enderlin & Osborne, 1992).

The first step in developing quality science instruction is to understand the areas that teachers are confident in and the areas where they believe their skills need to be strengthened. Previously, researchers have found that overall agricultural science teachers feel confident in their ability to include science credit in their courses (Balshwied & Thompson, 1999; Connors & Elliot, 1995; Dyer & Osborne, 1999; Newman & Johnson, 1993; Welton, Harbstreit & Borchers, 1994). There is currently not any current research on Texas agricultural science teachers and their confidence to teach the advanced animal science class for secondary science credit. While other states have offered this option in their courses for several years (Thompson & Balschweid, 1999), the 2010-2011 schools year was the first year that Texas had offered this to students. As a new development, it is important to understand the level of confidence teachers have in integrating these science concepts into their classes. It is also important to identify what programs are offering these courses, the teacher’s certification and, experience and at what grade levels students are taking this course.

The theoretical model for this study focused primarily on the perceptions of agricultural science teachers to teach the advanced animal science course for science credit. This was based loosely around the theory of planned behavior (Azjen, 1985), which is an extension of the theory of reasoned action (Fishbein & Azjen, 1975; Azjen & Fishbein, 1980). The theory of reasoned action depicts the psychological process by which attitudes cause behavior (Fishbein, 1967). Both were designed to exhibit the relationship between informational and motivational influences on behavior (Connor & Armitage, 1998). The theory of planned behavior suggests that behavioral intentions can be best viewed as consequences of an individual’s attitude. The theory of planned behavior suggests that demographic variables and knowledge, influences values and beliefs. These in turn affect attitude, intention and behavior. The theories impact the study of confidence levels and the factors that influence agriculture teacher success in teaching the advanced animal science course. The theory of planned behavior represents behavior as a function of behavioral intentions and perceived behavioral control (PBC) (Azjen, 1991). This concept is similar to Bandura’s (1984) concept of self-efficacy.

Teacher confidence is routinely linked to Bandura’s concept of self-efficacy. This can be described as a teacher’s judgment of their capabilities to organize and execute courses of action required to types of performance (Bandura, 1984). Self-efficacy can enhance or impair performance through their effects on cognitive, affective, or motivational intervening processes. It is important to note that a person’s beliefs about their capabilities are not the same as actual ability, but they are closely related. If a person has low efficacy or confidence in a task, then their performance in that task is expected to be low (Bandura, 1997). Conversely, higher ability levels would tend to increase their confidence levels and as a result, their level of performance.

As adapted for this study, these theories suggest that agricultural science teachers past experiences and demographic characteristics influence their decisions to integrate science and teach the advanced science courses in their agricultural programs. By understating teacher confidence and perceptions of teaching the advanced animal science course researchers will more likely be able to determine how confident there are to successfully implement this course into their programs. This study addresses Research Priority three of the American Association for Agricultural Education (AAAE) National Research Agenda – Sufficient Scientific and Professional Workforce That Addresses the Challenges of the 21st Century (Doerfert, 2011).

Purpose and ObjectivesThe purpose of this study was to determine the confidence level of Texas agricultural science

teachers offering science credit in the advanced animal science class and determine teachers’ levels of confidence in teaching the required curriculum for that course. The researchers also sought to provide demographic data on the teachers, student enrollment and programs that are offering the science credit in their courses. The teaching of such courses is a new development in Texas and as such, it is important to describe what types of programs are providing this educational opportunity to their students.The following research objectives were employed to conduct this study:

1. Describe characteristics of Texas teachers teaching the advanced animal science course for science credit.

2. Describe the teacher’s confidence levels to teach each of the 15 Advanced Animal Science TEKS required by the State of Texas for the advanced animal science course prior to teaching the course.

3. Measure the teachers’ confidence levels to teach each of the 15 Advanced Animal Science TEKS required by the State of Texas for the advanced animal science course after having taught the course

4. Determine if there were relationships between agriscience teachers’ confidence levels and teacher characteristics prior to and after having taught the advanced animal science course.

5. Determine if practical differences exist between agriscience teacher confidence levels prior to and after teaching the advanced animal science course.

Methods

The target population for this study was Texas agricultural science teachers and programs that were offering the advanced animal science course for science credit. A census of teachers identified those who were currently teaching the advanced animal science course for science credit. Subjects were selected based upon the criteria that they were teaching the agricultural science courses for science credit. The researcher sought to obtain representation from all ten areas of state of Texas. The state FFA association has divided the state into ten semi-autonomous associations (Texas FFA, 2010). A modified version of Dillman’s Mail and Internet Surveys (2007) was used for data collection and correspondence with sample participants. The researcher contacted 51 teachers identified as teaching the advanced animal science course. Forty-four of those contacted responded to the study. This resulted in a response rate of 86%.

This study utilized a descriptive-correlational research design. According to Gall, Gall and Borg (2007), correlational research is defined as “A type of investigation that seeks to discover the direction and magnitude of the relationship among variables through the use of

correlational statistics” (p. 636). This study is descriptive as it also employs a methodology that allowed secondary agricultural science teachers and program directors to describe their agricultural science program’s characteristics and to gauge level of preparedness.

The researcher developed instrument was a 48-item instrument consisting of nominal and ordinal scales of measurement to rank order questions that asked the participant to indicate level of confidence; containing questions based on a Likert-type scale. The instrument was developed specifically for this research. The content for this instrument was developed using referenced materials from previous studies and accepted scholarly publications in agricultural education. The instrument was pilot tested in order to determine reliability. Cronbach’s alpha coefficients were used to measure internal consistency in order to establish reliability. The pilot test data revealed a reliability Cronbach’s alpha coefficient of .943. A single coefficient was present because the pilot test consisted of questions about how teachers felt prior to teaching the course, as the pilot test group could not have taught the course. Nunnally (1967) suggested that Cronbach’s alpha coefficients of .5-.6 are acceptable in the early stages of research. Reliability of the pre-test and final instrument is presented in Table 1.

Table 1Pilot Test and Final Instrument Reliability Scores

Instrument Cronbach’s AlphaPilot Test .934Final Instrument

Prior to teaching course .964After teaching course .952

FindingsObjective One: The first objective was to identify the demographic characteristics of teachers who were teaching the advanced animal science course for science credit. This included questions about participant age, years of teaching experience, what the teachers initial certification was, if they had received the secondary science teacher certification and how many years, if any, had the participant taught a core secondary science course (biology, chemistry, physics, etc.). Participants in this study varied in age. The majority of participants (43.2%) were 21-30 years old (n = 19). The remaining participants were in the age ranges of 31-40 (29.5%, n =13), 41-50 (13.6%, n = 6) and 51-60 (13.6%, n = 6). No participants were found to be older than 60 years of age. Table 2 demonstrates the teacher’s levels of teaching experience. Table 2Participants Years of Teaching Experience

Years Teaching f %

1-5 years 17 38.6

6-10 years 10 22.711-15 years 6 13.6

16-20 years 4 9.121-25 years 3 6.8

Over 25 years 4 9.1

Most teachers in this study (n = 42) received their initial degree in agricultural science with a teaching certificate (95.5%). Other initial degrees teachers received included agriculture business (n = 1, 2.3%) and poultry science (n = 1, 2.3%). There were 8 (18.2%) teachers who had received their secondary science teaching certificate. The remaining teachers (n = 36) had not (81.8%). All of the teachers who had received the science credit (n = 8) had taught a core science class at some point in time. The largest group (n = 5) had only taught a secondary science 1 to 5 years (11.4%). The remaining teachers had taught secondary science a varied number of years ranging from 6 to 10 years (n = 1, 2.3%), 11 to 15 years (n = 1, 2.3%) and 16 to 20 years (n = 1, 2.3%).Objective Two: The second objective sought to evaluate teachers’ level of confidence to teach the course objectives of the advanced animal science course prior to teaching the course. Table 3 distinguishes the fifteen courses standards, defined as the Texas Essential Knowledge and Skills (TEKS), under the Texas Administrative Code Title 19 130.7c. Teachers’ confidence levels were evaluated upon a five-point Likert-Type scale with responses of: 1= no confidence, 2= very little confidence, 3= moderately confident, 4= confident and 5= very confident. Table 3Texas Essential Knowledge and Skills Content Objectives

Course Objective (TEKS)

Description

130.7c (1) 40% of instructional time is conducted in field and laboratory using safe, environmentally appropriate, and ethical practices.

130.7c (2) The student uses scientific methods and equipment during filed and laboratory investigations

130.7c (3) The student uses critical thinking, scientific reasoning and problem solving to make informed decisions within and outside the classroom.

130.7c (4) The student evaluates the employability characteristic of an employee

130.7c (5) The student demonstrates principles relating to the human, scientific, and technological dimensions of scientific animal agriculture and the resources necessary for producing domesticated animals.

130.7c (6) The student applies the principles of reproduction and breeding to livestock improvement.

130.7c (7) The student applies the principles of molecular genetics and heredity

130.7c (8) The student examines and compares animal anatomy and physiology in livestock species.

130.7c (9) The student determines nutritional requirements of ruminant and non-ruminant animals.

130.7c (10) The student evaluates animal diseases and parasites.

130.7c (11) The student defines how an organism grows and how specialized cells, tissues, and organs develop.

130.7c (12) The student recognizes policies and issues in animal science.

130.7c (13) The student discusses livestock harvesting operations

130.7c (14) The student explores methods of marketing livestock

130.7c (15) The student develops an advanced supervised agriculture experience program as it relates to agriculture, food, and natural resources.

The teachers’ confidence levels prior to teaching advanced animal science were evaluated with a Likert-type scale and were then analyzed using the SPSS version 18 statistical software. The scores were analyzed and given a summated score. The overall total summated score a teacher could receive landed in the range of 0 to 75. These scores were reported with frequencies, percentages, mean and standard deviation. The researcher found that teachers’ confidence levels were moderately confident to confident prior to teaching the advanced animal science course.

The researcher found that teachers’ confidence levels were moderately confident to confident prior to teaching the advanced animal science course. Teachers in this study (N= 44) felt the most confident to teach objective 130.7c (15): The student develops an advanced supervised agriculture experience program as it relates to agriculture, food, and natural resources; with a mean score of 4.14 and standard deviation of 0.88. TEKS four: The student evaluates the employability characteristics of an employee and five: The student demonstrates principles relating to the human, scientific, and technological dimensions of scientific animal agriculture and the resources necessary for producing domesticated animals, were the next highest that teachers felt confident to teach with both having a mean score of 4.09 and a standard deviation of 0.96.

The TEKS six, seven and eight: The application of the principles of reproduction and breeding to livestock improvement, the principles of molecular genetics and heredity, and comparing animal anatomy and physiology in livestock species, were unique in that they did not have a single participant select no confidence in teaching these objectives. Conversely, teachers felt the least amount of confidence in teaching TEKS 130.7c (2): The student uses scientific methods and equipment during field and laboratory investigations. Objectives TEKS 130.7c 3: The student uses critical thinking, scientific reasoning, and problem solving to make informed decisions within and outside the classroom and TEKS 130.7c 9: The student determines nutritional requirements of ruminant and non-ruminant animals; had the highest variability with standard deviations of 1.07 respectively. The summated score was 57.79 with a standard deviation of 11.97.

Objective Three: The third objective sought to evaluate teachers’ level of confidence to teach the TEKS of the advanced animal science post- teaching the course. Confidence levels of teachers’ (N= 44) after teaching the advanced animal science course were higher than the scores prior to teaching the course. The summated score resulted in a mean score of 61.09 out of a possible 75 with a standard deviation of 9.88. Teachers felt the most confident teaching objective (4): the evaluation of the employability characteristics of an employee, with a mean of 4.32 and standard deviation of 0.74. In both objectives (4) and (5) participants only recorded scores in the confident to very confident range. Teachers felt the least confident to teach TEKS 130.7 (2): the use of scientific methods and equipment during field and laboratory investigations, with a mean score of 3.70 and a standard deviation of 0.82. This was the only objective that participants marked with a no confidence score on this part of the instrument. The next lowest confidence score was found in TEKS 130.7 (3): the student uses critical thinking, scientific reasoning, and problem solving to make informed decisions within and outside the classroom, with a mean of 3.73 and standard deviation of 0.97 respectively. The TEKS with the most variability was TEKS 130.7 (c) 7: The student applies the principles of molecular genetics and

heredity, with a standard deviation of 1.02. Table 4 illustrates the differences in teacher confidence scores prior to and after having taught the course.

Table 4Differences in Teacher Confidence Scores Prior to and After Teaching the Course

Prior to Teaching Course After Teaching Course

TEKS M SD M SD130.7c (1) 3.70 1.02 4.07 0.87

130.7c (2) 3.40 0.97 3.70 0.82

130.7c (3) 3.57 1.07 3.73 0.97

130.7c (4) 4.09 0.96 4.32 0.74

130.7c (5) 4.09 0.96 4.23 0.76

130.7c (6) 4.02 0.93 4.22 0.80

130.7c (7) 3.70 0.95 3.86 1.02

130.7c (8) 3.75 0.92 4.22 0.80

130.7c (9) 3.95 1.07 4.11 0.87

130.7c (10) 3.84 0.99 4.04 0.86

130.7c (11) 3.86 1.02 3.86 0.89

130.7c (12) 3.66 1.01 4.15 0.83

130.7c (13) 3.93 0.97 4.15 0.81

130.7c (14) 4.06 0.89 4.13 0.85

130.7c (15) 4.14 0.88 4.20 0.82

Objective Four: The fourth objective was to describe any possible relationships between teachers’ demographic characteristics and their levels of confidence. The demographic variables were compared to the teachers’ confidence scores prior to teaching the advanced animal science course and after teaching the course. Nominal demographic data was correlated to confidence levels with a point-biserial coefficient while ordinal demographic data was correlated with a Pearson product correlational coefficient.

There was found to be a relationship of moderate magnitude between participants’ age and their confidence levels prior to teaching the advanced animal science course for science credit. These were found within objectives (2): The student uses scientific methods and equipment during field and laboratory investigations, with a score of .426, and (3): the student uses critical thinking, scientific reasoning, and problem solving to make informed decisions within and outside the classroom with a score of .418. There were found to be substantial and moderate correlations between participants’ years of teaching experience and confidence levels prior to teaching the course. The strongest correlations (.514) were found with TEKS 130.7 (c) 2 and 130.7 (c) 3: The student uses critical thinking, scientific reasoning, and problem solving to make informed decisions within and outside the classroom, with (.513). A moderate relationship

(.351) was found with TEKS 130.7 (c) 1: 40% of instructional time is conducted in field and laboratory investigations using safe, environmentally appropriate, and ethical practices. The remaining correlations were found to be low to negligible.

There were only low to negligible Pearson product-moment correlation coefficients found between teachers age and confidence levels after teaching the advanced animal science course. The highest correlation found was on TEKS (2), with a score of .253. The same was found with teacher’s years of teaching experience and confidence after teaching. There was found to be a significant negative correlation between teachers’ attaining the secondary science certification and confidence levels after teaching the course. TEKS 130.7 (c) 2 had moderately negative point bi-serial correlation of -.316.

Objective Five: The last objective sought to determine if any practical differences exist between agriscience teacher’s confidence levels prior to and after teaching the advanced animal science course. This was measured utilizing the summated scores of teachers’ confidence levels. The effect size was measured using means, standard deviations and Cohen’s d measure of effect size. Thalheimer and Cook (2002) suggest the relative size of Cohen’s d to measure effect size. Table 5 illustrates the coefficients (Thalheimer & Cook, 2002).

Table 5Relative Size Measures of Cohen’s d Coefficient (Thalheimer & Cook, 2002)Size Cohen’s d Coefficient

Negligible Effect >= -0.15 and <.15Small Effect >=.15 and <.40Medium Effect >=.40 and <.75Large Effect >=.75 and <1.10Very Large Effect >=1.10 and <1.45Huge Effect >1.45

To measure effect size, the summated score from teachers’ confidence were taken. There was found to be a small effect size between the teachers’ confidence scores before and after teaching the advanced animal science course for science credit. Table 6 demonstrates effect size and practical differences between confidence scores. Table 6Effect Size of Summated Confidence ScoresConfidence Scores M SD Cohen’s d EffectPrior to Teaching Advanced Animal Science 57.79 11.97After Teaching Advanced Animal Science 61.09 9.88

Effect Size 0.3 Small

The researcher also tested the individual course TEKS to establish if there was any substantial measure of effect size between them. Two of the TEKS were found to have medium measures of effect. Cpourse objective 130.7c (8): The student examines and compares animal anatomy and physiology in livestock species had the largest change in effect size with a Cohen’s

d of 0.55. Objective 130.7c (12): The student recognizes policies and issues in animal science, was found to have the second largest change in score (0.54).

Conclusions

The majority of participants were found to be in the age range of 21 to 30 years old with 1-10 years of teaching experience. This is consistent with the findings of Myers and Dyer (2004) that recent college graduates exhibit an even higher level of proficiency and desire to integrate science concepts than individuals who are further removed from the application of such skills in a college setting. Most participants had received their initial teacher certification in agricultural education and had not received the secondary science certification. This may indicate that there is a lack of more experienced teachers who are implementing the advanced animal science courses in their programs. This is similar to other research findings (Washburn, Myers, & Dyer, 2004; Dormondy, 1993).

Most of the teachers had not received the secondary science certification. This reflects agricultural science teachers in the state (Young, 2010). This may show a lack of desire for teachers to receive the secondary science certification as found in Dormondy (1993). Dormondy found that teachers who received the science certification were more likely to teach a core science course. This may be one of the reasons behind teachers not receiving their science certification. Caution should be taken when making any inferences beyond the study.

Prior to teaching the advanced animal science course for science credit teachers felt the most confident to teach objective 130.7 (c) 15: The student develops an advanced supervised agriculture experience program as it relates to agriculture, food, and natural resources. The participants felt the least confident in teaching objective 130.7 (c) 2: The student uses scientific methods and equipment during field and laboratory investigations. The findings of this objective infer that teacher preparation programs provided teachers with adequate training to be successful in the area of Supervised Agricultural Experiences (SAE).

Teachers did not show confidence using scientific methods or equipment in laboratory and field activities. This was congruent with the findings of Balschweid and Thompson (1999) that teachers felt they needed more training with scientific equipment. Training must be provided that prepares teachers/ students for these scenarios. As the advanced animal science course requires 40% instructional time in a laboratory environment, it is important to have these skills to meet the needs of the course. The lack of confidence to use scientific equipment could be due to a lack of experience with scientific equipment. Teachers should incorporate their strengths with SAE projects with scientific methods in field activities. By combining a hands-on activity like the SAE project, the teachers can increase their confidence in teaching in a field based environment.

After having taught the advanced animal science course, teachers felt the confident teaching objective 130.7 (c) 4: The student evaluates the employability characteristics of an employee. Teachers were found to have the least amount of confidence in teaching objective 130.7 (c) 2: The student uses scientific methods and equipment during field and laboratory investigations, prior to and after having taught the advanced animal science course for science credit. This was the only TEKS objective that teachers felt no confidence in teaching after having already taught the course. The findings suggest that after teaching the course, teachers have a better understanding of employability characteristics and careers in animal science.

Teaching the course did not improve teachers’ ability to incorporate scientific methods or their confidence with equipment in laboratory and field activities. This barrier can be attributed to teachers’

lack of experience with scientific equipment and field-based activities. An underlying cause for this could be because of the lack of such resources as reported by Whent (1994). The teachers, though inexperienced, have a realistic idea of what they can teach.

There was a relationship between participant age and confidence in teaching the advanced animal science course for science credit. The strongest magnitude found between participant age and confidence levels prior to teaching the animal science course was TEKS 130. (c) 2: The student uses scientific methods and equipment during field and laboratory investigation. It is important to note that this was the objective that overall teachers felt the least confident to teach. It can be concluded from the findings that more experienced teachers were found to be more confident prior to teaching the advanced animal science course than teachers who are younger or have less years of experience. Teachers who have more experience have higher confidence in using scientific methods and equipment in laboratory and field based investigations

There were findings after teaching the course showing teachers felt more confident to teach animal anatomy and physiology in livestock species and recognize policies and issues in animal science. Pre-service teachers who go through a student teaching experience in programs teaching the advanced animal science course will have more confidence and be better prepared to teach the course (Warnick, Thompson and Gummer, 2004). Teachers will be better prepared to teach about animal physiology and recognize issues in animal science after teaching and working with individuals teaching the advanced animal science course. There was found to be a measurable negative correlation between teachers’ attaining the secondary science certification and confidence levels after teaching the course (rs = -.316). Those teachers who had not received the secondary science certification were found to continually have the lowest confidence level in teaching TEKS 130.7 (c) 2: the student uses scientific methods and equipment during laboratory investigation. Teachers should be encouraged to attain a science certification if they are planning on teaching the advanced animal science course for credit.

Educational leaders and university faculty should encourage more experienced faculty and cooperating teachers to teach the advanced animal science course for 4th year science credit. Less experienced teachers should be encouraged work with science teachers in using scientific equipment and teaching lab based activities, similar to the findings of Thompson (2001). Inexperienced teachers should take advantage of professional development activities that encourage lab-based activities. Teachers should also consider receiving the secondary science certification to enhance their understanding of science principles. Earning the science certification can increase confidence in teachers to teach advanced animal science for science credit

Teachers should incorporate their strengths with SAE projects with scientific methods in field activities. Teachers must work to strengthen their ability to incorporate science activities in a laboratory and field based setting. The agricultural science program is the best place to incorporate such teaching strategies. Teachers should utilize their animal project centers and learning labs. Involvement in the science fairs is another suggested practice for teachers. Professional development opportunities for teachers should be developed to assist teachers in teaching this course. This training should focus on the integration of scientific methods and working in laboratory environments. Teachers should expose themselves to laboratory equipment and scientific investigative practices. A relationship with industry leaders and collaboration with science teachers would aid this practice.

The findings of this study need to be distributed to teachers and university faculty teacher educators to use in the preparation of teachers and development of teaching curriculum. Ideal areas for the distribution of this information include state agriculture teachers conference, professional development workshops and in agricultural education courses. Further research should be conducted

to continue to investigate confidence as these course offerings are offered to more programs. Teachers who are not currently teaching the course should be tested to gauge their confidence levels to teach the 15 TEKS objectives for the advanced animal science course. There should be additional research which controls for teacher demographic characteristics to determine if there are any other factors that affect teacher confidence levels.

These research findings suggest that young teachers are the main teachers who are teaching this course. There should be research done to identify barriers of experienced educators’ decision to teach the course. Research in the area of teachers’ barriers to integrate science should be further studied to determine what motivates teachers to incorporate science in their curriculum. Additionally, this study should be replicated and involve random assignment and a larger sample size of induction teachers. A comparison of teachers’ levels of confidence across state curriculum should be done to gauge any similarities and differences in teachers’ levels of confidence dependent upon curriculum standards.

ReferencesAzjen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision

Processes, 50, 179-211

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.

Balschweid, M., & Thompson, G. (1999). Integrating science in agricultural education: Attitudes of Indiana agricultural science and business teachers. The 26th Annual National Agricultural Education Research Conference, Orlando, FL.

Balschweid, M. & Thompson, (2002). Integrating science in agricultural education: Attitudes of Indiana agricultural science and business teachers. Journal of Agricultural Education, 43(2), 1-10.

Bandura, A. (1984). Recycling misconceptions of perceived self-efficacy. Cognitive Therapy and Research, 8, 231-255.

Bandura, A. (1997). Self-efficacy: The exercise of control, New York: Freeman

Cepica, M., Dillingham, J., Eggenberger, L., Stockton, J. (1988). The history of agricultural education in Texas. Lubbock: The Texas Tech University Press

Connor, M & Armitage, C (1998) Extending the theory o planned behavior: A review and avenues for further research Journal of Applied Sociology, (28)15, 1439-1464.

Connors, J., & Elliot, J. (1995). The influence of agriscience and natural resources curriculum on students’ science achievement scores. Journal of Agricultural Education, 36(3), 57-63.

Dewey, J. (1944). Democracy and education: An introduction to the philosophy of education. New York, NY: The Free Press.

Dillman, D. (2007). Mail and internet surveys: The tailored design method (2nd Ed.). Hoboken, NJ: John Wiley & Sons, Inc.

Doerfert, D. (2011) National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Dept. of Agricultural Education and Communications.

Dyer, J. & Osborne, E. (1999). The influence of science applications in agriculture courses on attitudes of Illinois guidance counselors at model student teaching centers. Journal of Agricultural Education, 40(4), 57-66.

Enderlin, K. & Osborne, E. (1992) Student achievement, attitudes and thinking skill attainment in an integrated science/agriculture course. Proceedings of the Nineteenth Annual National Agriculture Education Research Meeting, St. Louis Missouri

Fishbein, M (1967) Attitude and the prediction of behavior. Reading in attitude theory and measurement (pp. 477-492) New York, NY: Wiley & Sons

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.

Fraenkel, J. & Wallen, N.(2006). How to design and evaluate research in education. NewYork: McGraw-Hill.

Gall, M., Gall, J., & Borg, W. (2007). Education research: An introduction (8th ed.). Boston: Pearson Education.

Hillison, J. (1996). The origins of agriscience: Or where did all that scientific agriculture come from? The Agricultural Education Magazine, 37(4), 8–13

Johnson, D (1996) Science credit for agriculture: Perceived support, preferred implementation methods and teacher science courses. Journal of Agriculture Education 37(1) 22-30

Newman, M. & Johnson, D. (1993). Perceptions of Mississippi secondary agriculture teachers concerning pilot agriscience courses. Journal of Agricultural Education, 34(3), 49-58.

Myers, B. & Dyer, J. (2004), Agricultural teacher education programs: A synthesis of the literature, Journal of Agricultural Education, 45(3), 44–52

Myers, B., Washburn, S. G., & Dyer, J. (2004). Assessing agriculture teachers’ capacity for teaching science integrated process skills. Journal of Southern Agricultural Education, 54(1), 74-85.

Newman, M. & Johnson, D. (1993). Perceptions of secondary agriculture teachers concerning pilot agriscience courses. Journal of Agricultural Education, 34(3), 49-58.

National Academy of Sciences: Committee on Agricultural Education (1988). Understanding agriculture: New directions for education. National Academies

Nunnally, J. C. (1967) Psychometric Theory. New York: McGraw Hill.Ramsey, J & Edwards, C (2004) Informal learning in science: Does agricultural education have a role?

Journal of Southern Agricultural Education Research 54(1), 86-99.

Texas State Board of Education (SBOE) (2006) State of Texas accountability manual www.tea.state.tx.us/index4.aspx?id=8327&menu_id=2147483659 retrieved August 2011

Texas FFA (2010) www.texasffa.org retrieved August 2011

Thompson, G., & Schumacher, L. (1998). Selected characteristics of the National FFA Organization’s Agriscience teacher of the year award winners and their agriscience programs. Journal of Agricultural Education, 39(2), 50-60

Thompson, G. & Balschweid, M. (1999). Attitudes of Oregon agricultural science and technology teachers toward integrating science. Journal of Agricultural Education, 40(3), 21-29.

USDA (1999) United Sates Department of Agriculture www.usda.gov retrieved August 2011

Warnick, B., Thompson, G. & Gummer, E. (2004). Perceptions of science teachers regarding the integration of science into the agricultural education curriculum. Journal of Agricultural Education, 45(1), 62-72

Washburn, S., & Myers, B. (2008) Agriculture teacher perceptions of preparation to integrate science and their current use of inquiry based learning. Proceedings of the 35th American Association for Agricultural Education National Research Conference. Reno, NV

Welton, R., Harbstreit, S., & Borchers, C. (1994). The development of an innovative model to enhance the knowledge and skill levels in basic sciences for secondary Agriscience teachers. Paper presented at the 21st Annual National Agricultural Education Research Meeting, Dallas, TX.

Whent, L. (1994). Factors influencing resource sharing between agriculture and science teachers participating in the agriscience program. Journal of Agricultural Education, 35(3), 11-17.

Reusable Learning Objects:Faculty Perceptions and Best Practices in a College of Agriculture

Theresa Pesl Murphrey, Texas A&M UniversityM’Randa R. Sandlin, Texas A&M University

James R. Lindner, Texas A&M UniversityKim E. Dooley, Texas A&M University

Abstract

Educators across the field of agricultural education continue to strive to improve the educational experience for students. The use of reusable learning objects (RLO) is one method that is being pursued. For the purpose of this study, an RLO was defined as a short (i.e., 5-15 minutes), media-based instructional package that includes a learning objective, content, media (pictures, videos, and/or audio), and an assessment. This study is grounded by Kolb’s theory of experiential learning in the collection of preflection and reflection responses from participants and the area of instructional design in regard to the development of reusable learning objects.The purpose was to investigate faculty perceptions of RLOs and by doing so, document the challenges to creating RLOs and determine best practices for development and use in order to internationalize curriculum. A qualitative research design including face-to-face, semi-structured pre and post-interviews was employed. Respondents reported positive perceptions of RLOs both prior to and after their engagement in the development process. This study revealed recommendations for practice that can assist the profession in encouraging the development and use of reusable learning objects.

Introduction and Literature Review

Educators across the field of agricultural education continue to strive to improve the educational experience for students. The use of reusable learning objects is one method among others, such as students’ oral verbalization (Pate & Miller, 2011), inquiry-based instruction (Thoron, Myers, & Abrams, 2011), and experiential learning (Wulff-Risner & Stewart, 1997), that is being pursued. RLOs are commonly defined and described in a variety of ways. The IEEE (Institute of Electrical and Electronics Engineers, Inc.) broadly defined a learning object as “any entity, digital or non-digital, that may be used for learning, education or training” (2002, p. 5). A more specific definition stated that learning objects are “generally understood to be digital and multimedia-based, which can be reused and – in some cases – combined with other learning objects to form larger pieces of instruction” (Farha, 2009, p. 2). Another way to explain learning objects is that “each unit should do one thing and only one thing” (Boyle, 2003, p. 2). Some authors have clarified that RLOs are small, only large enough to include, at the most, a few related ideas (Conlan, Dagger, & Wade, 2002; Polsani, 2003). The length can range from one to two hours of content (Downes, 2001) based on how many ideas were covered and how complex each idea was, however they should be independent of other related content (Boyle, 2003).

Researchers have articulated that an RLO is an object that can come in all shapes and forms (Downes, 2001; Farha, 2009; Muzio, Heins, & Mundell, 2002; Polsani, 2003). However, there is still a definite amount of ambiguity involved when defining an RLO because of the vast

differences in characteristics (Polsani, 2003; Sicilia & Lytras, 2002). For the purpose of this study, an RLO was defined as a short (i.e., 5-15 minutes), media-based instructional package that includes a learning objective, content, media (pictures, videos, and/or audio), and an assessment.

Benefits of Reusable Learning Objects

The possible benefits of using RLOs in the classroom are diverse and could have far-reaching impacts for faculty. A 2009 study by Farha found that test scores for students using learning objects were “nearly three times higher” (p. 8) than traditional students who used texts. In addition, usage can decrease time and costs for faculty, as they have the ability to create lessons from units of already-developed material rather than assemble a lesson from scratch (Brusilovsky, 2004; Downes, 2001; Sicilia & Lytras, 2002). Using RLOs, especially within the context of online learning, helps students learn in a “spiraling, progressive manner” (p. 315) which is a mode of learning that comes naturally to the brain and promotes deep learning (Hamid, 2002).

While educators have historically been required to do at least some re-authoring of material in order to mold it to the needs of their current students, RLOs allow educators to easily reuse material by breaking it up into small chunks. Because the lessons based on RLOs could be “personalized to a learner’s cognitive preferences,” the RLOs can result in “more effective learning” (Conlan, Dagger, & Wade, 2002, p. 1). “[RLOs’] most significant promise is to increase and improve the effectiveness of learning and human performance” (Hodgins, 2002, p. 76). According to this author, the major benefit of RLOs is the “ability to capture knowledge” (p. 79) so that it can be reused and eventually be improved for new information. The power of reusable learning objects is the impact “when just-right information is flowing to the right place, person, and time” (p. 79).

Drawbacks with using Reusable Learning Objects

Given the benefits that exist, one might wonder why RLO use has not been adopted on a more wide-scale basis. Sharing RLOs can be difficult due to their individual nature. Thus, what is a primary benefit becomes a drawback. As shared by Duval (2001), it can be extremely difficult to share metadata between users due to the use of “independently developed systems for metadata management” (p. 462). This ultimately means that potential users of RLOs may find locating usable RLOs difficult, thus, there is a need to make finding them easier. Given that RLOs can be created on different programs and stored in different ways, the reuse of an RLO created by another individual is made difficult (Brusilovsky, 2004). Duval (2001) stated the importance of “standardization” in the field of education and training, particularly so that content and tools can be reused.

The basic step of defining RLOs can also create dilemmas that affect overall creation and use. Muzio et al. (2002) shared drawbacks that could be associated with the use of RLOs that included size (how large should it be?) and the issue of “intellectual property” (p. 24). Related to this is the question of what is the best way to compile or classify RLOs (Churchill, 2007; Downes, 2001; Hodgins, 2002; Lukasiak, Agostinho, Bennett, Harper, Lockyer, & Powley, 2005). Developers have concerns that their RLO will be used without citation, or if they should

be freely shared or not (Downes, 2001; Muzio et al, 2002). Finally, the ideal length of a learning object is a subject that has been contested for years (Churchill, 2007; Conlan, Dagger, & Wade, 2002; Muzio et al., 2002; Sicilia & Lytras, 2002).

Hamid (2002) listed three elements, “information architecture,” “user interface design,” and “content strategy” (p. 313) as aspects that users and designers should be aware of when creating online learning content. Lack of awareness and understanding of these three areas could create drawbacks.

Context of the Study

This study was part of a USDA Higher Education Challenge Grant that was awarded to faculty at the University of Florida The goal of the grant was to utilize the development of RLOs to internationalize undergraduate curricula. This study was an examination of participating faculty’s perceptions of RLOs and the development process both before and after their engagement in the process. A literature review conducted by Vincenti (2001) found a “substantive overlap between the benefits of international/intercultural experiences and qualities needed for intercultural effectiveness and those needed for interdisciplinary work” (p. 42). In other words, international experiences assist individuals in preparation for interdisciplinary work, according to their literature review, because they practice putting their material into different cultural formats during their time abroad. This study sought to determine faculty perceptions and reactions to RLO development in the context of using content collected in an international setting.

The improvement of instruction to be increasingly efficient and effective across agricultural education is critical. This study sought to add to the body of knowledge related to teaching and learning by focusing on the use of reusable learning objects. Completion of this study supports two research priorities of the National Research Agenda for the American Association for Agricultural Education (Doerfert, 2011): Priority 4: Meaningful, Engaged Learning in All Environments, and Priority 5: Efficient and Effective Agricultural Education Programs.

Theoretical Framework

The framework for this study utilized Kolb’s theory of experiential learning in the collection of preflection and reflection responses from participants and the area of instructional design in regard to the development of reusable learning objects.

Kolb’s theory of experiential learning (Kolb, 1984) and, as an extension of Kolb’s model, the addition of preflection (Jones & Bjelland, 2004) provided a mechanism to collect rich data from participants. Kolb outlined four stages of learning: abstract conceptualization, active experimentation, concrete experience, and reflective observation. As individuals are guided through each of these stages, an awareness and understanding of the topic at hand is gained. Jones and Bjelland (2004) introduced the idea of preflection. Preflection is a means by which participants are made aware of the expectations of the experience to be had. This activity promotes participants’ learning during the first three stages of Kolb’s theory of experiential learning model and, in turn, promotes a higher level of information processing during the reflection observation stage.

The overarching framework for this study was based upon instructional design and the need for functional units of instruction. As stated by Love (1964), “successful teachers know that a unit of instruction must center on the needs of the student” (p.20). Students have become more technologically savvy and thus, there is a need for instructors to alter their perspectives of what instruction can be. There are a variety of ways in which instruction can be improved. “The tools of animation, video, and sound [can be used to] provide learners with working models that convey complex concepts” (Boyd & Murphrey, 2002, p.37). Gagne (1985) outlined nine steps that have guided the creation of quality instruction. These concepts included gaining attention, providing objectives, encouraging recall, the presentation of material, providing guidance and feedback while also encouraging/assessing performance and enhancing retention. While it is true that reusable learning objects do not necessarily address all of the steps explicitly. These steps provide a good guide for the creation of quality content that can meet the needs of today’s students.

Purpose

The purpose of this study was to investigate faculty perceptions of RLOs in order to better understand the creation process and use of RLOs to globalize the undergraduate curricula. A specific goal of the study was to document the following: 1) perspectives of the definition of an RLO, 2) challenges of creating and using RLOs, 3) benefits of creating and using RLOs, 4) best practices for development, and 5) best practices for use.

Methods and Procedures

A qualitative research design was used for this study. Participants were purposefully selected. According to Merriam (2009), criterion-based selections, or purposive samples, are selected based on identified, desirable characteristics. The participants were chosen based on their participation in the Trinidad Faculty Abroad experience. There were a total of eight faculty members who participated in the international experience and thus were selected for participation. Participants can be described as including both male and female faculty members with extensive teaching experience and adequate use of technology.

Each participant was engaged in a face-to-face, semi-structured pre and post-interview process (Merriam, 2009). The protocol contained open-ended questions about the objectives of RLOs and the creation process. The exact wording and order of the items were not predetermined; rather, they served as guiding questions for the researchers to explore identified topics and issues. Time was allowed for the participants to communicate any additional information and/or comments to the researchers. The same protocol was used for both the pre and post interviews. However, it was reworded for the post interview to encourage reflection on the experience and allow the researchers to identify any changes or impacts of the experience on the participants. The participants were coded (R2-R9) to ensure confidentiality.

Each interview, both pre and post, lasted approximately 30-40 minutes. The interviews were held in a location chosen by the participant so they would feel comfortable. Two researchers were present at each interview and took field-notes to record the participant’s responses. After the

interviews were completed, the researchers compared and compiled field-notes in a debriefing session to ensure the understanding and accuracy of the recorded responses; the data was compiled into one document. Follow-up interviews were conducted to further understand the best practices associated with RLO development and use. Participants were contacted by telephone, email, or in-person.

The establishment of trustworthiness (Lincoln & Guba, 1985) is critical within qualitative research and is dependent on creating credibility, transferability, dependability, and confirmability. Credibility was established through persistent observation, referential adequacy, and peer debriefing by the researchers (Erlandson, Harris, Skipper & Allen, 1993). Purposive sampling and the use of participant quotes enabled transferability, while the use of a reflexive journal and audit trail ensured dependability and confirmability (Erlandson et al., 1993).

The data was analyzed using the constant comparative method as described by Glaser and Strauss (1967). This method of qualitative data analysis is comprised of four stages: (a) comparing incidents applicable to each category, (b) integrating categories and their properties, (c) delimiting the theory, and (d) writing theory (Glaser & Strauss, 1967). The researchers unitized the data and categorized them into emergent themes. The themes were identified as perceived definitions of RLOs, challenges of RLO creation, benefits of RLO use, and best practices.

Findings and Discussion

Perspectives of the Definition of an RLO

During preflection, faculty participants articulated that a RLO is “information that would accomplish one learning objective. It may consist of printed material, web, audio, video, various opportunities to engage the student in that learning objective” (R8). RLOs package “content, case studies, and assessments” (R4) to address a topic. The responses are not surprising given that project planners had informed participants of RLO components during the initial faculty participant recruitment process. Participants also indicated that RLOs were easily transferable and usable by interested parties. Although only one of the faculty members had created RLOs in the past, the other seven faculty members indicated that they had created what they felt to be similar learning objects for their classes (e.g., case studies, annotated presentations, etc.).

In analyzing the reflection interview data, the experience affected the faculty’s understanding of the RLO creation process and content requirements. Faculty were more aware of the student’s perspective. “The experience changed my idea of a RLO; it made more important the need to provide as rich a context as possible” (R6). “[A RLO] should be contextually rich. It takes students virtually to a place and gives them a vicarious experience” (R8). Faculty participants also expanded their view/understanding of the content requirements. “The PowerPoint is just the beginning. You have to write the assessment, write the key of the assessment, provide enough information [for those that want to use your RLO]” (R3). “The expectations are to include more videos/interviews than I thought” (R4); RLO users need to be able “to put their own context to it to make it applicable to larger systems” (R8).

Challenges of RLOs

During preflection, participants indicated that the RLO creation process would not be difficult, but it would be most challenged by lack of time to work on the materials. “[RLO creation] will not be difficult, especially in terms of innovative ideas; the time constraint will be difficult” (R7). The lack of a set template was also a challenge for faculty. “It will not be hard after I identify a form” (R4). “I suspect there will be a lot of agonizing over the first one; then you get a work flow pattern established” (R3). Faculty indicated that the work may be made more efficient by collaborating with another faculty member through teamwork (R8, R9).

During post-reflection, the faculty spoke about the challenge to RLO creators to provide ample and vivid context for both the teachers and students that may review the content (R3, R6, R8). “The difficult part is creating the context. I feel the responsibility to create the context to make it hit home [with the students]” (R6). In addition to providing acceptable context, challenges also included issues related to time and layout. Challenges expanded to include filtering through and gaining access to all of the media that was collected. “The video I want, another faculty member has it; also, I don’t have access to all the pictures and video right now” (R8). Writing the script for the narration was also seen as a challenge (R2). Contrary to the faculty’s initial preflection to collaborate, not one RLO was created as a team effort.

Benefits of RLOs

During preflection, when asked about the potential impact of the RLOs on their curricula, faculty agreed that RLOs would not only extend the students’ understanding of the content, but would also provide the students with a broader perspective of the content (R2-R9). RLOs will allow students to “see how others do what we do in a different context” (R5) and “get students to think about broader, more varied context” (R6). Participants reported that RLOs would allow students to see an international setting and possibly correct their misconceptions of different cultures. “There are misconceptions of different cultures; [students] see them as third world and tribal versus having cities, etc.” (R7).

During post-reflection, the faculty expanded on the impact that the international experience and RLO development could have on their curricula. Faculty indicated that the RLOs would be welcomed by the students as a new teaching method. “Students will value that it is something that I experienced and created, not just a video I found” (R2). Faculty also responded that the RLOs would be much easier to present because they were a genuine experience. “I feel more comfortable presenting the information to students because it is a genuine experience; it will feel more real to the students” (R5). Respondent R8 indicated that RLOs are a new teaching method that could be incorporated into a teaching methods curriculum. Respondents also reported hope that the RLOs would increase the students’ awareness of opportunities abroad (R2, R4, R6, R7, R9). “I hope, if we do a good job, it would elicit more of a study abroad interest for our students” (R6) and an “increased awareness of opportunities abroad, such as study, research, and careers” (R9). The use of RLOs will not only provide students with an increased awareness of international opportunities, but will allow students to make global connections. “[RLOs] will provide students a different perception of how policies can impact the U.S. and how they impact other countries” (R7). As an extension, the faculty expressed hope that their RLOs can be used

by other faculty in their own disciplines and in other disciplines to make both global and cross-discipline linkages. “I see opportunities for the strengthening of relations between disciplines, such as agriculture, health, and urban planning” (R6).

RLO Best Practices for Development

Faculty provided reflections on best practices for RLO development. Faculty members made suggestions that affect every aspect of RLO creation, starting with the planning process. It was shared that the excitement and opportunities in the destination country can become overwhelming. Faculty suggested that RLO creators have a clear idea of the topic(s) that they want to address. “The trip provides you with so many valuable opportunities, ideas, and contacts that you get overwhelmed in the process” (R7); “…losing focus becomes easy. Having a concrete topic beforehand helps you to remain centered on the information you are looking for to assist you in creating a high quality RLO” (R9).

Every faculty member (R2-R9) indicated in the preflection that teamwork may be a beneficial component to RLO creation; in the end, not one RLO was created as a team effort. In reflecting on the best practices of teamwork in RLO creation, faculty had varied opinions. “I work well by myself, but teamwork is always good to stimulate each other. I guess I would favor it, but small teams, i.e., not more than two people per team” (R2). “I think utilization of teams would have been a good idea. This framework would have made participants accountable to other team members” (R7).

There were also mixed opinions about the type of media inclusion that should be used in RLOs. “I think video is more important because it includes audio and pictures” (R9); “I think [short videos] would be more effective [for student learning]” (R2). “I’m really glad I did the video segments, but I must admit, I spent an inordinate amount of time planning them, and they didn’t add as much as I thought they would” (R3). “Video with audio is best—but also most difficult. Audio over pictures is probably most realistic” (R8).

The most resounding best practice was to work on and try to complete the RLOs while still in the destination country. “Stick to the goal of having the RLO done BEFORE departing the country” (R3), “the problem is that once you got back to the U.S., other issues take precedence over the RLO” (R7). “I really do think the reflective work time in country is important” (R3).

Another suggestion was the use of a trip theme for the RLO topics to address. “Everyone would be writing toward the same learning outcomes…taking a team approach to developing a very targeted, comprehensive learning module; everyone contributes in the areas of their expertise” (R3). “This would allow for more utilization beyond case study focus” (R7).

RLO Best Practices for Use

Reflection indicated that RLOs may be best used as lesson enhancers versus primary lesson topics. One faculty member shared, “The most effective use of an RLO is to enhance a current topic in a course…reflect on the information in the course and use the RLO to improve global

understanding of the issue” (R9). “RLOs can be used best as interest approaches, as advanced organizers, as realistic problems. [They are] less valuable to teach specific content” (R8).

Conclusions

Respondents reported positive perceptions of RLOs both prior to and after their engagement in the development process. Thus, it can be concluded that for this group of faculty the idea of creating a small, reusable learning piece was not a new phenomenon, but rather the reintroduction of a process with a new name. During their reflection, faculty indicated that RLOs would be easily transferable and usable by others. However, as shared by Duval (2001), the sharing of material can be difficult. In fact, the literature clearly stated that the success of RLO use will depend on “standardization” (Duval, 2001) of RLO development. It is possible that the way in which the program was organized and administrated influenced the perception of the participants and caused them to feel that the RLOs developed as part of the program would be easily shared as a result of support from program staff.

Participation in the RLO development process appeared to have changed the participants’ perception of the type of content that should be used and the way that context should be used in the creation of RLOs. Post-reflections revealed that participants indicated a need for more video to be used in the RLOs developed in order to provide adequate context. It is possible that this is connected to the international focus of the RLOs.

Prior to participation, faculty did not perceive that the RLO creation process would be difficult. However, post-reflections revealed that faculty believed the development to be time consuming and require a higher level of technical ability than believed previously. It can be concluded that there is a need for increased support to be provided in terms of training and technical support. The use of video was specifically identified as an area where assistance was needed.

Findings related to collaboration and teamwork revealed that while these were aspects that faculty thought would be beneficial – they did not engage in collaboration or teamwork in the actual development of their own RLOs. It can be concluded that engagement in the RLO development process caused faculty to be more individual in their approach rather than working as teams and that there is a need to encourage teamwork and collaboration through project activities. Participants shared in post-reflections that they believed that quality and efficiency could be improved through collaboration.

Post-reflections revealed that faculty viewed the RLO development process as a means to bridge disciplines. It was concluded that one possible way to expand this would be to create RLO development teams from different disciplines and develop common goals regarding the content and characteristics to be included in the RLOs developed.

Comments related to RLO use and applications during reflection leads one to conclude that engagement in the process increased participants’ understanding and desire to use RLOs.

Implications

RLOs offer tremendous potential in regard to extending the reach of faculty to serve students in an efficient manner. However, it is recognized that challenges exist in regard to development and delivery. RLOs must be developed in a way that provides value to both instructors and ultimately to the students. The findings from this study revealed that faculty gained a stronger understanding of RLOs and their value through engagement in the process. Findings also revealed that while faculty may see value in the creation of RLOs, they recognize that the creation can be time consuming and require technical skills for quality development.

Recommendations for Practice

This study revealed recommendations for practice that can assist the profession in encouraging the development and use of reusable learning objects. A clear definition and description of how RLOs will be used must be provided to participants involved in the process. In addition, technical support should be provided that allows the faculty to focus on the content to be shared in each RLO. In addition, the use of metadata will be important as the RLOs are promoted for use by other faculty. While participants reflected that the RLOs they developed would be useful to others, it is not known to what extent RLOs have been utilized. Further, professional development in regard to effective development strategies and the use of media is critical.

Recommendations for Further Study

The focus of this study was limited to the perceptions of faculty involved in the development and use of RLOs related to an international experience. Additional quantitative research is needed that focuses on the adoption and use of RLOs developed as part of the project. For example, how many students were impacted as a result of the RLOs developed? How have the faculty involved selected to use the RLOs developed? How many faculty, outside of those who participated in the creation of the RLOs, have used the RLOs for instructional purposes? What impact have the RLOs had on students? Addressing these questions will allow further data to support or dispute the use of RLO utilization in agricultural education settings.

References

Boyd, B. L., & Murphrey, T. P. (2002). Evaluation of a computer-based, asynchronous activity on student learning of leadership concepts. Journal of Agricultural Education, 43(1), 36-45. doi: 10.5032/jae.2002.01036

Boyle, T. (2003). Design principles for authoring design principle for authoring dynamic, reusable learning objects. Australasian Journal of Educational Technology, 19(1), 46.

Brusilovsky, P. (2004). A distributed architecture for adaptive and intelligent learning management systems. International World Wide Web Conference, 104-113.

Churchill, D. (2007). Towards a useful classification of learning objects. Educational Technology Research and Development , 55, 479-497.

Conlan, O., Dagger, D., & Wade, V. (2002). Towards a standards-based approach to e-Learning personalization using reusable learning objects. In Proceedings of World Conference on E-learning in Corporate, Government, Healthcare, and Higher Education, 210-217.

Doerfert, D. L. (Ed.) (2011). National research agenda: America Association for Agricultural Eductaion’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Downes, S. (2001). Learning objects: Resources for distance education worldwide. International Review of Research in Open and Distance Learning, 2(1). Retrieved from http://www.irrodl.org/index.php/irrodl/index

Duval, E. (2001). Standardized metadata for education: A status report. In Montgomerie, C. and Jarmo, V. (Eds.), Ed-Media 2001, World Conference on Educational Multimedia and Hypermedia. AACE, 458-463.

Erlandson, D. A., Harris, E. L., Skipper, B. L., & Allen, S. D. (1993). Doing naturalistic inquiry: A guide to methods. Newbury Park, CA: Sage.

Farha, N. W. (2009). An exploratory study into the efficacy of learning objects. The Journal of Educators Online, 6(2). Retrieved from http://www.thejeo.com/Archives /Volume6Number2/FarhaPaper.pdf

Gagne, R. (1985). The conditions of learning (4th ed.). New York, NY: Holt, Reinhart & Winston.

Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. Chicago, IL: Aldine.

Hamid, A. A. (2002). e-Learning: Is it the “e” or the learning that matters? Internet and Higher Education, 4, 311-316.

Hodgins, W. H. (2002). The future of learning objects. In Proceeding of the 2002 eTEE Conference: e-Technologies in Engineering Education - Learning Outcomes Providing Future Possibilities, 76-82.

IEEE (2002). Draft standard for learning object metadata. New York: The Institute of Electrical and Electronics Engineers, Inc.

Jones, L., & Bjelland, D. (2004). International experiential learning in agriculture. Proceedings of the 20th Annual Conference, Association for International Agricultural and Extension Education, Dublin, Ireland, 963-964. Retrieved from http://www.aiaee.org/2004 /Carousels/jones-carousel-NEW.pdf

Kolb, D. A. (1984). Experiential Learning. Englewood Cliffs, NJ: Prentice-Hall.

Lincoln, Y., & Guba, E. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Love, G. M. (1964). Needed – more functional units of instruction. Journal of Agricultural Education, 5(2), 18-21. doi: 10.5032/jaatea.1964.02018

Lukasiak, J., Agostinho, S., Bennett, S., Harper, B., Lockyer, L., & Powley, B. (2005). Learning objects and learning designs: An integrated system for reusable, adaptive and shareable learning content. ALT-J: Research in Learning Technology, 13(2), 151-169.

Merriam, S. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA: Jossey-Bass.

Muzio, J. A., Heins, T., & Mundell, R. (2002). Experiences with reusable e-learning objects: From theory to practice. Internet and Higher Education, 5, 21-34.

Pate, M. L., & Miller, G. (2011). A descriptive interpretive analysis of students’ oral verbalization during the use of think-aloud pair problem solving while troubleshooting. Journal of Agricultural Education, 52(1), 107-119. doi: 10.5032/jae.2011.01107

Polsani, P. R. (2003). Use and abuse of reusable learning objects. Journal of Digital Information, 3(4). Retrieved from http://journals.tdl.org/jodi/index

Sicilia, M. M. & Lytras, M. D. (2002). Scenario-oriented reusable learning object characterizations. International Journal of Knowledge and Learning, 1(4), 332-341

Thoron, A. C., Myers, B. E., & Abrams, K. (2011). Inquiry-based instruction: How is it utilized, accepted, and assessed in schools with national agriscience teacher ambassadors? Journal of Agricultural Education, 52(1), 96-106. doi: 10.5032/jae.2011.0109

Vincenti, V. B. (2001). Exploration of the relationship between international experiences and the interdisciplinary work of university faculty. Journal of Studies in International Education, 5(1), 42-63.

Wulff-Risner, L., & Stewart, B. (1997). Using experiential learning to teach evaluation skills. Journal of Agricultural Education, 38(3), 43-50. doi: 10.5032/jae.1997.03043

Traditional and Social Media Channels Used by Texas Agricultural Producers

David L. Doerfert, Associate Chair & ProfessorLindsay Graber, M.S.

Courtney Meyers, Assistant ProfessorErica Irlbeck, Assistant Professor

Dept. of Agricultural Education & CommunicationsTexas Tech University

Abstract

Communication has been a key factor in bringing about change in agriculture in the past suggesting that social media technologies could be used to advance agriculture in the future, as well as keeping agricultural specialists up-to-date on current events and information. The increasing pace of advancements in agriculture and communications technology has created a significant need to monitor and adjust to changes in the communication behaviors of the various industry stakeholders and audience segments. The purpose of this study was to understand the current use of traditional and social media channels by Texas agricultural producers. By random sample, 3,000 farmers were surveyed to collect quantitative data related to communication technology use. Producers identified agricultural magazines being the primary channel type for most information types and decision-making needs. At this time, producers are not using social media technologies to access production-related information. However, unlike previous studies, producer use of the Internet is increasing and has become the primary means to access commodity market-related information. The authors recommend additional research examining the communication channel adoption patterns of producers before extensive use of these channels by agricultural communicators in their strategic and tactical information dissemination plans.

Introduction-Theoretical Framework

Communication has arguably been a key factor in bringing about change in agriculture in the past. This history suggests that social media technologies could be used to advance agriculture in the future, as well as keeping agricultural specialists up-to-date on current events and information (Anderson-Wilk, 2009). As these new communication technologies can be utilized for meaningful, targeted, interpersonal communications, these technologies could also facilitate awareness and engagement with a population (Anderson-Wilk, 2009).

By understanding an audience and its behavior, agricultural communicators are best able to communicate with members of the audience. This increased understanding can equate to conservation of company resources, such as time, efforts and monetary resources (Evans, 2010). However, an audience’s usage pattern can differ for various media channels and is influenced by a range of factors including subject matter and demographic characteristics (Ferguson, 1999).

Adoption of Communication Technologies

A historical review of diffusion research yields nine different traditions (Rogers, 2003). Of those nine different traditions, rural sociology and communication are of the most studied, with approximately 20 percent of research in rural sociology and 15 percent in communication (Rogers, 2003). Diffusion is the process that occurs when an innovation is communicated through various channels, over a period of time, to a body of people (Rogers, 2003). It can also be defined as a type of social change, where alteration occurs within a social structure. Rogers defined communication as the process in which people share information to reach a mutual understanding. With diffusion, the communication process is a special type of communication, and has the specific purpose to communicate new ideas (Rogers, 2003).

An early effort in diffusion research was the study by Ryan and Gross (1943) who examined differences between impulsive, instantaneous decisions and those that are made based upon a process. In the study, the rural sociologists determined that a typical farmer would attain information through different channels. The gathering of that information influenced farmers’ decision to adopt (or not adopt) a new hybrid of seed corn. This process has been further refined by Rogers (2003) and is regarded as the innovation-decision process.

Rogers (2003) discussed communication channels within various stages of the innovation-decision making process and noted that channels exhibit different roles in the process. Communication channels are either interpersonal (versus mass media) or localized (versus cosmopolite) (Rogers, 2003). Mass media channels, channels that transmit messages via mass medium, utilize a source (or sources) to: (a) reach a vast audience in quick manner, (b) create knowledge and disseminate information, and (c) change attitudes (with little strength) (Rogers, 2003). To change attitudes that are strongly held, Rogers referenced the use of interpersonal channels – those that involve face-to-face communication between two or more people. Rogers used that reference to explain the importance of peer communication with late adopters and laggards. Interpersonal communication channels can facilitate a two-way exchange of information with opportunity to clarify information or request further detail and act as a persuasion tool (Rogers, 2003). Rogers also acknowledged that diffusion through the Internet can increase the rate of adoption for certain innovations.

Media Channel Use

Rosengren (1974) stated that to stimulate motives for media use, communication should intermingle with social and psychological factors. To meet a specific goal, people will select a certain type of media based upon its ability to help meet that goal (Katz, Blumler, & Gurevitch, 1973). Drawing upon this Uses and Gratifications (U&G) theoretical foundation, researchers will commonly seek to understand two variables: audience needs and the relationship between media characteristics and audience requirements (Katz, Blumer, et al., 1973). One defining portion of the theory suggests that U&G theory researchers recognize that the audience may select more than one type of media and some

media choices may be older, more traditional types of media that satisfy needs in unchanged ways (Katz, Blumer, et al., 1973).

To explore the sources of media gratifications, Katz, Blumer, et al. (1973) noted that audience gratifications can be obtained from (a) media content, (b) exposure to the media per se, and (c) the social context that typifies the situation of exposure to different media. Each medium is different based upon the three aforementioned qualities. Media content also varies in topical choice and style characteristics (Katz, Gurevitch, & Haas, 1973). Exposure to method, more commonly referred to as channels, refers to the delivery route for a particular type of news or information. Variance in the channels’s exposure can occur and therefore may hinder or increase effectiveness of the medium (Katz, Gurevitc, et al., 1973).

Media channel attributes should also be studied to determine the likelihood that the new media are similar (or dissimilar) from the current media and can also satisfy like needs, thus causing gratification in the reader. Those needs that are “psychologically related or conceptually similar will be satisfied by media with similar attributes” (p. 515). Katz, Gurevitch, et al. (1973) consolidated 35 identified needs into five types: (a) needs related to strengthening information, knowledge, and understanding (cognitive needs); (b) needs related to strengthening aesthetic, pleasurable, and emotional experience (affective needs); (c) needs related to strengthening credibility, confidence, stability, and status (combination of cognitive and affective needs; labeled as integrative needs); (d) needs related to strengthening contact with family, friends, and the world (also integrative needs); (e) needs related to escape or tension-release which we define in terms of the weakening of contact with self and one’s social roles.

Cho et al., (2003) stated that users could also replace the types of media channels they commonly use. In these instances, once a new medium is selected and it performs comparable to the old medium, the user will potentially cease use of the older medium. Research has also shown that the audience is capable of verbalizing motives for selecting a type of media based on personal interests and that future researchers should explore significances in popular culture for an important contrast between the audience and uses and gratifications of the media (Katz, et al., 1974).

The Internet’s Impact on Channel Selection

When a new technology is created, hypotheses are made about that technology’s integration into society. Specific to U&G, the new technology is studied to determine if its media aspect can satisfy social and psychological needs of users (Cho, et al., 2003). Commenting on the Internet, Cho et al. (2003) explained that there is a wide breadth of content online versus the content in traditional media; that content is “virtually unregulated.” Papacharissi and Rubin (2000) conclude that the Internet has both interactive and informational qualities. Because of those qualities, the Internet could potentially meet the same user needs as more traditional media.

With credit to prior work by Webster and Wakshlag (1983), McQuail (2005) developed an integrated model of media choice that represents both the audience and media sectors (Figure 1). Conducive to U&G, this model includes eight factors that affect an individual’s selection of media. McQuail described media-related needs as those that benefit the individual; individuals may seek various types gratification from media such as “information and education, guidance and advice, diversion and relaxation, social contact, value reinforcement, cultural satisfaction, emotional release, identity formation and confirmation, lifestyle expression, security, sexual arousal and filling time” (p. 428). The integrated model of media choice interrelates audience factors and media factors to provide explanation for media choice. These two sets of factors are independent but can influence each other in decision-making for media use (the final point on the model) (McQuail, 2005). Each factor is judged on its relative distance from the end point of media use.

Figure 1: An Integrated Model of Media Choice (McQuail, 2005)

With the recent emergence of social media technologies (e..g. blogs, Facebook, Twitter), additional consideration should be given to the use of social media when developing communication and educational outreach strategies. One characteristic that differentiates social media from traditional media channels is that users have different levels, or types, of ways to use the channel (Sterne, 2010). It is possible for a person to strictly utilize social media for information and news consumption; participation and the establishment of an active online network is not necessary for media consumption (Evans, 2010). Therefore, it is advantageous for communications planners to know their audience and its behavior online. In return, social media strategy and planning must be flexible to accommodate rapid changes (Evans, 2010).

Related Research in Agricultural Communications

Recent research reported that agricultural producers employ various media channels to attain news and information about the agriculture industry (Readex Research, 2010; National Association of Farm Broadcasters, 2008). These research studies have shown that utilization of the Internet, for information and news-seeking purposes, has increased over time. However, the Internet was not found in these studies to be the primary or secondary selected communication channel. In the American Business Media Agri Council’s 2010 media channel study (Readex Research, 2010), the results indicated general support of print media channels (magazines and newspapers), while the National Wave Study (National Association of Farm Broadcasting, 2008) reported extensive use of broadcast radio within the agricultural industry.

Outside of the studies completed by industry-based professional organization, there is limited research within the agricultural communications profession that examined communication channels use within the industry. Wilkinson (2009) found that Texas agricultural producers utilize more than 10 channels for news and information, with magazines and television broadcasts being the most popular. The least used channel for obtaining agriculture news and information is blogs. As inferred, the traditional media channels were used more frequently than newer, Web-driven channels, such as blogs (Wilkinson, 2009).

Purpose and Objectives

The purpose of this study was to understand the current use of traditional and social media by adult agricultural producers in Texas. The contents of this manuscript reflect a portion of a master’s thesis completed by the lead researcher. Related to the AAAE research priority focused on new technologies, practices and product adoption decision (Doerfert, 2011), the results of this study are foundational to future research with this and the other research AAAE research priorities. The following research objectives were used to guide this study:

1. Describe respondents through personal demographic variables: age, gender, ethnicity, highest education level attained, primary profession, length of time farming, current household income, and political affiliation.

2. Describe respondents through production agriculture-related demographics of acres owned/rented during 2011 and the agriculture commodities they produce, including the primary commodity produced.

3. Describe respondents in terms of the media channels they utilize and the frequency that they access production and industry-related news.

4. Determine potential differences in media channel use by respondents when making short-term and long-term production decisions.

Methods and Procedures

This study used descriptive survey research methodology by means of a questionnaire mailed to agricultural producers in Texas. The mail survey method was selected to reach a large quantity of people for data collection within the study’s financial means and allowed the researcher to ask a variety of questions to a large amount of people. Mailing address data was accessible for the targeted population, whereas telephone numbers and email addresses were not readily available. Additionally, the researcher elected to not administer an online survey as not all of the members of the population may have Internet access or utilize the Internet. Dillman, Smyth, and Christian (2009) identified four different types of error that could occur: sampling error, non-coverage error, non-response error, and measurement error. Efforts to decrease each type of error were conducted in this study. The Institutional Review Board (IRB) at Texas Tech University granted the researcher permission to complete this study.

The target population for this study was Texas agricultural producers. Initial efforts sought to secure mailing lists of the state’s various agricultural commodity organizations as these lists would include the desired agricultural producers. However, it was discovered that such lists did not exist, were incomplete, or were not available for external use. Using an alternative source to establish a population frame, the researcher obtained a list of mailing addresses from the United States Department of Agriculture (USDA) Farm Service Agency (FSA). This mailing list was comprised of agricultural producers who had a connection to agricultural land in Texas and had worked with the agency or received any type of subsidy or payment from the USDA. The members of this list could be landowners, renters, or both. The resulting population frame from FSA was 93,460 potential subjects.

To select the sample of 3,000 from the final FSA population, a random sample was drawn through the use of a Microsoft Office Excel 2007 (Excel) random number generator. To reduce frame and sampling error, each population members’ name and mailing address that exhibited any of the following attributes were eliminated from the sample and replaced with another randomly selected name if the mailing address was (a) outside Texas; (b) to a bank or other financial institution; (c) to an estate or trust; (d) to a business or organization that is not directly affiliated with farmers or ranchers in the aspect of agriculture; or (e) of someone who participated in the panel of experts to examine the instrument’s face and content validity.

A researcher-developed instrument was used for this study. The first section of the instrument contained frequency-type question and multiple question, multiple answer-type questions to describe respondents in terms of the media channels they utilize and the frequency that they access production-related news (objectives 3 & 4). The final section of the instrument collected personal and agricultural demographic information from the respondents (objectives 1 & 2). In the first section, respondents were initially asked to identify the importance of specific characteristics of media sources. A total of 12 characteristics were evaluated in terms of importance using a six-point, Likert-type scale (1 = very important; 6 = very unimportant). Respondents were then asked to identify which media channel they use to seek information related to the primary commodity of

their farming/ranching operation and for the type of decisions (short-term (daily/weekly), long-term). The eighteen types of media sources differed in type and were categorized as either print, broadcast, face-to-face, web or social media. Respondents could indicate differences in their media channel use by seven categories of information they may seek. These information categories were weather, water management, ag-related policy, crop production, commodity markets reports, livestock production, and trade-related news.

To decrease possible measurement error, the instrument was subjected to review by a panel of experts (n = 12) comprised of agricultural producers, agricultural education and communications faculty members and graduate students, agricultural economics faculty members, and various affiliates of the agricultural industry in Texas. This set of experts was selected based upon their level of knowledge pertaining to the questionnaire subject matter and the overall survey research process. The panel’s review of the instrument sought to establish face and content validity. Following the review, suggestions from the panel were evaluated and used to revise the instrument prior to mailing.

Members of the sample were contacted four times throughout the data collection process. An envelope with a letter describing the study, the survey instrument and a return envelope for the instrument was mailed to all 3,000 members of the sample on June 29, 2011. Approximately one week after the first mailing of the instrument, on July 9, 2011, a reminder card was mailed to all 3,000 mailing addresses in the sample. On July 23, 2011, another letter describing the study, the survey instrument, and a return envelope for the instrument were mailed to members of the sample who had not returned the survey instrument. A final reminder card was mailed to those who had received a second copy of the instrument; this card was mailed on July 30, 2011, three weeks after the first mailing of the instrument. Data collection ceased on September 2, 2011.

Each envelope mailed from Texas Tech University had a return address with the researcher’s name; beside the researcher’s name was the coding number for each instrument. This coding number was also placed as part of the researcher’s contact information on the bottom of each letter mailed to members of the sample. Return envelopes had the researcher’s name and department address in both the sender and receiver fields. Placement of the coding numbers was selected to be discrete and avoid removal by study participants. First-class postage was applied to all pieces of mail. To increase response rate, return envelopes were provided (Dillman, et al., 2009).

There were a total of 805 responses to the mail survey; with an initial mailing to 3,000 people, 805 responses equated to a response rate of 26.8%. Incomplete surveys were not included in the final data analysis yielding a usable data set of 542 respondents. For nominal and ordinal data, descriptive statistics were selected, and measures of central tendency (means, medians, modes, and ranges) were calculated. The researcher utilized SPSS v.18 for Windows to calculate each statistic.

A post-hoc test was used to determine the reliability of the instrument. Reliability was calculated for both early (responded to initial mailing or first reminder) and late responders providing insight if differences existed between response groups. Calculated

means were used, per split file for each data set, to establish reliability, and the Cronbach’s alpha based on standardized scores are reported. Overall, 0.93 (early responders) and 0.94 (late responders) alpha scores were found, indicating a high level of reliability of the instrument and no significant difference between early and late responders.

Findings

Personal and Agricultural Demographics (Objectives 1 and 2)

The mean age was 62.83 years (SD = 14.00). A greater amount of males (n = 416, 80.3%) responded to the survey, and a majority of respondents were white (n = 500, 96.2%). Most respondents indicated that they have at least some college education (n = 429, 79.1%) with the largest amount of respondents (n = 192, 36.9%) awarded a bachelor’s degree. Respondents’ current primary occupations varied though the largest amount indicated that farming/ranching was their primary occupation (n = 319, 62.0%); the majority had been farming/ranching an average of 32 years (M = 32.04; Mdn = 33.50). Income levels varied from under $30,000 to above $100,000, with most (n = 181, 40.8%) respondents indicating an annual, household income over $100,000. A majority of respondents were Republicans (n = 290, 60.3%).

The amount of owned acreage by the respondents ranged from 0 acres to 30,000 acres, with a mean of 987.2 (Mdn = 328.0). The mean for rented acreage was 1,066.7 (Mdn = 150.0). All 542 respondents indicated at least one commodity produced with the majority (n = 292, 53.9%) raising some beef cattle. When indicating the primary commodity of their operation, beef cattle (n = 131, 34.8%) and cotton (n = 131, 34.8%) led the responses.

Media Channel Use to Access Information (Objective 3)

To understand media usage patterns, the researchers sought to identify the importance of specific characteristics of media sources. A total of 12 characteristics were evaluated (Table 1) with most respondents regarding the various characteristics as very important. While the respondents deemed all characteristics important, accuracy of the information received was indicated as being the most important.

Table 1Importance of Media Source CharacteristicsCharacteristic n M SDThe news source is accurate. 269 1.24 0.49The news source is credible. 453 1.57 1.07The news source is reliable. 451 1.57 1.07Content is informative. 437 1.76 1.05Content released is current. 435 1.77 1.06

I find the information to be practical. 438 1.87 1.05Information is objective. 416 1.90 1.11The news source is fair and impartial. 433 1.93 1.24The news source and content is readily accessible. 425 1.99 1.10The news source is convenient. 432 2.01 1.11I do not have to pay a fee for the news source. 437 2.22 1.41A news sources’ political views are similar to mine. 419 3.20 1.49

NOTE: A six-point Likert-type scale was used (1 = very important; 6 = very unimportant).

Of the seven different types of agriculture information examined, magazines were the most commonly used media channel for five different types of information: water management (n = 87, 19.5%); agriculture-related policy (n = 126, 28.2%); crop production (n = 129, 28.9%); livestock production (n = 121, 27.1%); and agriculture-related trade news (n = 70, 15.7%) (Table 2). For weather-related news, respondents preferred television shows (n = 130, 29.1%). For commodity market reports, the Internet was the preferred media channel (n = 115, 25.7%). Social media channels were consistently the least popular media channel choice.

Table 2

Media Channel Use by the Type of Production Agriculture Information Being Sought (N = 447)

Production Agriculture-related Information Type

Agriculture-related Media Channel Used

WeatherWater

Managemt.Ag-related

PolicyCrop

ProductionLivestockProduction

Trade News

Market Prices

f % f % f % f % f % f % f %

PrintMagazines 36 8.1 87 19.5 126 28.2 129 28.9 121 27.1 70 15.7 59 13.2Newspapers 50 11.2 60 13.4 93 20.8 83 18.6 96 21.5 57 12.8 72 16.5Supplier newsletter 7 1.6 12 2.7 13 2.9 29 6.5 18 4.0 13 2.9 11 2.5

Broadcast

TV Shows 130 29.1 28 6.3 50 11.2 49 11.0 49 11.0 35 7.8 61 13.6Local Radio Shows 85 19.0 25 5.6 43 9.6 44 9.8 44 9.8 23 5.1 60 13.4Satellite Radio 47 10.5 8 1.8 6 1.3 5 1.1 7 1.6 7 1.6 9 2.0

Digital Media/Internet

Web sites 93 20.8 39 8.7 61 13.6 70 15.7 64 14.3 58 13.0 115 25.7Mobile Internet/Apps 77 17.2 8 1.8 13 2.9 15 3.4 16 3.6 12 2.7 40 8.9e-Newsletter 21 4.7 27 6.0 51 11.4 40 8.9 25 5.6 24 5.4 31 6.9RSS feed 5 1.1 2 0.4 3 0.7 2 0.4 2 0.4 0 0.0 7 1.6

Interpersonal

Peers 55 12.3 59 13.2 54 12.1 75 16.8 69 15.4 42 9.4 53 11.9Extension Agents 11 2.5 63 14.1 65 12.0 73 16.3 43 9.6 14 3.1 16 3.6

Trade Shows 21 4.7 26 5.8 25 5.6 35 7.8 32 7.2 18 4.0 24 5.4Conferences/seminars 6 1.3 29 6.5 33 7.4 31 6.9 22 4.9 14 3.1 17 3.8Commodity group correspondences

7 1.6 10 2.2 26 4.8 23 5.1 8 1.8 21 4.7 29 6.5

Social Media

Blogs 1 0.2 6 1.3 5 1.1 2 0.4 0 0.0 0 0.0 3 0.7Facebook 2 0.4 3 0.7 4 0.9 4 0.9 2 0.4 0 0.0 0 0.0Twitter 0 0.0 0 0.0 2 0.4 1 0.2 0 0.0 0 0.0 0 0.0

Note. The media channel with the greatest use by producers for each production agriculture-related information type are in bold face.

Media Channel Use to Make Production Decisions (Objective 4)

For decision-making, the most frequently used channel for short-term decisions was magazines (n = 263, 58.4%) (Table 3). After magazine usage, the most used sources were peers (n = 213, 47.3%); newspapers (n = 206, 45.8%); Extension agents (n = 176, 39.1%); and Internet sites (n = 156, 34.7%). Combined, these sources represent three of the five media channel categories—print, interpersonal, and digital media/Internet.

Respondents also indicated the type of media channel used to aid in the decision-making process for long-term decisions. Magazines (n = 281, 61.9%) are the most-used to channel in making long-term production decisions. The remaining four of the top five most-used channel included peers (n = 230, 50.7%); newspapers (n = 215, 47.7%); Extension agents (n = 199, 43.9%); and farm/ranch shows (n = 144, 31.7%).

Table 3

Producer Media Channel Use by Decision Type (N=454)

Decision TypeShort-term Long-term

Media Channel Type f % f %Agriculture magazines 263 58.4 281 61.9Peers 213 47.3 230 50.7Agriculture newspapers 206 45.8 215 47.4Extension agents 176 39.1 199 43.9Agriculture Internet sites 156 34.7 131 28.9Agriculture television shows 143 31.8 141 31.1Farm/ranch shows 122 27.1 144 31.7Agriculture radio shows 120 26.7 102 22.5Agriculture e-newsletters 120 26.7 119 26.2Agriculture conferences/seminars 92 20.5 131 28.9

Agriculture supplier/manufacturer newsletters 71 15.8 102 22.5

Mobile Internet access for agriculture purposes 59 13.1 46 10.1

Commodity groups 53 11.8 62 13.7Satellite radio 27 6.0 25 5.5Agriculture blogs 9 2.0 9 2.0RSS feeds for agriculture news 7 1.6 10 2.2Agriculture Facebook posts 5 1.1 4 0.9Agriculture Twitter feeds 1 0.2 1 0.2

Note. Respondents could select more than type of media source per specific type of information, percentages do not equal 100%. Short-term decisions were defined to respondents as those made on a daily or weekly basis. Long-term decision were defined as those made less frequently than short-term decisions.

From daily or weekly (short-term) to long-term decision-making, there was no change in the selection of five lesser-used media channels. However, the most consistently underutilized media channels included satellite radio (n = 25, 5.5%); RSS feeds for agriculture news (n = 10, 2.2%); blogs (n = 9, 2.0%); Facebook posts (n = 4, 0.9%); and Twitter feeds (n = 1, 0.2%).

Conclusions and Recommendations

The average respondent to this study were older, white males with some post-secondary education. For the majority of the respondents, farming/ranching was their primary source of income on a farming/ranching operation with slightly more rented acreage than owned. These respondents are experienced in production agriculture with a career that commonly exceeds three decades. It can be argued that these respondents are subsequently experienced in accessing and analyzing business-related information and making both short-term and longer-term decisions that have impacted the success of their production enterprise.

Similar to the American Business Media Agri Council’s 2010 media channel study (Readex Research, 2010), the results of this study found agricultural magazines as the most popular media channel used for all types of information, except weather and commodity market reports. Agricultural television shows are the most popular for weather news followed by weather-related Internet sites/pages. Internet sites/pages were the most popular channel to about commodity market prices. In terms of media channel use for decision-making purposes, producers utilize print media (specifically magazines) as a first preference for attaining information related to short and long-term production decisions and various types of information related to their operation. From these results, the Internet has surpassed broadcast channels as a means to access information for short-term decisions and is closely behind broadcast channels (< 3%) for use in long-term decisions.

As reported, recent research studies (Readex Research, 2010; National Association of Farm Broadcasters, 2008) have shown that utilization of the Internet, for information and news-seeking purposes, has increased over time. However, in those studies, the Internet was not found in these studies to be the primary or secondary selected communication channel. Further, the National Wave Study (National Association of Farm Broadcasting, 2008) reported extensive use of broadcast radio within the agricultural industry. Three years later, it appears for the results of this study that this conclusion is no longer the case. It is unclear as to whether the use of the Internet for weather and market information reflects the dynamic nature of these information types, is an increase in the adoption of this media channel, or is a combination of these factors. As both weather and commodity market information may have a greater role in short-term decisions (those made on a daily or weekly basis), the answer to this question may also explain the extent of the Internet’s use in the production-related decisions made on a more frequent basis. Further complicating this potential change in media channel use is the influence of mobile communications hardware and high-speed connectivity to information sources. Cho et al., (2003) stated that users could replace the types of media channels they commonly use when a new channel is selected and it performs comparable to the old channel. For the producers in Texas, change in channel use is occurring. What is not clear is the factor or combination of factors that are driving this change in channel use. Additional research is recommended to answer these and other questions.

As no one media channel surpassed 30% in terms of the number of producers using that channel for a type of information, it is clear that agricultural producers are multiple channel consumers. This mirrors earlier findings that agricultural producers employ various media channels to attain news and information about the agriculture industry (Readex Research, 2010; National Association of Farm Broadcasters, 2008). As stated earlier, Uses and Gratifications (U&G) research seeks to understand two variables: audience needs and the relationship between media characteristics and audience requirements (Katz, Blumler, et al., 1973). Katz, Blumer, et al. recognized that an audience may select more than one type of media channel and the results of this study confirm that previous finding.

As stated earlier, Katz, Gurevitch, et al. (1973) consolidated 35 identified needs into five types. Based on the characteristics or media sources that producers stated were important, it would appear that two of the need types—needs related to strengthening information, knowledge, and understanding (cognitive needs) and needs related to strengthening credibility, confidence, stability, and status (combination of cognitive and affective needs; labeled as integrative needs)—are the most important needs to producers. Additional research is recommended to examine these gratification needs types and their relationship to producer media channel use.

As evident in the results, change in media channel use by producers is occurring. What is not known from these results is whether the new social communication channels are in the beginning stages of adoption or have already been rejected by producers as a viable

alternative to their current channels. This research should be replicated in other states to confirm its results with other agricultural producers and related stakeholder populations.

As previously referenced, McQuail (2005) developed an integrated model (Figure 1) of media choice that explained media choice from two perspectives, the audience and the media. The model includes eight factors that influence an individual’s selection of a certain type of media, and those aspects impact producers’ media selection. From the results of this study, it appears that one of the most influential aspect is the media-related need as producers will use different types of media channels based on the type of information they need. As such, practitioners should consider the type of information be disseminated when selected media channels as part of their strategic and tactical communication plans.

Finally, this research served as baseline data for agricultural communications and outreach efforts in Texas. Further research should be completed to determine the return on investment for communication efforts. While producers are multi-channel consumers, resources (human, fiscal, and technological) for information dissemination efforts are often limited. The results will allow agricultural communicators to more effectively and efficiently construct communications plan that facilitate end-user information analysis, knowledge creation, and decision-making processes.

References

Anderson-Wilk, M. (2009). Changing the engines of change: Natural resource conservation in the era of social media. Journal of Soil and Water Conservation, 64(4), 129A-131A. doi:10.2489/jswc.64.4.129A

Cho, J., Zuniga, H. G., Rojas, H., & Shah, D. V. (2003). Beyond access: The digital divide and internet uses and gratifications. IT & Society, 1(4), 46-72.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method. Hoboken, New Jersey: John Wiley & Sons, Inc.

Doerfert. D. L. (Ed.) 2011. National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Evans, L. (2010). Social media marketing: Strategies for engaging in Facebook, Twitter & other social media. Indianapolis: Que Publishing.

Ferguson, S. D. (1999). Communication planning: An integrated approach. Thousand Oaks: SAGE Publications, Inc.

Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. American Association of Public Opinion Research, 37(4), 509-523. doi:10.1086/268109

Katz, E., Gurevitch, M., & Haas, H. (1973). On the use of mass media for important things. American Sociological Review, 38(2), 164-181.

McQuail, D. (2005). McQuail's mass communication theory. Thousand Oaks, CA: Sage Publications, Ltd.

National Association of Farm Broadcasters. (2008). NAFB wave research study: National producer media-use wave study. National Association of Farm Broadcasters.

Papacharissi, Z., & Rubin, A. M. (2000): Predictors of Internet use. Journal of Broadcasting & Electronic Media, 44(2), 175-196.

Readex Research. (2010). American Business Media Agri Council: 2010 media channel study. Stillwater, MN: Readex Research.

Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.

Rosengren, K. (1974). Uses and gratifications: A paradigm outlined. In J. Blumler, & E. Katz, The uses of mass communications: Current perspectives on gratifications research (pp. 269-286). Beverly Hills, CA: Sage.

Ryan, B., & Gross, N. C. (1943). The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology, 8(1), 15-24.

Sterne, J. (2010). Social media metrics. Hoboken: John Wiley and Sons, Inc.

Webster, J., & Wakshlag, J. (1983). A theory of TV program choice. Communication Research, 10(4), 430-436. doi:10.1177/009365083010004002

Wilkinson, J. (2009). The relationship of trust and personality factors of a knowledge source on the information-seeking behaviors of agriculture professionals (Master’s thesis, Texas Tech University). Retrieved from http://thinktech.lib.ttu.edu/handle/2346/521.

Factors Influencing Membership in Community Supported Agriculture in Texas

A growing concern for the future of farming in the rural-urban interface exists since the area may experience a disconnect among consumers, farmers, and food production. To maintain local agricultural production, Texas producers living in the rural-urban interface operate community supported agriculture (CSA) enterprises that sell shares of products harvested on their farms to individuals referred to as shareholders. CSAs experience a high annual attrition rate of their shareholders. Little to no research has discovered the factors influencing individuals to join a CSA in Texas. The researchers administered an online survey to shareholders of three Texas CSAs to identify their motivations, their attitudes and values toward the environment, and their level of community attachment. Shareholders desire fresh food products free of pesticides and desire to support local community members who grow food. For environmental attitudes, shareholders believe humans abuse the environment and the balance of nature is delicate and easily upset. Texas producers can use the information from this study to write messages used in community-based social marketing campaigns that recruit new individuals to join a CSA and retain existing shareholders. Additional research needs to identify additional factors that influence Texas shareholders, such as their demographic characteristics and information channels.

Factors Influencing Membership in Community Supported Agriculture in Texas

Introduction

The number of individuals living on U.S. farms comprises less than 2% of the U.S. population (United States Department of Agriculture, 2010). However, the population has been growing in American rural counties, with rural counties located closest to urban areas experiencing the most growth (Sharp, Imerman, & Peters, 2002). Concern exists for the future of farming in the rural-urban interface since the area may experience a disconnect among consumers, farmers, and food production (Sharp et al., 2002). Many non-farming residents express concern about agricultural production practices, dust, agricultural odors, environmental quality, food safety, and food quality. A result of these concerns is the lack of confidence non-farming residents may have in local farmers and America’s food system (Sharp et al., 2002). As a means to improve issues arising at the rural-urban interface, agricultural professionals recommended that neighboring farmers and non-farmers build trust and understanding to help alleviate conflicts (Abdalla & Kelsey, 1996; James, 1999). Other professionals suggested neighboring farmers and non-farmers in a community connect and improve their understanding of the food system through local food production (Groh & McFadden, 1997).

Farmers living in rural-urban interface areas are adopting direct-to-consumer marketing strategies to maintain their local agriculture and to overcome constraints imposed by non-farm neighbors’ attitudes toward agriculture (Sharp et al., 2002). A subscription CSA is a type of direct-to-consumer agricultural enterprise where a farmer makes the management decisions, but individuals, known as shareholders, pledge support by purchasing either a full share or half share of a portion of the products harvested on the farm (Brown & Miller, 2008). The share price can cover operating costs, taxes, insurance, housing, or the farmer’s labor (Brown & Miller, 2008). In years with disastrous yields or poor weather, shareholders receive limited produce while the farmer still receives a living wage. In years with good yields, shareholders receive more produce, and the farmer receives a better living wage. Some CSAs require shareholders to help work on the farm. Shareholders may pick up their share at the farm, while other CSAs deliver shares to a centralized location, a farmers’ market, or the home (Woods, Ernst, Ernst, & Wright, 2009). Most CSAs offer a variety of vegetables, fruits, and herbs; some CSAs also provide shares in eggs, meat, milk, baked goods, fiber, honey, beeswax, and firewood (Brown & Miller, 2008).

Consumers throughout the United States have shown their interest in local foods by purchasing produce through CSA membership (Cone & Myhre, 2000; Conner, 2003; Hassanein, 2004; Kelvin, 1994; Lang, 2010; Polimeni, Polimeni, Shirey, Trees, & Trees, 2006; Zepeda & Li, 2006). As a means to meet this local food demand, roughly 120 CSAs in Texas have provided a portion of the products harvested on farms to consumers who pledge monetary support (Local Harvest, 2011). Yet little to no research has explored the factors influencing Texas consumers’ motivations for joining a CSA, particularly their attitudes and values toward the environment. The knowledge gained from this study could provide Texas fruit and vegetable producers with information used to develop a community-based social marketing campaign for recruiting individuals to

join a CSA and retaining existing shareholders. Many farmers have equated marketing with selling their products rather than having a clear understanding of consumers’ wants and needs (Adam, Balasubrahmanyam, & Born, 1999). Marketing to consumers’ wants and needs is considered the most important factor that determines the success of an alternative agriculture enterprise. This type of marketing requires that farmers deliver products that make a profit and satisfy consumers’ wants. One National Research Agenda (Doerfert, 2011) priority was addressed: “Priority 2: New Technologies, Practices, and Products Adoption Decisions (p. 16).

Theoretical FrameworkAttitude Theory

Fishbein (1967) has established relationships between an individual’s beliefs of attributes of an object and the formation of the individual’s overall attitude toward the object. His work has shown evidence that an individual’s attitude toward an object is positively associated with the individual’s intentions to use the object (Ajzen & Fishbein, 1980).

Attitudinal characteristics are factors for explaining consumers’ motivation to join a CSA in the United States (Zepeda & Li, 2006). One of the major motivations relates to quality traits in food, such as fresh, organic, and seasonal. Fifteen members of a CSA in the northeast indicated that organic, fresh produce grown by a local farmer was the most popular reason for joining the CSA (Kelvin, 1994). These members expressed that the local farmer was excellent at providing fresh, tasty, seasonal produce. In a survey, shareholders indicated they joined the Roxbury Farm CSA because they desired fresh, organic vegetables; wanted to consume seasonal vegetables; and were concerned for the environment (Polimeni et al., 2006). Almost all of the respondents from the Roxbury Farm CSA (99%) reported that receiving fresh vegetables was either very important or important in their motivation for joining the CSA (Polimeni et al., 2006). Ninety-three percent of the Roxbury Farm CSA respondents agreed that receiving organic vegetables was either a very important or important factor in their decision to join. Similarly, data collected from members of eight CSAs in the Twin Cities area of Minnesota indicated several attitudes that motivated them to join including concern for a healthy environment, availability of organic foods, desire for fresh produce, knowledge of how and where their food was grown, desire to eat vegetables in season, and desire to reduce packaging (Cone & Myhre, 2000). Members of two CSAs, located in Ithaca, New York, ranked freshness as the most important trait considered in their decision to join a CSA, followed by locally grown and organically grown (Conner, 2003). Other important traits influencing the decision making process of CSA members in Ithaca, NY were variety of products, size of their share, ease of pickup or delivery, and season length (Conner, 2003). The two most common motivations for joining the From the Group Up CSA in Maryland were wanting locally grown produce (86%, n = 111) and organic produce (84%, n = 108) (Lang, 2010).

Research has indicated that health concerns have motivated consumers to join CSA programs. When ask about the benefits of joining, CSA members in Missoula, Montana, said it was important to do something good for their health (Hassanein, 2004).

Shareholders of the Redwood Roots Farm CSA in Humboldt County were concerned with healthy food for themselves and society at large (Buckley, 2009).

Environmental issues have motivated consumers to support alternative food systems, particularly organic food production. Specific environmental issues motivating consumers were soil erosion, fossil fuel dependency, pesticide contamination, and depletion of wetlands, prairies, and wildlife habitat. The literature has less research on the relationship of environmental concern and local food production. In 2004, Hassanein reported that 82% of CSA members in Missoula, Montana, said supporting efforts to protect the environment was a benefit gained from their membership.

FoodRoutes Network, a national non-profit organization supporting local, community-based food systems in the United States, suggested marketing campaign ideas derived from consumers’ attitudes and behavior toward consuming locally grown food (Greenberg Quinlan Rosner Research, 2002). The organization reported that many shoppers believe locally grown products are fresher and of better quality. Shoppers were more supportive of buying local products after learning the benefits of sustaining their communities and local economy, supporting local farmers, and getting superior quality food. A minority of shoppers purchased locally grown products at CSAs. The organization recommended increasing consumption of locally grown products by increasing awareness of where to find locally grown food, delivering messages about why purchasing locally grown products is important, and removing barriers toward buying locally grown products (Greenberg Quinlan Rosner Research, 2002). Furthermore, messages need to relate back to shoppers’ values and attitudes toward locally grown products.

Community Attachment

Community attachment explains individuals’ positive feelings toward the place in which they live (Hummon, 1992). The two most widely studied concepts related to community attachment are place attachment and sense of place (Brehm, Eisenhauer, & Krannich, 2004). Place attachment explains the emotional attachment toward a particular geographic locale whether it is an individual’s house, neighborhood, or community (Brehm et al., 2004). Sense of place refers to individuals’ thoughts, feelings, and beliefs about their place of residence (Hummon, 1992). Another definition of sense of place describes community attachment as positive beliefs and feelings toward a place that leads individuals to develop a sense of belonging and meaning to their lives (Brehm, 2003). Individuals who have a feeling of rootedness indicate their strong feelings of community attachment.

Studies focusing on community attachment as a factor that motivates consumers to join a CSA have shown mixed results (Brehm & Eisenhauer, 2008). Bregendahl and Flora (2006) measured attachment as a factor influencing consumers to join a CSA in Iowa. While the CSA owner in Iowa worked to develop attachment with and among consumers, consumers were more interested in improving their own well being by purchasing food that was healthy and better for the environment. Consumers still considered support of local farmers as an important factor impacting their decision to subscribe to the CSA. In Missoula, Montana, 93% of CSA members also reported that supporting local farming was very important in their decision to join a CSA

(Hassanein, 2004). Furthermore, members in a CSA ranked supporting local producers as the second most important reason for joining (Hinrichs & Kremer, 2002). To further support community attachment as a motivating factor, 91% of Roxbury Farm CSA respondents (n = 234) indicated that supporting a local farm was either very important or important in their membership decision (Polimeni et al., 2006). Similarly, roughly 51% of respondents (n = 131) from the Roxbury Farm CSA joined the CSA because they wanted a stronger sense of community (Polimeni et al., 2006). While the previous studies found that community attachment were important factors explaining shareholders’ membership in a CSA, a few studies have found that community attachment was not relevant. In a study of CSAs in Ithaca, New York, the researchers discovered that community attachment was not important in their decision to join a CSA; the least important factors were the connection to the farm or community and sense of community (Conner, 2003). Other low rated reasons for joining the CSA in Ithaca included a sense of doing something with a community and an opportunity to attend festivals and events.

Community-Based Social Marketing

Community-based social marketing (CBSM) is a strategy for fostering sustainable behavior in agriculture (Kennedy, 2010; McKenzie-Mohr & Smith, 2011). CBSM is founded on the belief that personal contact at the community level is the most effective approach to change behaviors (Kennedy, 2010). This approach involves (1) identifying barriers and benefits to performing the sustainable behavior, (2) designing a strategy that uses behavior change tools, (3) piloting the strategy with a small segment of a community, and (4) evaluating the behavior change once it has been adopted in the community.

Community-based social marketers must recognize the internal and external barriers to performing a sustainable behavior and understand these barriers differ among community residents (McKenzie-Mohr & Smith, 1999). Internal barriers can include the lack of knowledge regarding how to perform the behavior; external barriers include structural changes that must be made to make the sustainable behavior easier to perform. Community-based social marketers develop a social marketing strategy to remove the barriers and enhance the benefits of performing the sustainable behavior.

Prompts are an effective behavior change tool because they remind individuals to perform the sustainable behavior (McKenzie-Mohr & Smith, 1999). Either a visual or auditory prompt serves as a reminder to engage in a sustainable behavior that individuals already perform. These prompts are not meant to increase and individual’s knowledge or change his or her behavior. Community-based social marketers encourage the use of prompts with commitment strategies and norms to encourage individuals to act on the prompt.

Communication is a tool of CBSM used to capture the attention of the target audience in order to initiate behavior change (McKenzie-Mohr & Smith, 1999). Communication efforts include the use of messages, credible sources, personal contact, modeling, and community block leaders. Persuasive messages influence individuals’ attitudes and behaviors (Kennedy, 2010; McKenzie-Mohr & Smith, 1999). However, community-based social marketers need to know the intended audience’s attitudes, beliefs, and behaviors before crafting persuasive messages. These messages need to be tailored to the different segments of the community be effective. Additionally, effective messages need information that is vivid, concrete, and personalized. Vivid information is more likely to be recalled at a later time because the information stands out

against other information. How the information is framed is also important. Most sustainable behaviors can be presented positively or negatively. Marketing research indicated that messages emphasizing negative motivations to engage in sustainable behaviors or losses as a result of inaction are more effective than positive messages (McKenzie-Mohr & Smith, 1999). Messages delivered through personal contact from credible sources are more influential on forming individuals’ attitudes and behaviors than mass media coverage. Persuasion communication research indicates that personal contact is more influential than mass media when influencing attitudes and behaviors (McKenzie-Mohr & Smith, 1999).

Consumers’ motivations for buying local food were used for developing a CBSM marketing campaign in England (Socio-Economic Research and Intelligence Observatory, 2008). Most grocery shoppers preferred the attributes of quality, freshness, and taste when purchasing local food, particularly fruit, vegetables, eggs, dairy products, and meat products. Consumers also expressed a desire to support local producers, reduce food miles, and know where their produce comes from. The research indicated that marketing communication messages should focus on the different motivations for buying local food. Promotional messages should educate consumers to overcome their barriers toward buying local food, focusing on what to buy, when and where from, value for money, ease of preparation, and versatility of use (Socio-Economic Research and Intelligence Observatory, 2008).

Purpose and Research Questions

As a means to meet consumers’ desire for local food in Texas, roughly 120 CSAs have sold shares of products harvested on the farms to consumers who joined (Local Harvest, 2010). However, CSAs face high annual attrition rates of their members (Kolodinsky & Pelch, 1997). This study fills a gap in the knowledge of the factors influencing Texas consumers to join a CSA, particularly their motivations, their attitudes and values toward the environment, and their level of community attachment. The following research questions were used to guide the quantitative data collection:

1. What motivates shareholders to join a CSA in Texas?2. What are the shareholders’ environmental attitudes?3. What is the level of community attachment CSA shareholders have with their

communities?

Methods and Procedures

The instrument used for this study was part of a larger descriptive, collective case study used to explore the marketing strategies of three CSA owners and the factors influencing their shareholders to join a CSA in Texas. This collective case study used a mixed methods design to collect and analyze quantitative and qualitative research data concurrently but separately as a way to better understand the research problem (Creswell & Plano Clark, 2007).

For the quantitative data collection, the researchers used the list of 126 CSAs in Texas available on Local Harvest’s website (Local Harvest, 2010). Local Harvest, an organic

and local food website, maintains the most comprehensive searchable database of CSAs in the United States (Local Harvest, 2011). By calling each CSA, the researchers discovered that 64 out of the 126 CSAs in Texas either discontinued their operation or did not have a current working telephone number. These CSAs were determined to be an inaccessible group. Of the 62 Texas CSAs from the database that the researchers talked to, three owners agreed to participate in the study. CSA 1, located near Dallas, has approximately 50 shareholders who all agreed to participate in the study. CSA 2, located in a rural community in Northeast Texas, has 83 shareholders. Eighty-two of the CSA 2 shareholders had e-mail addresses; however, only 46 shareholders gave the CSA owner permission to receive the e-mails asking for their participation in the study. Thirty-nine out of the 46 shareholders for CSA 2 had working e-mail addresses. CSA 3 is in a rural community near San Antonio and has approximately 120 shareholders that all had e-mail addresses and agreed to receive e-mails to respond to the survey.

The data collected from three constructs of the researcher-developed survey was used in preparing this manuscript. One construct of the survey used previous items from instruments (Brehm & Eisenhauer, 2008; Cone & Myhre, 2000; O’Hara & Stagl, 2001) to indicate how strongly shareholders agreed or disagreed (1 = strongly disagree to 5 = strongly agree) with 17 statements about their motivations for joining a CSA. The researchers used the revised New Ecological Paradigm Scale to measure shareholders’ level of agreement with seven statements measuring their attitudes and values toward the environment on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) (Dunlap, Van Liere, Mertig, & Jones, 2000). Items measuring community attachment in regards to community supported agriculture were adapted from Brehm and Eisenhauer (2008) and Brehm et al. (2004). Respondents indicated their level of agreement on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) to seven statements.

A panel of experts comprised of faculty and graduate students in agricultural education and communications established face and content validity of the survey. Prior to administering the survey to the study’s sample, the researcher conducted a pilot test with 33 shareholders from two CSAs in Texas to establish reliability of the researcher-developed survey. The Cronbach’s alpha value from the pilot test was 0.90 for the motivations to join construct. The construct measuring environmental attitudes had a Cronbach’s alpha of 0.81. The construct measuring community attachment had seven statements with a Cronbach’s alpha score of 0.78.

The researchers collected the quantitative data through administration of an online survey to members of the three CSAs in Texas using SurveyMonkey™, an online questionnaire builder and administrator service. The CSA owners did not permit the researchers to have access to the names, mailing addresses, phone numbers, and e-mail addresses of the CSA shareholders. The researchers created the e-mails and online survey that the CSA owners sent to their shareholders. As the first contact with participants, CSA owners sent a pre-notice e-mail to their shareholders requesting assistance with completing the survey. CSA owners sent an e-mail with the link to the online survey as the second contact. CSA owners sent a third e-mail to thank their shareholders for responding to the survey and remind others who have not.

A total of 85 online surveys were returned for a response rate of 41%. Of the 50 shareholders in CSA 1 who agreed to participate in the study, 26 shareholders (52.0%) responded to the survey. The sample for CSA 2 (n = 39) had a response rate of 49%. Of the 120 shareholders in CSA 3, 40 shareholders responded to the survey for a response rate of 33%.

The data for each CSA were exported into an Excel spreadsheet, and the researchers combined the data from all CSAs into one Excel spreadsheet. The researchers imported the data into SPSS® 18.0 for Windows™ PC. The researchers handled non-response rate to the online survey by using the Fisher’s exact test (Ary, Jacobs, Razavieh, & Sorensen, 2006). No significant differences were found between early and late respondents for ethnicity, employment situation, marital status, ownership arrangement, and the number of children under 18 living in their households. Frequencies and descriptive statistics described shareholders’ motivations for joining a CSA, environmental attitudes, and community attachment. The results presented in this study are cumulative of the three CSAs.

Findings

Question 1: What motivates shareholders to join a CSA in Texas?

As seen in Table 1, the highest rated motivation was the desire for fresh food products (M = 4.78, SD = 0.64). The second highest rated motivation was tied between the desire to support local community members who grow food (M = 4.73, SD = 0.92) and the desire for food free of pesticides (M = 4.73, SD = 0.72). The fourth highest rated motivation was tied between the desire to support sustainable agricultural practices (M = 4.69, SD = 0.76) and the desire for locally grown food products (M = 4.69, SD = 0.71).

Table 1

Motivations for Joining a CSA Construct (n = 51)

Statement M SDStrong desire for fresh food products. 4.78 0.64Support local community members who grow food. 4.73 0.92Strong desire for food that is free of pesticides. 4.73 0.72Strong desire to support sustainable agricultural practices. 4.69 0.76Strong desire for locally grown food products. 4.69 0.71Strong desire to support my community’s local economy. 4.61 0.72Strong desire to know where and how my food is grown. 4.58 0.79Strong desire for organic food products. 4.53 0.79Strong desire to eat food products that are in season. 4.47 0.81Strong desire for food that tastes better than what I can find in a local grocery store. 4.45 0.92Strong desire for food products that are not genetically engineered. 4.33 1.03Strong desire to reduce packaging on my food products. 4.33 0.89Strong desire for affordable food. 4.27 0.96

Strong desire to develop a stronger sense of community. 4.24 0.89Strong desire for food that is easily accessible. 4.16 0.90Strong desire to meet new people who care about where their food comes from. 3.49 0.93Specific health reasons/conditions that require this kind of food products. 2.41 1.28Note. The scale was 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree.

Question 2: What are the shareholders’ environmental attitudes?

Respondents had the highest level of agreement (M = 4.64, SD = 0.53) with the statement that “despite our special abilities humans are still subjects to the laws of nature” (Table 2). Respondents agreed “humans are severely abusing the environment” (M = 4.27, SD = 0.89). The third highest environmental attitude was that “the balance of nature is very delicate and easily upset” (M = 4.23, SD = 0.76).

Table 2

Environmental Attitudes Construct

Statement N M SDDespite our special abilities, humans are still subject to the laws of nature. 47 4.64 0.53Humans are severely abusing the environment. 48 4.27 0.89The balance of nature is very delicate and easily upset. 47 4.23 0.76Plants and animals have as much right as humans to exist. 49 4.20 0.94If things continue on their present course, we will soon experience a major ecological catastrophe. 50 3.98 1.02When humans interfere with nature it often produces disastrous consequences. 47 3.98 0.99We are approaching the limit of the number of people the Earth can support. 48 3.67 1.31Note. The scale was 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree.

Question 3: What is the level of community attachment CSA shareholders have with their communities?

As seen in Table 3, the highest ranked statement was “I feel ‘at home’ in my community” (M = 3.76, SD = 0.93). Respondents reported a mean of 3.68 (SD = 1.05) for their level of agreement with the statement “My neighbors would be very helpful if I had a personal emergency or crisis.” The third highest-ranking statement was that respondents trust other residents of their community (M = 3.39, SD = 0.83).

Table 3

Community Attachment Construct

Statement N M SDI feel “at home” in my community. 45 3.76 0.93My neighbors would be very helpful if I had a personal emergency or crisis. 47 3.68 1.05I trust other residents of my community. 46 3.39 0.83Residents of this community trust one another. 45 3.20 1.10I feel I am adequately involved in community decisions. 47 2.79 1.18If there was an important issue facing the community, the connections I made with other CSA members would be useful. 47 2.66 1.20I have a high level of trust in the local community government. 47 2.62 0.99Note. The scale was 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree.

Conclusions

A limitation of this study is that the results are limited to the shareholders and owners of the three CSAs in Texas. These owners and shareholders represent three different areas of Texas, which might differ from other CSAs in Texas. Another limitation concerns the quantitative data collection process. Dillman’s (2007) Tailored Design Method depends on multiple contacts with participants to accomplish a high response rate. The CSA owners insisted that they send the e-mail messages to their members on behalf of the researchers. They only permitted the sending of a prenotice email, e-mail with the survey link, and a thank you/reminder e-mail. More contacts with the CSA shareholders could have increased response rate to the survey.

Research question one sought to discover the motivations for joining a CSA in Texas. Respondents had a strong desire for fresh food products (M = 4.78, SD = 0.64), to support local community members growing food (M = 4.73, SD = 0.92), for food free of pesticides (M = 4.73, SD = 0.72), to support sustainable agricultural practices (M = 4.69, SD = 0.76), for locally grown food (M = 4.69, SD = 0.71), and to support their community’s local economy (M = 4.61, SD = 0.72). Many of the motivations related to environmental concerns, particularly the desire to reduce packaging on food products, to use sustainable agricultural practices, to consume non-genetically engineered food products, and to consume food products free of pesticides. Respondents might acknowledge that more sustainable practices are needed (Dunlap et al., 2000). Sustainable agriculture practices are frequently referred to as natural, organic, low-input, or alternative. Sustainable agriculture practices refer to the use of cropping rotations that mitigate disease or pests, reduce the need for pesticides, and protect against soil erosion and water contamination (Non-Governmental Organization, 1992).

CSA shareholders from three CSAs in Illinois and three CSAs in New Hampshire indicated their motivations for joining using the same 17-item construct from this study (Brehm & Eisenhauer, 2008). Respondents from those CSAs in Illinois and New Hampshire also had a strong desire for fresh food, for food free of pesticides, for locally

grown food products, to support local community members who grow food, and to support their community’s local economy (Brehm & Eisenhauer, 2008). Survey responses from shareholders of two CSAs in Ithaca, New York, ranked freshness as the most important motivation and locally grown as the second most important motivation for joining a CSA (Conner, 2003). The motivations concerning food quality traits—fresh, organically grown produce—were also important to CSA shareholders in the Northeast (Kelvin, 1994). Similarly, 99% of the respondents from the Roxbury Farm CSA indicated that receiving fresh vegetables was either very important or important (Polimeni et al., 2006). In contrast to the food quality traits, concerns related to building social networks via the CSA and motivations based on specific health conditions were less important motivations for joining the CSAs in Texas, Illinois, and New Hampshire (Brehm & Eisenhauer, 2008). Respondents from the three Texas CSAs expressed neutral agreement with the desire to meet new people who care about where their food comes from. Texas CSA shareholders disagreed that specific health reasons/conditions required this kind of food products. Similarly, CSA shareholders in Illinois and New Hampshire agreed they desired to meet new people who care about where their food comes from and held a neutral opinion that specific health reasons/conditions required this kind of food (Brehm & Eisenhauer, 2008).

Research question two determined the environmental attitudes of CSA shareholders. Respondents’ agreement with the seven statements from the New Ecological Paradigm Scale construct could explain that these individuals are gaining awareness of the material effects an industrialized country has on the environment (Catton & Dunlap, 1980). Individuals can change or perform behaviors that complement their environmental awareness (Catton & Dunlap, 1980). The respondents in this study share a pro-ecological worldview through their agreement that nature’s balance is fragile and an ecological crisis can occur if the balance of nature is not maintained. Researchers have studied local food systems through linkages with consumers’ environmental concerns and their food consumption practices (Weatherell, Tregear, & Allinson, 2003). Environmentally friendly practices were one of the important civic issues considered by consumers in rural and urban areas of England when making food choice decisions (Weatherell et al., 2003). Environmental concern might be related to consumers buying organic food and locally grown food (Pederson, 2000). Weatherell et al. (2003) explained that consumers with an increased awareness of and concern for conventional industrialized systems and their impact on the environment were interested in buying local foods or engaging in sustainable food systems. These consumers might have notions that local food systems are environmentally sustainable (Lea & Worsley, 2005; Storstad & Bjakhaug, 2003).

Research question three considered CSA shareholders’ level of community attachment. Respondents indicated their level of agreement on a 5-point Likert scale with seven statements measuring their sentimental feelings toward the place in which they live and their level of trust. Respondents did not express strong agreement or agreement with the statements measuring their attachment to their community, particularly their level of trust in residents of their community and in their local government. They disagreed (n = 47; M = 2.62, SD = 0.99) with the statement about having a high level of trust in their local government. Respondents disagreed that connections made with other CSA shareholders

would be useful if an important issue faces their communities. Shareholders who only pick up their share at a CSA farm or various drop-off locations may not have interaction with each other and less opportunity to build attachment for their community or CSA.

Implications

Findings from this study have potential implications for developing marketing practices for CSAs located in or near Texas communities. The information is organized by the components McKenzie-Mohr and Smith (1999) included in a community-based social marketing (CBSM) campaign, specifically identifying benefits, seeking commitment, and writing effective messages. In order to promote the desired behavior of joining a CSA, owners need knowledge of why individuals would perform the behavior. A benefit for CSA owners is the knowing shareholders’ motivations for joining a CSA. CSA owners can refer to this information to more effectively target the type of consumer who would be likely to join. Shareholders who responded to the survey desired locally grown, fresh food products that are free of pesticides. They also wanted to support local community members who grow food, sustainable agricultural practices, and their community’s local economy. These key messages should be incorporated on communication materials from CSAs.

Trust is an important characteristic that can impact commitment to adopting sustainable behavior, such as joining a CSA (McKenzie-Mohr & Smith, 1999). McKenzie-Mohr and Smith (1999) explained that shared environmental attitudes and concern promote commitment to a sustainable behavior because the shareholders have common values and norms. The third and fourth highest rated responses that shareholders provided about their community attachment related to trust. Shareholders had a neutral response to trusting other residents of their community and to agreeing residents of their community trust one another.

A way for potential shareholders to build trust and commitment to a CSA is by having the CSA owner help potential shareholders view themselves as environmentally concerned. Respondents had the highest level of agreement with the environmental statement that “despite our special abilities humans are still subjects to the laws of nature.” Respondents also agreed “humans are severely abusing the environment.” The third highest ranked environmental attitude statement indicated agreement that the balance of nature is very delicate and easily upset. Lastly, respondents agreed “plants and animals have as much right as humans to exist.” A CSA owner could use these environmental attitude statements to build trust. This trust can help potential shareholders trust each other and the CSA owner, allowing them to work together and support a local food movement in their community.

Persuasively written messages have the intent of influencing an individual’s attitudes and/or behaviors (McKenzie-Mohr & Smith, 1999). When recruiting potential shareholders, a community-based social marketing campaign might need to persuade potential shareholders to adopt a different lifestyle. A good way to capture potential shareholders’ attention is through recruitment messages that focus on shareholders’ motivations for joining, knowing that potential shareholders may share these same

motivations. CSA shareholders desire fresh food products, desire to support local community members who grow food, desire food free of pesticides, desire to support sustainable agricultural practices, and desire for locally grown food products. CSA owners should continue to emphasize the attributes of the farm, such as locally grown, organic, and fresh since those are motivations for joining a CSA. These motivations have already encouraged membership from their shareholders, so messages about the motivations for joining a CSA could entice similar individuals to join.

Recommendations for Future Research

In this study, 17 motivations for joining a CSA were reported. The motivations to join construct used in the survey was adopted from a previous survey of CSA members in Illinois and New Hampshire. The 17 items had a Cronbach’s alpha of .90 on the pilot test, which is considered an acceptable value. However, further research should explore if shareholders have additional motivations for joining. Furthermore, more research is needed to further test and refine the motivations to join construct. Factor analysis with a larger sample size would indicate if the motivations to join construct has subscales. An exploratory factor analysis would determine which of the 17 items form a construct for further use in surveys. Exploratory factor analysis would also reduce the number of items in the construct to a more manageable size. Additional data collection would permit the use structural equation modeling to explain the relationships between the different constructs.

Results from this study provide opportunities for future research. In regards to theory, additional research is needed to measure shareholders’ level of community attachment through their involvement in their CSA. Additional research is needed to develop a construct specific to measuring shareholders’ attachment to their community and their CSA. Much of the previous research has surveyed or interviewed current shareholders to discover their motivations, community attachment, and environmental attitudes. A useful next step would be to conduct a broader study that collects data from a general population in one community, multiple communities, or a region of the United States.

This study focused on three factors for joining a CSA: motivations, environmental attitudes, and level of community attachment. Researchers could identify other factors that influence an individual to join a CSA. A study could reveal the demographic and socio-economic characteristics that the shareholders in the three Texas CSAs share. Studies concerning CSA membership in the United States have shown that many shareholders have the common characteristics of gender, ethnicity, and level of education. Other characteristics revealed in these studies included age, household structure, number of children in household, household income, political affiliation, length or residency, and location of residency. When designing a CBSM campaign, CSA owners should know about the audience interested in joining. CSA owners could more efficiently invest their time and marketing efforts by knowing the demographic characteristics of individuals interested in joining a CSA. Additional research could discover what information channels shareholders use for learning about their CSA. This information would identify what information channels are most effective for recruiting potential shareholders. For a CBSM campaign, CSA owners need to disseminate their persuasive

messages through the information channels used by potential shareholders. Prompts are a visual or auditory reminder used in CBSM campaigns to remind individuals to perform a sustainable behavior (McKenzie-Mohr & Smith, 1999). CSA owners could use the preferred information channels to remind shareholders of their reasons for joining, such as their motivations, environmental attitudes, and attachment to their community and CSA.

References

Abdalla, C. W., & Kelsey, T. W. (1996). Breaking the impasse: Helping communities cope with Change at the rural-urban interface. Journal of Soil and Water Conservation, 51(6), 462-466.

Adam, K., Balasubrahmanyam, R., Born, H. (1999). Direct marketing. Retrieved from Appropriate Technology Transfer for Rural Areas website: https://attra.ncat.org/attra-pub/PDF/directmkt.pdf

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall Inc.

Ary, D., Jacobs, L., Razavieh, A., & Sorensen, C. (2006). Introduction to research in education. Belmont, CA: Wadsworth.

Brehm, J. M., & Eisenhauer, B. W. (2008). Motivations for participating in community-supported agriculture and their relationship with community attachment and social capital. Southern Rural Sociology, 23(1), 94-115.

Brehm, J. M., Eisenhauer, B. W., & Krannich, R. S. (2004). Dimensions of community attachment and their relationship to well-being in the amenity-rich rural west. Rural Sociology, 69(3), 405-429.

Brown, C., & Miller, S. (2008). The impacts of local markets: A review of research on farmers markets and community supported agriculture (CSA). American Journal of Agricultural Economics, 90(5), 1296-1302. doi: 10.1111/j.1467-8276.2008.01220.x

Buckley, J. K. (2009). Food, land, and community: A social movement in Humboldt County (Unpublished master’s thesis, The Humboldt State University). Retrieved from http://humboldt-dspace.calstate.edu

Catton, W. R. Jr., & Dunlap, R. E. (1980). A new ecological paradigm for post-exuberant sociology. American Behavioral Scientist, 24(1), 15-47.

Cone, C. A., & Myhre, A. (2000). Community-supported agriculture: A sustainable alternative to industrial agriculture?. Human Organization, 59(2), 187-197.

Conner, D. S. (2003). Community supported agriculture pricing and promotion strategies: Lessons from two Ithaca NY area farms (EB 2003-07). Retrieved from Cornell University, Department of Applied Economics and Management website: http://dyson.cornell.edu/outreach/extensionpdf/2003/Cornell_AEM_eb0307.pdf

Creswell, J. W., & Plano Clark, V. (2007). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Dunlap, R. E., Van Liere, K. D., Mertig, A. G., Jones, R. E. (2000). Measuring endorsement of the new ecological paradigm: A revised NEP scale. Journal of Social Issues, 56(3), 425-441.

Farnsworth, R. L., Thompson, S. R., Drury, K. A., Warner, R. E. (1996). Community supported agriculture: Filling a niche market. Journal of Food Distribution Research, 27(1), 90-98.

Fishbein, M. (1967). Readings in attitude theory and measurement. New York, NY: John Wiley.

Greenberg Quinlan Rosner Research. (2002). Report on building support for buying local. Retrieved from Food Routes Network website: http://www.foodroutes.org/BuildingSupport.pdf

Groh, T., & McFadden, S. (1997). Farms of tomorrow revisited: Community supported farms--farm supported communities. Kimberton, PA: The Biodynamic Farming and Gardening Association, Inc.

James, B. H. (1999). Rural neighbors: Living and working together. OSU Extension Fact Sheet. Columbus, Ohio: The Ohio State University Extension. Retrieved from http://ohioline.osu.edu/cd-fact/1280.html

Kelvin, R. (2004). Community supported agriculture on the urban fringe: Case study and survey. Kutztown, PA: Rodale Institute Research Center.

Kennedy, A. L. (2010). Using community-based social marketing techniques to enhance environmental regulation. Sustainability, 2(4), 1138-1160. doi: 10.3390/su2041138

Kolodinsky, J. M., & Pelch, L. L. (1997). Factors influencing the decision to join a community supported agriculture (CSA) farm. Journal of Sustainable Agriculture, 10(2/3), 129-141. doi: 10.1300/J064v10n02_11

Lang, K. B. (2010). The changing face of community-supported agriculture. Culture & Agriculture, 32(1), 17-26. doi: 10.1111/j.1556-486X.2010.01032.x

Lea, E., & Worsley, T. (2005). Australians’ organic food beliefs, demographics and values. British Food Journal, 107, 855-869.

Local Harvest. (2010). Retrieved from http://www.localharvest.org/csa/

Local Harvest. (2011). Retrieved from http://www.localharvest.org/

McKenzie-Mohr, D., & Smith, W. (1999). Fostering sustainable behavior: An introduction to community-based social marketing. Gabriola Island, BC, Canada: New Society Publishers.

O’Hara, S. U., & Stagl, S. (2001). Global food markets and their local alternatives: A socio-ecological economic perspective. Population and Environment: A Journal of Interdisciplinary Studies, 22(6), 533-553.

Pederson, L. H. (2000). The dynamics of green consumption: A matter of visibility.Journal of Environmental Policy and Planning, 2, 193-210.

Polimeni, J. M., Polimeni, R. I., Shirey, R. L., & Trees, W. S. (2006). The demand for community supported agriculture. Journal of Business & Economics Research, 4(2), 49-59.

Sharp, J., Imerman, E., & Peters, G. (2002). Community supported agriculture (CSA): Building community among farmers and non-farmers. Journal of Extension, 40(3). Retrieved from http://www.joe.org/joe/2002june/a3.php

Storstad, O., & Bjorkhaug, H. (2003). Foundations of production and consumption of organic food in Norway: Common attitudes among farmers and consumers? Agriculture and Human Values, 20, 151-163.

United States Department of Agriculture. (2010, March 22). Extension. Retrieved from http://www.csrees.usda.gov/qlinks/extension.html

Weatherell, C., Tregear, A., & Allinson, J. (2003). In search of the concerned consumer: UK public perceptions of food, farming and buying local. Journal of Rural Studies, 19, 233-244.

Woods, T., Ernst, M., Ernst, S., & Wright, N. (2009). 2009 survey of community supported agriculture produces. Retrieved from University of Kentucky Cooperative Extension Service website: http://www.ca.uky.edu/cmspubsclass/files/extensionpubs/departmentseries/2009-11.pdf

Zepeda, L., & Li, J. (2006). Who buys local food?. Journal of Food Distribution Research, 37(3), 1-11. Retrieved from http://purl.umn.edu/7064

A Mixed-Mode Survey of Media Channels and Public Opinion: Perceptions of Agriculture and “The Swine Flu”

Annie R. Specht, Texas A&M UniversityBilly R. McKim, Texas A&M University

Abstract

In 2009, the H1N1, or “swine flu,” pandemic made headlines across the globe. The Texas Department of State Health Services (DSHS) launched an informational campaign aimed at combating the spread of H1N1 through preventative measures. As part of the campaign’s evaluation, a sample of Texas residents were surveyed about their awareness of the campaign, their media use, and their beliefs regarding the pandemic. A portion of those surveyed indicated that the H1N1 virus was related to pork and other agriculture products. An investigation of those individuals found that those possessing misinformation related to H1N1 tended to be fairly well-educated, female Caucasians with an annual household income above the state median. Those individuals gained most of their information from television, with newspapers and websites also contributing. Social media outlets were not widely used for information gathering. Based on the results of this study, agricultural communicators should carefully monitor information presented on television and be prepared to counter those messages. Social media, although popular as a networking tool, is less effective at spreading awareness than more traditional forms of media.

Introduction

In April 2009, the World Health Organization (WHO) declared a Phase 5 alert—the second-highest level in the WHO’s warning system—in response to the growing threat of a worldwide influenza outbreak (Grady, 2009). The pandemic, initially dubbed “swine flu” by the popular press because of the recombinant swine, avian, and human DNA in the virus strain, was quickly renamed by WHO and U.S. government officials to the more scientifically accurate “H1N1 virus” (Levine, 2009).

According to Secretary of Agriculture Tom Vilsack, the name change was sparked by fears that the phrase “swine flu” would dissuade consumers from purchasing pork products despite reports from WHO and the Centers for Disease Control that properly cooked pork was safe for consumption (Etter, Carlson, & Thacker, 2009; Levine, 2009). Those fears were not unfounded as several countries, including China and Russia, banned the import of pork and other meat products from areas of the United States and Mexico hit by the influenza outbreak (Martin & Krauss, 2009).

The H1N1 pandemic spurred a number of information campaigns aimed at halting the spread of the virus through preventative measures, such as hand-washing. The Texas Department of State Health Services (DSHS) launched its public education campaign in October 2009. Aimed at raising awareness of all influenza strains, the Texas Flu campaign included a website, news media coverage, and social media to spread its

message to at-risk populations, including schoolchildren (DSHS, 2011). The campaign’s impact was evaluated through phone interviews, mailed survey questionnaires, and focus groups (DSHS, 2011). Among the evaluation outcomes were a number of comments from respondents who seemed to believe that H1N1 was directly related to the nation’s agriculture industry.

Theoretical Framework

The Communication Model and Receiver CharacteristicsCommunications scholars have long wrestled with the development of a general model of information flow. Early theorists Shannon and Weaver (1949) hypothesized a system of signal transmission, a linear pathway through which messages flow from a source at one end to a receiver at the other. Noise, the entropic disruptions that occur as a message moves from source to receiver, distorts the message before it can be decoded by the recipient (Ritchie, 1986; Shannon & Weaver, 1949).

Figure 1. Shannon & Weaver’s (1949) model of signal transmission

In Shannon and Weaver’s (1949) mathematical communication model, the receiver determines the meaning of the message, and this meaning is not always that intended by the information source (Bade, 2009). The receiver’s perception of the intended meaning of a message is based on a number of factors, chief among them the individual’s psychosocial characteristics (Freimuth, Cole, & Kirby, 2000). Receiver characteristics “are important because the success of a communication may depend on the sender’s ability to accurately identify the characteristics, cognitive abilities, and interests of the intended audience” (Nohre, MacKinnon, Stacy, & Pentz, 1999, p. 246). These attributes include sex, socioeconomic status, ethnicity, academic achievement, and religious affiliation (Creusen, 2010; Nohre et al., 1999).

Media Channels and Public OpinionSince Marshall McLuhan’s (1964) famed declaration that “the medium is the message,” researchers have attempted to determine which communications channels are most effective at conveying messages. Television has long been considered the most valuable medium for reaching audiences and producing attitude changes (Andreoli & Worchel, 1978), but the growing popularity of the Internet has led to increased emphasis on Web-based communication, despite evidence that traditional media are still more successful (Woodly, 2008). A majority of Internet users utilize social media, including social networking sites, weblogs, and microblogs, as part of their communications mix (Kaplan

& Haenlein, 2010). Newspapers and radio, on the other hand, have declined sharply over the past decades, a direct result of their competition with online technology (McCombs, Holbert, Kiousis, & Wanta, 2011).

Media Coverage of Pandemic DiseasesMedia coverage of infectious diseases has a profound impact on public perceptions of those illnesses. Threats of pandemics, which are dramatic and rare, tend to lead to alarmist, rather than neutral, media coverage that influences consumer perceptions of risk even among better educated audiences (Young, Norman, & Humphreys, 2008). Bovine spongiform encephalopathy (BSE), better known by the media sobriquet “mad cow disease,” decimated beef and dairy herds in the United Kingdom and led to embargoes on North American cattle exports when the disease was discovered in Canada in 2003 (Lewis & Tyshenko, 2009). Avian influenza, or “bird flu,” outbreaks in Asia reduced chicken sales dramatically and led to expenditures of billions of dollars on disease prevention in domestic poultry flocks across the globe (Ishida, Ishikawa, & Fukushige, 2010).

Purpose & Objectives

According to Doerfert (2003), the American populace has become increasingly removed from its agrarian roots, leading to the degradation of public knowledge, attitudes, and perceptions toward agriculture: “Limited knowledge…makes [the public’s] views uncertain and malleable” (p. 12). The growing “agrarian knowledge gap” (Specht, 2010) emphasizes the importance of agricultural literacy, defined by Frick, Kahler, and Miller (1991) as “possessing knowledge and understanding of our food and fiber system” (p. 52). With less than two percent of the U.S. population directly involved in agriculture, building understanding of the other ninety-eight percent who are impacted by the industry is of utmost importance.

The purpose of this study is to develop a profile of survey respondents who demonstrate a lack of agricultural awareness relating to the 2009 H1N1 pandemic to aid communicators in developing messages that target those uninformed sectors of the population. This study satisfies Priority 1 of American Association for Agricultural Education’s 2011-2015 National Research Agenda: public and policy-maker understanding of agriculture and natural resources.

To fulfill this stated purpose, the researchers defined the following objectives for the study:

1. To describe the sector of the public that associated H1N1 with agriculture; and2. To describe that sector’s media use related to the H1N1 pandemic.

MethodThe target population consisted of residents in the state of Texas. Data beyond the scope of this study were collected, so only the data directly related to this study will be presented. Many of the questions included in the data collection instrument used in this study were obtained with permission from Decima Research. Decima Research conducted a similar study of the H1N1 flu virus in Canada that was published in 2010.

Decima determined their instrument to be valid and reliable in their report; nonetheless, measures were taken to further validate (face and content validity) the modified instruments prior to distribution using a panel (n = 25) consisting of social science researchers, health care practitioners, public outreach specialists, and members of the public who were not selected to participate in the study.

Mailed SurveyData collected by mailed questionnaire were the most limited and brief of the three modes of data collection to ensure that 1,000 useable responses were returned, as required by the funding agency. A simple-random address-based sample (n = 10,000) of Texas residents was secured from the U.S. Postal Service’s Computerized Delivery Sequence File (CDS). The CDS database contains more than 135 million residential addresses and provides near 100% coverage of all households in the U.S.

The researcher-developed, three-page, scannable questionnaire was printed on 8 ½ in. by 14 in. paper and included nominal-, ordinal-, and scale-type questions; several brief open-ended questions were also included. Some nominal questions solicited follow-up responses, i.e., “if yes, then….” Additionally, several questions in the mail questionnaire presented respondents with a list of items and asked them to “select all that apply.” These data did not indicate rank order.The mail survey procedures followed the recommendations of Dillman, Smyth, and Christian (2009), including a pre-notice via mailed postcard, followed by two survey packets, consisting of a cover letter on state agency letterhead, a scannable self-administered questionnaire, and a preaddressed business-reply envelope. The mail survey packets were originally distributed to the sample of Texas residents (n = 10,000) on June 6, 2011, and responses were received through July 15, 2011. During this time, 1,988 questionnaires were completed and returned.

Phone SurveyLandline Random-Digit Dial (RDD) sampling techniques and a Computer Assisted Telephone Interviewing (CATI) interface were used to collect data through phone interviews. Landline RDD sample is a telephone sample that is randomly generated within residential area codes and exchanges. In this case, a sample was generated from a database of all residential area codes in Texas. The CATI interface is an interactive computer system that aids interviewers in asking questions over the phone and provides a mechanism for the interviewer to immediately key in an individual’s answers. The phone survey was conducted in English and Spanish between June 15, 2011 and July 1, 2011, during which 1,002 phone-based questionnaires were completed.Data collected by phone allowed for a greater number of questions and responses that are more specific. Additionally, respondents were provided the option of completing the questionnaire in English or Spanish. The phone questionnaire included nominal-, ordinal-, and scale-type questions.

Web SurveyCommercially available Web survey software was used to implement Web-based data collection and allowed respondents to complete the questionnaire in one of 48 languages.

A simple-random sample of Texas residents was obtained from a database of more than 12 million verified e-mail addresses. The Web-based survey was conducted between June 13, 2011 and June 15, 2011. During this time, 1,433 surveys were completed.

Data collected by Web-based software allowed for the most extensive list of questions and specific responses. Additionally, respondents were provided the option of completing the questionnaire in one of 48 languages. The Web-based questionnaire included open-ended and nominal-, ordinal-, and scale-type questions. The Web-based software used an advanced skip logic and if-then logic to obtain the most specific data. Where possible, respondents were asked to rank questions and, in some cases, answer multiple levels of if-then logic questions.

Data AnalysisData were analyzed using SPSS® version 20.0 for Windows™ platform computers. A total of 4,423 useable responses were obtained through the multi-modal data collection process and presented as a basis of comparison to the population in Texas. Respondents who associated pork or other agriculture products with the H1N1 flu virus were identified by a “yes” response to the question “To the best of your knowledge, is it possible to contract the H1N1 flu or the ‘swine flu’ by eating pork products?” (n = 441). Additionally, a content analysis of two open-ended questions was conducted and yielded 53 additional individuals who attributed the H1N1 flu to agricultural products. These individuals were identified by responses such as “wearing a mask, stay away from chicken or pork;” “not visit any farms & eat pork;” and “I avoid eating meats or other animal products.” The 494 respondents who associated pork or other agriculture products with the H1N1 flu virus represented 11.2% of the population and were the basis for this study.

PopulationGender and ethnicity were collected in both the Web and phone modes of data collection. A majority of the respondents (59%) were female with the remaining 41% male. Of the respondents, 71% identified themselves as White or Caucasian, 11% as African American, 14% as Hispanic, 2% as Asian, and the remaining 3% as Native American, Pacific Islander, or other. Education level and household income were collected in each mode of data collection. Of the respondents, 18% had a high school degree, while 24% had a bachelor’s degree. An additional 14% had a post-graduate degree; 8% had less than a high school education.

Household income data indicated that 19% had a household income of less than $20,000 per year and 19% had an annual household income of more than $100,000 per year. Twenty-three percent had an income level of $20,000 to $39,999 per year. Finally, 32% of the respondents reported having children in the household. A more expansive description of the respondents was included in Table 1.

Table 1Statewide Respondents’ Gender, Ethnicity, and Age

Mode of Data Collection

Total Mail Phone Webf % f % f % f %

GenderMale 964 41.1 -- -- 370 36.9 594 44.3Female 1380 58.9 -- -- 632 63.1 748 55.7

EducationElementary 86 2.2 51 2.8 33 3.3 2 0.2Some high school 213 5.4 105 5.7 73 7.3 35 3.3Completed high school 688 17.6 257 13.9 195 19.5 236 22.0Some community college/technical college 626 16.0 288 15.5 103 10.3 235 21.9Completed community/technical college 352 9.0 176 9.5 55 5.5 121 11.3Some university 465 11.9 203 10.9 119 11.9 143 13.3Completed university (bachelor’s degree) 932 23.8 468 25.2 256 25.5 208 19.3Post-graduate 549 14.0 306 16.5 148 14.8 95 8.8

(Continues)Total Mail Phone Web

f % f % f % f %Household Income

Less than $20,000 656 18.7 305 18.4 153 15.3 198 18.6$20,000 to $29,000 423 12.1 147 8.8 109 10.9 167 15.7$30,000 to $39,000 379 10.8 153 9.2 82 8.2 144 13.5$40,000 to $49,000 331 9.4 132 7.9 68 6.8 131 12.3$50,000 to $59,000 304 8.7 135 8.1 60 6.0 109 10.3$60,000 to $69,000 259 7.4 133 8.0 49 4.9 77 7.2$70,000 to $79,000 224 6.4 106 6.4 42 4.2 76 7.1$80,000 to $99,000 279 8.0 166 10.0 46 4.6 67 6.3$100,000 or more 651 18.6 385 23.2 172 17.2 94 8.8

Race/EthnicityWhite/Caucasian 1624 70.5 -- -- 664 69.0 960 71.5African American 249 10.8 -- -- 89 9.3 160 11.9Hispanic 176 7.6 -- -- 13 1.4 163 12.1Asian 191 8.3 -- -- 168 17.5 23 1.7Native American 27 1.2 -- -- 16 1.7 11 0.8Pacific Islander 3 0.1 -- -- 1 0.1 2 0.1Other 34 1.5 -- -- 11 1.1 23 1.7

Comparison to Texas PopulationOverall, the demographic data from the sample and the Texas population were similar (less than 10 percentage point difference) for most variables. However, for ethnicity, the responding sample had a higher percentage of Whites/Caucasians (70.5% for the sample to 46.2% for the state) and a lower representation of Hispanics (7.6% for the sample to 38% for the state).

Table 2Comparison of Respondents’ Gender, Education Level, Income, and Ethnicity to Texas Population

% TX Residentsa % Total RespondentsGender

Male 49.8 41.1Female 50.2 58.9

EducationElementary 15.5 7.6Completed high school 57.5 54.5Completed university (bachelor’s degree) 18.1 23.8Post-graduate 8.9 14.0

Household IncomeLess than $20,000 20.0 18.7$20,000 to $59,999 40.1 41.0$60,000 to $100,000 21.1 21.8$100,000 or more 18.7 18.6

(Continues)% TX Residents % Total Respondents

Race/EthnicityWhite/Caucasian 46.2 70.5African American 10.9 10.8Hispanic 37.9 7.6Asian 3.3 8.3Native American 0.3 1.2Pacific Islander 0.1 0.1Other 1.3 1.5

Note: aU.S. Census Bureau data

To provide a basis of comparison, Texas residents who responded to the survey were compared based on whether they associated pork or other agriculture products with the H1N1 flu virus or not (see Table 3).

Table 3A Comparison of Statewide Respondents’ Gender, Education Level, Income, and Ethnicity to Respondents Who Associated Pork or Other Agriculture Products With The H1N1 Flu Virus

Associated H1N1 Flu Virus with Pork ProductsYes No

f % f %Gender

Male 141 43.3 741 43.8Female 185 56.7 950 56.2

EducationElementary 23 5.3 15 .9Some high school 49 11.2 70 4.2Completed high school 104 23.7 331 19.8Some community college/technical college 78 17.8 278 16.7Completed community/technical college 34 7.8 143 8.6

Some university 46 10.5 218 13.1Completed university (bachelor’s degree) 65 14.8 399 23.9Post-graduate 39 8.9 214 12.8

Household IncomeLess than $20,000 98 25.1 269 17.8$20,000 to $29,000 62 15.9 215 14.2$30,000 to $39,000 50 12.8 182 12.1$40,000 to $49,000 32 8.2 165 10.9$50,000 to $59,000 34 8.7 140 9.3$60,000 to $69,000 28 7.2 106 7.0$70,000 to $79,000 24 6.1 99 6.6$80,000 to $99,000 24 6.1 97 6.4$100,000 or more 39 10.0 236 15.6

(Continues)

Associated H1N1 Flu Virus with Pork ProductsYes No

f % f %Race/Ethnicity

White/Caucasian 165 51.2 1245 75.0African American 47 14.6 162 9.8Hispanic 83 25.8 200 12.0Asian 14 4.3 16 1.0Native American 4 1.2 19 1.1Pacific Islander 0 0.0 3 .2Other 9 2.8 16 1.0

To describe media use of respondents who associated pork or other agriculture products with the H1N1 flu virus, Table 4 provides a description of the medium by which respondents received their information about the H1N1 pandemic, Table 5 provides a description of the medium respondents believed to be the easiest to understand, and Table 6 provides a description of the medium by which respondents preferred to receive information.

Table 4Medium by Which Respondents Received Their Information About The H1N1 Pandemic

Television Radio Newspaper Web SMa

f % f % f % f % f %

GenderMale 116 89.9 31 34.1 44 46.3 46 48.9 6 7.3Female 145 92.4 38 40.0 53 47.3 56 56.6 8 9.8

EducationElementary 18 100.0 5 100.0 4 100.0 1 100.0 0 0.0Some high school 43 97.7 14 66.7 17 68.0 11 61.1 5 35.7Completed high school 88 94.6 30 56.6 38 64.4 19 44.2 5 12.8Some CC/TC 71 97.3 21 45.7 29 52.7 31 55.4 6 14.3

Completed CC/TC 25 80.6 11 45.8 11 47.8 18 69.2 2 10.0Some university 36 90.0 6 23.1 14 45.2 21 70.0 2 8.3Completed universitya 54 93.1 12 35.3 25 56.8 22 64.7 3 11.5Post-graduate 27 90.0 14 70.0 15 71.4 16 72.7 4 36.4

Household IncomeLess than $20,000 84 97.7 26 57.8 37 71.2 23 56.1 8 25.8$20,000 to $29,000 58 95.1 17 38.6 21 46.7 22 51.2 1 2.6$30,000 to $39,000 40 90.9 14 43.8 21 60.0 23 69.7 3 11.1$40,000 to $49,000 26 92.9 5 35.7 11 55.0 12 60.0 3 20.0$50,000 to $59,000 28 90.3 9 45.0 13 52.0 13 68.4 1 5.9$60,000 to $69,000 24 88.9 8 50.0 9 47.4 10 62.5 4 30.8$70,000 to $79,000 19 95.0 8 50.0 9 52.9 8 61.5 3 25.0$80,000 to $99,000 18 85.7 6 54.5 13 72.2 7 58.3 1 14.3$100,000 or more 30 90.9 8 44.4 7 43.8 16 64.0 1 9.1

(Continues)Television Radio Newspaper Web SMa

f % f % f % f % f %Race/Ethnicity

White/Caucasian 125 88.0 31 33.0 52 46.0 52 52.5 5 5.7African American 41 95.3 12 38.7 12 38.7 18 54.5 2 7.4Hispanic 71 95.9 16 37.2 25 54.3 18 43.9 3 8.8Asian 11 84.6 5 50.0 3 33.3 9 75.0 3 33.3Native American 4 100.0 0 0.0 1 50.0 2 66.7 0 0.0Pacific Islander 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0Other 6 85.7 5 83.3 3 60.0 3 60.0 1 20.0

Note: aSocial Media, i.e., Twitter, Facebook, blogs, YouTube; bCompleted bachelor’s degree; CC/TC = community college/technical college.

Table 5Medium Respondents Believed to be the Easiest To Understand

Television Radio Newspaper Web SMa

f % f % f % f % f %Gender

Male 99 82.5 19 22.4 32 34.4 33 37.1 5 6.1Female 116 85.3 26 29.5 41 39.4 35 36.8 5 6.1

EducationElementary 17 100.0 4 100.0 1 100.0 0 0.0 0 0.0Some high school 34 91.9 12 60.0 9 50.0 11 61.1 4 28.6Completed high school 74 92.5 18 40.9 32 56.1 9 23.1 5 12.5Some CC/TC 57 85.1 16 34.0 24 44.4 21 40.4 3 7.3Completed CC/TC 26 86.7 9 39.1 11 44.0 13 54.2 1 5.3Some university 34 87.2 4 16.7 11 37.9 19 59.4 1 4.2Completed universitya 35 77.8 7 23.3 20 50.0 14 45.2 2 8.0Post-graduate 24 92.3 12 66.7 14 66.7 11 64.7 4 40.0

Household Income

Less than $20,000 72 92.3 19 47.5 29 58.0 15 39.5 5 15.6$20,000 to $29,000 46 82.1 12 28.6 15 33.3 20 47.6 2 5.1$30,000 to $39,000 28 70.0 9 30.0 17 51.5 19 57.6 2 7.4$40,000 to $49,000 21 95.5 7 43.8 8 50.0 9 45.0 3 21.4$50,000 to $59,000 25 92.6 4 21.1 9 39.1 8 42.1 3 16.7$60,000 to $69,000 22 91.7 5 35.7 5 31.3 8 50.0 1 9.1$70,000 to $79,000 16 100.0 5 38.5 7 46.7 5 41.7 1 10.0$80,000 to $99,000 14 77.8 5 50.0 11 68.8 6 50.0 1 14.3$100,000 or more 27 90.0 5 33.3 10 55.6 5 33.3 1 9.1

(Continues)

Television Radio Newspaper Web SMa

f % f % f % f % f %

Race/EthnicityWhite/Caucasian 106 79.1 23 25.0 42 39.3 36 36.7 3 3.4African American 32 91.4 9 31.0 7 23.3 11 34.4 2 7.4Hispanic 57 91.9 10 27.0 16 37.2 12 31.6 1 2.9Asian 8 72.7 1 12.5 3 37.5 6 66.7 3 33.3Native American 4 100.0 0 0.0 0 0.0 0 0.0 0 0.0Pacific Islander 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0Other 5 71.4 2 40.0 3 60.0 3 60.0 1 20.0

Note: aSocial Media, i.e., Twitter, Facebook, blogs, YouTube; bCompleted bachelor’s degree; CC/TC = community college/technical college.

Table 6Preferred Medium to Receive Information

Television Radio Newspaper Web SMa

f % f % f % f % f %

GenderMale 18 21.7 6 7.4 8 9.9 11 13.4 2 2.4Female 29 31.9 9 11.0 13 15.1 15 17.2 2 2.4

EducationElementary 3 100.0 1 100.0 1 100.0 0 0.0 1 100.0Some high school 11 55.0 2 16.7 1 9.1 6 40.0 2 16.7Completed high school 22 43.1 9 21.4 11 26.2 7 17.9 1 2.7Some CC/TC 18 36.0 11 24.4 10 21.7 12 26.1 4 9.1Completed CC/TC 9 40.9 5 22.7 4 19.0 2 10.5 1 5.3Some university 6 23.1 2 8.3 4 16.0 2 8.3 0 0.0Completed universitya 13 41.9 4 14.8 7 24.1 7 25.0 3 11.5Post-graduate 8 57.1 3 27.3 5 38.5 3 30.0 0 0.0

Household IncomeLess than $20,000 22 52.4 7 21.2 9 28.1 4 13.3 3 9.7$20,000 to $29,000 15 34.1 6 15.8 7 17.5 8 20.5 1 2.7

$30,000 to $39,000 13 38.2 7 22.6 7 22.6 6 20.0 2 7.1$40,000 to $49,000 4 26.7 4 26.7 2 14.3 4 26.7 1 7.1$50,000 to $59,000 4 22.2 0 0.0 1 5.9 5 27.8 2 11.1$60,000 to $69,000 7 46.7 3 23.1 4 28.6 2 16.7 0 0.0$70,000 to $79,000 5 38.5 4 30.8 2 18.2 3 25.0 0 0.0$80,000 to $99,000 6 54.5 2 25.0 6 54.5 3 37.5 1 14.3$100,000 or more 7 43.8 3 23.1 2 16.7 3 23.1 1 8.3

(Continues)

Television Radio Newspaper Web SMa

f % f % f % f % f %

Race/EthnicityWhite/Caucasian 22 24.2 7 8.0 8 9.0 11 12.5 1 1.1African American 5 18.5 3 11.1 1 3.7 5 17.9 1 3.7Hispanic 13 32.5 2 5.9 6 17.1 5 13.9 1 2.9Asian 4 50.0 0 0.0 3 37.5 3 33.3 0 0.0Native American 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0Pacific Islander 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0Other 2 40.0 3 60.0 2 40.0 1 20.0 1 20.0

Note: aSocial Media, i.e., Twitter, Facebook, blogs, YouTube; bCompleted bachelor’s degree; CC/TC = community college/technical college.

A summary of the overall media use and preferred sources for information is presented in Figure 2. Television was perceived as the most relied on, easiest to understand, and most preferred source of information, followed by Newspaper, then Web.

Figure 2. Overall media use and preferred sources

Conclusions

Relied On Easiest Preferred0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

TelevisionRadioNewspaperWebSocial Media

DemographicsMisinformation related to the H1N1 outbreak—namely, that the virus was transmissible through pork or other agricultural products—was relatively evenly distributed among demographics. The respondents were predominately female (56.7%) and Caucasian (51.2%). Educational achievement did little to reduce misconceptions, with 34.2% of misinformed respondents reporting at least some university-level education. Just over 38% of respondents who associated the H1N1 virus with agriculture products reported a household income above the Texas median of $48,286.

Media Use and PreferencesAcross demographic variables, the majority of respondents indicated that they received information related to the H1N1 pandemic via television, followed by newspapers, Web sources, and radio. Social media, considered a primary medium for the DSHS campaign, received little use among respondents. When respondents indicated which media were easiest to understand, the same pattern was discovered: Televised messages were considered the most comprehensible, followed by newspapers, Web sources, radio broadcasts, and social media messages. Media preferences were much more varied. Though television still ranked as the most preferred information channel, newspapers, the Internet, and radio were statistically deadlocked across all demographics. Social media were ranked last by respondents in terms of preference but gained some ground compared to actual use and understanding.

The “Average” Misinformed ConsumerBased on the results of this study, the consumer who associates agriculture products with the H1N1 virus is likely to be female, Caucasian, and relatively well-educated, having completed at least an associate’s or technical degree program. The consumer is a lazy knowledge-seeker, relying on television and newspapers for information and less likely to seek out information on the Web or via social media outlets.

Implications & Recommendations

Rogers (2003) defined a communication network as “interconnected individuals who are linked by patterned flows of information” (p. 27). Within these networks, individuals who frequently influence the attitudes of others are known as “opinion leaders” (Rogers, 2003, p. 27). Opinion leaders are often characterized by more exposure to forms of external communication and higher socioeconomic status than their peers (Sun, Youn, Wu, & Kuntaraporn, 2006; Rogers, 2003). The results of this study—that those respondents who reported misinformation related to H1N1 and its connection to agriculture tended to be of higher SES than their peers—indicate that these individuals are potential opinion leaders who may be capable of propagating misinformation among their communication networks.

Study respondents’ media use and preferences provide insight into future campaign planning. The overwhelming use of television as an information source—and the reported comprehension advantage is provides—indicates that future health-related campaigns should target this medium. Traditional media like television and newspapers continue to

hold an important position in the communication mix, indicating that audiences still rely on tried-and-true information sources. However, based on the respondents’ clear preference for televised messages, television content may also be the source of the misinformation reported by study participants. Agricultural communicators and educators, therefore, must monitor television messages and prepare to counter those messages in the same format.

Despite widespread popularity, social media like Twitter and Facebook continue to lag behind traditional media in terms of usefulness as an information source. Those who use social media may focus on the entertainment or socialization aspects of the technology rather than its information-gathering abilities. Because social media are generally low-cost, low-input communication tools, their continued use in information campaigns is encouraged; however, communicators must be aware of the technology’s limitations.

ReferencesAmerican Psychological Association Task Force on Socioeconomic Status. (2007).

Report of the APA Task Force on Socioeconomic Status. Washington, DC: American Psychological Association.

Andreoli, V., & Worchel, S. (1978). Effects of media, communicator, and message position on attitude change. Public Opinion Quarterly, 42(1), 59-70.

Bade, D. (2009). Ethos, logos, pathos or sender, message receiver?: A problematic rhetoric for information technologies. Cataloguing & Classification Quarterly, 47, 612-630.

Creusen, M. E. H. (2010). The importance of product aspects in choice: The influence of demographic characteristics. Journal of Consumer Research, 27(1), 26-34.

Decima. (2010). Rollin survey of Canadians' knowledge, awareness and level of concern of H1N1 flu virus in Canada. Toronto, Canada.

Department of State Health Services [DSHS]. (2011). Stopping the flu is up to you. Retrieved from http://www.texasflu.org/

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method. Hoboken, NJ: Wiley & Sons.

Doerfert, D.L. (2003). Agricultural literacy: An assessment of research studies published within the agricultural education profession. Proceedings of the 22nd Annual Western Region Agriculture Education Research Conference, Portland, OR.

Etter, L., Carlson, D., & Thacker, C. (2009, April 27). Pork industry moves to quell flu fears. The Wall Street Journal. Retrieved from http://online.wsj.com/

Freimuth, V., Cole, G., and Kirby, S. (2000). Issues in Evaluating Mass Mediated Health Communication Campaigns. Copenhagen: WHO Regional Office for Europe.

Frick, M.J., Kahler, A.A., & Miller, W.W. (1991). A definition and the concepts of agricultural literacy: A national study. Journal of Agricultural Education, 32(2), 49-57. doi: 10.5032/jae.1991.02049

Grady, D. (2009, May 1). W.H.O. gives virus a name that’s more scientific and less loaded. New York Times. Retrieved from http://www.nytimes.com/

Ishida, T., Ishikawa, N., & Fukushige, M. (2010). Impact of BSE and bird flu on consumers’ meat demand in Japan. Applied Economics, 42(1), 49-56.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53, 59-68.

Laughery, K. R., & Brelsford, J. W. (1991). Receiver characteristics in safety communications. In Proceedings of the Human Factors Society 35th Annual Meeting: Vol. 35 (pp. 1068-1072).

Levine, M. (2009, April 28). U.S. officials offer new name for swine flu. FOX News. Retrieved from http://www.foxnews.com/

Lewis, R. E., & Tyshenko, M. G. (2009). The impact of social amplification and attenuation of risk and the public reaction to mad cow disease in Canada. Risk Analysis, 29(5), 714-728.

Martin, A., & Krauss, C. (2009, April 29). Pork industry fights concerns over swine flu. New York Times. Retrieved from http://www.nytimes.com/

McCombs, M., Holbert, L., Kiousis, S., & Wanta, W. (2011). The news and public opinion: Media effects on civic life. Malden, MA: Polity Press.

McEwen, W. J. (1978). Bridging the information gap. The Journal of Consumer Research, 4(4), 247-251.

McLuhan, M. (1964). Understanding media: The extensions of man. New York, NY: Signet.

Nohre, L., MacKinnon, D. P., Stacy, A. W., & Pentz, M. A. (1999). The association between adolescents’ receiver characteristics and exposure to the alcohol warning label. Psychology & Marketing, 16(3), 245-259.

Ritchie, D. (1986). Shannon and Weaver: Unravelling the paradox of information. Communication Research, 13(2), 278-298.

Shannon, C., & Weaver, W. (1949). The mathematical theory of communication. Urbana, Illinois: Univ. of Illinois Press.

Specht, A. R. (2010). Investigating the cultivation effects of television advertisements and agricultural knowledge gaps on college students’ perceptions of modern dairy husbandry practices (Master’s thesis). Retrieved from http://etd.ohiolink.edu/

Sun, T., Youn, S., Wu, G., and Kuntaraporn, M. (2006). Online word-of-mouth (or mouse): An exploration of its antecedents and consequences. Journal of Computer-Mediated Communication, 11(4), Article 11. Retrieved from http://jcmc.indiana.edu/vol11/issue4/sun.html

U.S. Census Bureau, (2011). State & County QuickFacts: Texas. Retrieved from http://quickfacts.census.gov/qfd/states/48000.html

Williams, T. G. (2002). Social class influence on purchase evaluation criteria. The Journal of Consumer Research, 19(2-3), 249-267.

Woodly, D. (2008). New competencies in democratic communication? Blogs, agenda setting and political participation. Public Choice, 134, 109-123.

Young, M. E., Norman, G. R., & Humphreys, K. R. (2008). Medicine in the popular press: The influence of the media on perceptions of disease. PLoS ONE 3(10): e3552.

Going Global: Study Abroad Intentions of Agriculture and Natural Resource Students

Rachel Bobbitt, Texas Tech UniversityDr. Cindy Akers, Texas Tech University

AbstractStudy abroad programs affiliated with colleges and universities all over the world

work at developing cultural awareness in students, as well as preparing them to grow academically and personally. While these experiences are encouraged, not all students choose to participate, especially, it appears, students in agriculture and natural resources. This study was designed to search for a better understanding as to why some students choose to participate while others do not, using the theory of planned behavior (TPB). The TPB states a person’s behavioral intention is determined by three factors: attitude towards the behavior; degree of social pressure felt to perform or not perform the behavior; and the degree of control over performing the behavior (Ajzen, 1991). A tailored-design, web-based questionnaire was distributed to 1,537 agriculture and natural resources undergraduates, with a 33% response rate. As expected, the TPB predictors were highly correlated with intentions. Multiple regression results support the TPB and direct measures of the theory variables predicted 54% of intent to study abroad. The results show the TPB can improve understanding of a student’s intention to study abroad, and the findings can be used to develop and market study abroad programs in which students are likely to participate.

IntroductionAgriculture is a global enterprise. Consequently, developing leaders who can cope

with the mounting complexities of operating in such an expanding world market is becoming increasingly important. “Arguably, an understanding of agriculture’s history and current economic, social, and environmental significance, both domestically and internationally, is important for all Americans” (Doerfert, 2011, p. 11). As students graduate and move into the workforce, international experience will be necessary if they are to help the United States remain competitive in a global market (Moore, Boyd, Rosser, & Elbert, 2009). One possible way to achieve this global understanding is through study abroad experiences. Study abroad programs have become the most visible and popular international activity to enrich and broaden students’ global competency (Zhai & Scheer, 2002).

For university students, academic study abroad programs may take a variety of forms. Students may participate in fully integrated programs and spend a semester abroad enrolled in an institution in a host county (Dwyer, 2004), while some universities offer semester-long hybrid programs or short-term faculty-led programs (Anderson, Lawton, Rexeisen, & Hubbard, 2005; Engle & Engle, 2003). Regardless of program length, study abroad programs can have a significant positive impact on the lives of participants (Dwyer, 2004).

Not only are study abroad experiences highly valued by employers (Fischer, 2010), but researchers have demonstrated positive effects that such experiences have on students. The impacts of an international experience stretch beyond the areas of academic success and career development and are seen as being profoundly influential on personal

development and intercultural awareness (Dwyer, 2004). Studies have shown that students who study abroad develop a deeper understanding and respect for global issues (Carlson, Burn, Useem, & Yachimowicz, 1990; Kitsantas, 2004), more favorable attitudes toward other cultures (Anderson et al., 2006; Kitsantas, 2004), stronger intercultural communication skills (Sutton & Rubin, 2004), improved personal and professional self-image (Cushner & Mahon, 2002), and better foreign language skills (DuFon & Churchill, 2006; Sutton & Rubin, 2004). In addition, surveys of former study abroad participants consistently indicate that they believe the experience improved their self-confidence, ability to handle ambiguity, insight into their own value systems, and overall maturity (Carlson et al., 1990). In light of the known benefits and the increasing importance of an international study experience, by encouraging participation in study abroad opportunities, agricultural educators can promote “actively and emotionally engaged in learning” (Doerfert, 2011, p. 21) by their students.

Most universities offer a range of study abroad programs to a multitude of countries that provide students with the opportunity to travel and explore the world, gain experience and earn credits toward completing their degrees (Dwyer, 2004). Much research has been done on the benefits that study abroad participation has on college students (Acker & Scanes, 2000; Bruening & Frick, 2004; Dwyer, 2004; Moore et al., 2002), and on perceptions of study abroad programs (Moore et al., 2009; Zhai & Scheer, 2002). However, little is known about what influences students to choose to study abroad. This is certainly the case within agriculture and natural resources. And although interest in study abroad programs has never been higher among American college students, as few as 1.5 percent of college students travel overseas to study every year (Williamson, 2010).

In addition to cost and timing considerations, various other factors may influence students’ decisions regarding program choice, including their motivations and their attitude toward the program. However, there is a shortage of research on how attitudes are formed and what factors play important roles in forming attitude toward the participation in study abroad programs (Nyaupane, Teye, & Paris, 2008).

By determining why agriculture and natural resource students choose a study abroad experience and by identifying what factors deter participation in these endeavors, colleges can effectively develop and market study abroad programs in which students will likely participate. This research presents the results of a study performed to analyze the factors which impact the attitude toward and likelihood of agriculture and natural students at Texas Tech University to participate in a study abroad program. The Theory of Planned Behavior (Ajzen, 1991; Ajzen & Fishbein, 1980) acted as the theoretical foundation to provide an increase in the understanding of the factors influencing students’ attitudes toward their choice to study abroad and the subsequent behaviors. This study aligns with research priority 4 of the American Association for Agricultural Education to develop “meaningful, engaged learning in all environments” (Doerfert, 2011, p. 21).

Theoretical FrameworkAccording to the Theory of Planned Behavior (see Figure 1), a person’s

performance of a specified behavior is determined by that person’s intention to perform

the behavior. Behavioral intention is depicted as a function of three basic determinants: attitude toward performing the behavior, subjective norms, and perceived control (Ajzen, 1991; Ajzen & Fishbein, 1980).

Figure 1. Theory of Planned Behavior (Ajzen, 1991; Ajzen & Fishbein, 1980).

Attitude is the overall belief about something—the evaluative opinion the individual holds in regard to the behavior in question(Ajzen, 1991). The attitude is the degree to which performance of the behavior is positively or negatively valued. Attitudes toward a particular behavior are influenced by a combination of two related factors: affective beliefs and instrumental beliefs. Affective beliefs refer to emotions and drives felt by the prospect of performing a behavior. This is in contrast to instrumental beliefs, which refers to a more cognitive consideration of the extent to which performing a behavior would be advantageous (Breckler & Wiggins, 1989).

Subjective norms are the beliefs that one holds about the normative expectations of others, such as parents, friends, classmates, teachers, etc. According to the TPB, individuals have a sense or belief about whether or not these individuals and groups would approve or disapprove of the behavior. Measures of subjective norms consists of perceived pressure, approval or disapproval from the influential people in one’s life, the pressure to engage in the given behavior, and the individual’s motivation to comply (Ajzen, 1991).

Perceived behavioral control is the belief about the perceived ease or difficulty in engaging in the behavior (Ajzen, 1991), as well as any previous experiences with carrying out the specific behavior and any anticipated hindrances. The influence of perceived behavioral control depends on self-efficacy (confidence in one’s own ability to perform the behavior) and perceived controllability if the behavior (Armitage & Conner, 2001). Perceived behavioral control plays an important part in the theory of planned behavior (Ajzen, 1991). In fact, the theory of planned behavior differs from the theory of reasoned action in its addition of perceived behavioral control. Although, Ajzen (1991) has suggested that the link between behavior and behavioral control outlined in the model should be between behavior and actual behavioral control rather than perceived

BehaviorIntention

Attitude

Subjective Norms

Perceived Behavioral Control Actual Behavioral

Control

behavioral control, the difficulty of assessing actual control has led to the use of perceived control as a proxy.

Behavioral intention is considered as a mediating factor in the association between attitude, subjective norm, and perceived behavioral control on the one hand and behavior on the other hand. The stronger the intent to perform a behavior, the greater the likelihood that the individual will engage in the behavior. The theory of planned behavior can be useful in designing strategies to help people to adopt behaviors, such as studying abroad.

Purpose and ObjectivesThis research examined the power of the Theory of Planned Behavior (TPB) to

predict study abroad intentions of agriculture and natural resource students. The specific objectives are:

1. To identify participants’ attitudes toward studying abroad.

2. To identify participants’ subjective norms about studying abroad.

3. To identify participants’ perceived behavioral control of studying abroad.

4. To identify participants’ intention to study abroad.

5. To predict participants’ intention to study abroad using attitudes, subjective norms and perceived behavioral control.

Methods and ProceduresParticipants

The target population of this study was undergraduates in College of Agricultural Sciences and Natural Resources at Texas Tech University during the fall of 2011. Of the 1,537 questionnaires distributed, 465 were returned, yielding a response rate of 33.05%. Sheehan (2001) found that the mean response rate for internet surveys was 35% in 1998, 27% in 1999, and 24% in 2000. Sheehan also found that between 1986 and 2000, internet survey response rates continually declined, and concluded that response rates for internet surveys were likely to decline further still in the future.

After the exclusion of those who opted out and submitted incomplete questionnaires, the number of valid responses was 402. The majority of participants (n = 349, 87.0%) were “white,” followed by Hispanics (n = 40, 10%). Four participants (1.0%) indicated they were Black, not of Hispanic origin. Of the other ethnic groups in this study, 3 participants (.7%) each were American Native/Alaskan Native and Asian, and 1 (.2%) each was Native Hawaiian/Pacific Islander and Non-Resident Alien. No single grade level dominated the participants’ classification. Almost one-third were seniors (n = 123, 30.7%), closely followed by juniors (n = 112, 27.9%), sophomores (n = 87, 21.7%), and freshman (n = 77, 19.2%). Two participants (.5%) reported they had received a bachelor’s degree. Age of participants ranged from 17 years to 54 years, with a mean age of 21.07 (SD = 4.67). Collectively, respondents to this survey had an average estimated overall GPA of 3.30 (SD = .48).

InstrumentA researcher developed, web-based instrument was used to collect data about the

participants’ intention to study abroad. This study followed the methodological procedures proposed by Francis et al. (2004) for constructing and analyzing a TPB questionnaire. To predict whether the participants intended to study abroad, the questionnaire explored whether the participants are in favor of studying abroad (attitude), how much the participants feel social pressure to do it (subjective norm) and whether the participants feel in control of their participation (perceived behavioral control).

This study employed direct measures of the TPB constructs. Direct questions about engaging/not engaging in a behavior may be methodologically superior to the use of scenarios (Randall, 1989), as they tap what the respondent will do in reality over what the respondent would do in a hypothetical situation.

Attitude. To directly assess attitude toward studying abroad, a six items were used. Using 5-point, semantic differential scales, respondents were asked whether they felt participating in a study abroad program was easy/difficult, good/bad, valuable/worthless, pleasant/unpleasant, possible/impossible, and interesting/boring. To compute construct of attitude toward studying abroad, the six measures were averaged to create a single scale.

Subjective norms. To directly assess the respondent's subjective norm toward studying abroad, respondents were asked three questions, using a 5-point Likert scale: first, whether they agreed that, “Most of the students in CASNR with whom I am acquainted have or plan to study abroad”; second, “When it comes to study abroad, how much do you want to be like your friends?”; and third, how true is it that, “It is expected that I participate in a study abroad program.” Mean responses to the three questions were calculated to give an overall subjective norm score.

Perceived behavioral control. Perceived behavioral control was directly measured by assessing the participants’ self-efficacy and their beliefs about the controllability of the study abroad behavior, as suggested by Ajzen (1991). Seven items, using a 5-point Likert scale, measured self-efficacy and controllability. Self-efficacy was assessed by asking people to report how confident they are that they could study abroad if they wanted. Participants also indicated how strongly they agreed that they would have difficulty due to language barriers, costs, financial assistance, inflexible curricular requirements, and personal safety. Controllability was assessed by asking participants to report how much they agreed that “Whether I participate in a study abroad program is completely up to me.” These seven items were averaged to arrive at an overall measure of perceived behavioral control.

Intention. Behavioral intentions were measured by three items. Each item was measured on a 5-point Likert scale. Participants indicated how much “I have previously considered participating in a study abroad program.” They also indicated whether they agreed that “Participating in a study abroad program is something that interests me,” and whether “I intend to participate in a study abroad program.”

Validity and Reliability. An expert panel reviewed the questionnaire to establish content and face validity. Using pilot data obtained from surveys completed by 23 undergraduates majoring in agriculture and natural resources at five peer institutions, Cronbach alpha values were computed for each construct to assess the reliability of survey items. Cronbach alpha scores for attitude, subjective norms, perceived behavioral control and intent were .87, .67, .55, and .86 respectively. Nunnally (1962) suggested that reliability estimates of .50 to .60 might be high enough in the early stages of research.

Data Collection and AnalysisParticipants were surveyed during the fall of 2011 using a modified version of

Dillman, Smyth, & Christian’s (2009) tailored design method for internet, mail, and mixed-mode surveys. The design and methods of this study were deemed exempt by the Texas Tech University Institutional Review Board.

Statistical analysis was carried out using SPSS for Windows, version 18.0. Standard descriptive statistics were used to analyze the demographic characteristics of participants, as well as measures of attitude, subjective norms, perceived behavioral control, and intention. For objective five, regression analyses were performed to test the relationships between constructs in the theory of planned behavior. Intention was regressed on attitude toward performing the behavior, subjective norms, and perceived behavioral control. Davis’ (1971) conventions were used to label correlation relationships between variables.

FindingsObjective 1: Attitudes

Seven direct measures of attitude toward studying abroad were gauged, and then used to calculate an overall attitude construct score. The overall attitude construct had a mean score of 3.89 (SD = .74), on a scale from 1 to 5 with higher scores indicating a more positive attitude toward studying abroad (see Table 1). While the students’ attitude was favorable overall, individual instrumental items (whether the behavior achieves something: interesting/boring, valuable /worthless, good/bad) were more positive than the attitudes about affective items (how it feels to perform the behavior: pleasant/unpleasant, possible/impossible, easy/difficult).

Table 1

Attitudes toward Participating in a Study Abroad Program

Item M SD

Interesting/Boring 4.54 .80

Valuable /Worthless 4.32 .87

Good/Bad 4.27 .82

Pleasant/Unpleasant 4.09 .90

Possible/Impossible 3.34 1.22

Easy/Difficult 2.86 1.13

Overall Attitude 3.89 .74Note. Scale of items ranges from 1 = negative attitude to 5 = positive attitude.

Objective 2: Subjective Norms

Subjective norm is the influence that peer pressure has on shaping intent studying abroad. To measure subjective norms, a composite variable was created by averaging the scores of three sources of pressure: perceived intention of peers, desire to be like peers, and perceived expectations by others (see Table 2). Each item was rated on a scale of 1 to 5, where high scores reflect greater social pressure to study abroad. The mean of the overall subjective norm (M = 2.66, SD = .77) indicates that on average, the students are only somewhat influenced by other people in their decision to study abroad.

Table 2

Subjective Norms about Participating in a Study Abroad Program

Item M SD

Perceived Intention of Peers 3.05 1.04

Perceived Expectations by Others 2.69 1.19

Desire to be Like Peers 2.26 1.02

Overall Subjective Norms 2.66 .77Note. Scale of items ranged from 1 to 5, where high scores reflect greater social pressure to study abroad.

Objective 3: Perceived Behavioral Control

Behavioral controls are the real or perceived logistical hindrances that shape students’ intent to study abroad. The perceived behavioral control construct combined seven items related to potential difficulties, the student’s self-efficacy, and their beliefs about the controllability of the behavior. High scores reflect a greater level of control over participating in a study abroad program or, looked at in another way, lower scored indicated more perceived difficulty in controlling those factors.

On average, students felt the least control over potential difficulty due to costs (M = 2.30, SD = 1.14). Additionally, students agreed that inflexible degree requirements were difficult to control and could prevent their participation in a study abroad program. Worry over their personal safety (M = 3.55, SD, 1.08) was not seen as a difficulty compared to the other barriers. Results showed the students did not feel strongly one way or another about their control over each of the other potential difficulties in this study (see Table 3), since “3” indicated neutrality on the scale of 1 to 5. Students agreed that they felt slightly confidant that they could study abroad if they wanted (M = 3.79, SD = 1.13) and that the decision to study abroad was up to them (M = 3.25, SD = 1.09).

Table 3

Perceived Behavioral Control of Participating in a Study Abroad Program

Item M SD

Control of difficulty due to:

Personal Safety Risks 3.55 1.08

Financial Assistance Availability 3.45 .94

Language Barriers 3.14 1.08

Inflexible Curricular Requirements 2.68 1.12

Costs 2.30 1.14

Confidence of Ability 3.79 1.13

Perceived Controllability 3.82 1.09

Overall Perceived Behavioral Control 3.25 .60Note. Scale of items ranges from 1 to 5, where high scores reflect greater level of control over participating in a study abroad program.

Objective 4: Intentions

Table 4 shows the mean values for the items in the intention construct. Analysis revealed that, on average, students’ overall intent was slightly positive (M = 3.68, SD = 1.04). In general, students agreed that they did have interest in studying abroad (M = 4.15, SD = 1.02). At the same time, they were fairly neutral about their previous consideration (M = 3.65, SD = 1.31) and their explicit intention to study abroad (M = 3.24, SD = 1.25).

Table 4

Intent to Study Abroad

Item M SD

Interest 4.15 1.02

Previous Consideration 3.65 1.31

Intention 3.24 1.25

Overall Intent 3.68 1.04Note. Scale of items ranges from 1 to 5, where high scores reflect stronger intention to study abroad.

Objective 5: Prediction

In order to determine if attitude, subjective norms and perceived behavioral control could explain intent to study abroad, a multiple linear regression was conducted. Table 5 presents the regression model results for student intentions to study abroad.

Table 5

Regression Analysis to Explain Intent to Study Abroad

Item

r

β t p R2Adjusted

R2Intent AttitudeSubjective

Norms

Attitude .69* .53 12.28 .001*

Subjective Norms .52* .46* .25 6.41 .001*Perceived Behavioral Control .46* .54* .33* .09 2.25 .025*

Model .54 .54Note. *p < .05

Each of the predictor variables had a significant (p < .05), substantial, positive correlation with intent to study abroad. Analysis also showed that a combination of

attitude toward the behavior, subjective norms and perceived behavioral control significantly explained student’s intent to study abroad. Attitude (β = .53, p < 0.01) had the strongest influence on behavior intention, followed by subjective norms (β = .25 p < 0.01). The overall F(3, 395) value for the model was 154.72 with a p value smaller than .001. Both R2 and adjusted R2 for this model were .54.

The three-predictor model explained 54% of the variance in intent to study abroad. The results are consistent with the theory predictions that students’ personal attitude, subjective norm, and perceived behavioral control influence their intentions to studying abroad.

Conclusions/RecommendationsThis study contributes to the academic literature on the theory of planned

behavior, as well as study abroad as an academic program. As expected, attitudes, subjective norms, and perceived behavioral control all significantly predicted intentions to study abroad, providing support for the original TPB model. That is, students with positive attitudes toward studying abroad, who believed that others would approve of the behavior, and who believed they had control over carrying out the behavior were more likely to intend to participate in a study abroad program.

The findings showed that the students’ attitude toward studying abroad was the most important linear predictor of their intention to study abroad. Students who place value on the benefits and see worth in the experience are more likely to participate. Furthermore, the study found that perceived behavioral control, including self-efficacy and controllability, was a factor influencing interests and subsequent choice goals related to study abroad. Thus, students who have the self-assurance than they can effectively participate in a study abroad program are more likely than are their less confident peers to make such academic decisions. The subjective norms were least important in shaping the decision. This is consistent with Armitage & Conner’s (2001) findings that several authors argued that it is the weakest component of the TPB. Nevertheless, the positive role played by peers in encouraging study abroad behavior in general is significant in this study. Therefore, the intention to study abroad appears to be a personal choice and, to a lesser degree, influenced by others.

The findings of the current study concur with the theoretical underpinnings of the theory of planned behavior (Ajzen, 1991). This support is largely consistent with previous research examining the application of the theory to general behaviors (Armitage & Conner, 2001). Given that all the direct measures of the theory of planned behavior were highly predictive of study abroad intentions, these findings provide useful information for interventions designed increase study abroad participation in this population. The results of the present study indicate that targeting students’ personal attitudes, elements of internal motivation and control, and their perceptions of pressure from others may be useful strategies to increase study abroad participation in agriculture and natural resource students.

This research supports many practical implications for marketing within higher education by professors and student services professionals involved in the development,

promotion, and outcomes of study abroad programs. Interventions designed to change behavior can be directed at one or more of its determinants: attitudes, subjective norms, or perceptions of behavioral control (Ajzen, n.d.). First, when promoting study abroad programs directly to students themselves, practitioners should emphasize the benefits of opening new career opportunities, gaining an opportunity to grow and develop as a person, and exposure to an interesting and/or fun experience (benefits found by Acker & Scanes, 2000; Bruening & Frick, 2004; Moore et al., 2009; Zhai & Scheer, 2002), since attitude had the strongest relationship to and was the best predictor of intention. Additionally, as the cost of study abroad programs was seen as a potential barrier to participation in available programs, practitioners should ensure that costs are kept low, and that grants, scholarships, or other forms of financial aids are readily available and easy to obtain. Furthermore, a series of persuasive communications, as recommended by Ajzen (n.d.), could be developed to show how a study abroad program could fit into existing curriculum/degree plans. It is important to remember that changing one or two beliefs many not produce a change in intent, and a multi-pronged intervention, grounded in the TPB, is likely to produce the desired study abroad behavior in this population.

Although TPB constructs predict behavioral intention, intention may not necessarily lead to actual study abroad behavior. Given the possibility of inconsistency between intention and behavior, it is important to examine both intention and behavior in a single study to fully understand the relationship among the three TPB components, intention, and behavior. Thus, future research should be directed toward a longitudinal study involving the actual study abroad behaviors as a major dependent variable.

While the research has potential limitations, it is the first to address intent to study abroad in the agriculture and natural resources context. Given the importance of agriculture worldwide, this research just represents an important part of the dialogue related to study abroad in agriculture and natural resources.

ReferencesAcker, D., & Scanes, C. G. (2000). A case for globalizing undergraduate education and

student learning at colleges of agriculture. Journal of International Agricultural and Extension Education, 7(1), 47-51.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Ajzen, I. (n.d.). Behavioral interventions based on the theory of planned behavior. Retrieved from http://people.umass.edu/aizen/pdf/tpb.intervention.pdf.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting behavior. Englewood Cliffs, NJ: Prentice Hall.

Anderson, P. H., Lawton, L., Rexeisen, R. J., & Hubbard, A. C. (2005). Short-term study abroad and intercultural sensitivity: A pilot study. International Journal of Intercultural Relations, 30(4), 457-469.

Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40, 471–499

Breckler, S. J., & Wiggins, E. C. (1989). Affect versus evaluation in the structure of attitudes. Journal of Experimental Social Psychology, 25, 253-271.

Bruening, T. H., & Frick, M. (2004). Globalizing the U.S. undergraduate experience: A case study of the benefits of an international agriculture field-based course. Journal of International Agricultural and Extension Education, 11(1), 89-96.

Carlson, J. S., Burn, B. B., Useem, J., & Yachimowicz, D. (1990). Study abroad: the experience of American undergraduates. Westport, Connecticut: Greenwook Press, Inc.

Cushner, K., & Mahon, J. (2002). Overseas Student Teaching: Affecting Personal, Professional, and Global Competencies in an Age of Globalization. Journal of Studies in International Education, 6(1), 44-58.

Davis, J. A. (1971). Elementary survey analysis. Englewood, NJ: Prentice-Hall.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail and mixed-mode surveys: The tailored design (3rd ed.). Hoboken, New Jersey: John Wiley & Sons, Inc.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

DuFon, M. A., & Churchill, E. (Eds.). (2006). Language learners in study abroad contexts. Great Britain: MPG Books Ltd.

Dwyer, M. M. (2004). More is better: The impact of study abroad program duration. Frontiers: The Interdisciplinary Journal of Study Abroad, 10, 151-163.

Engle, L., & Engle, J. (2003). Study abroad levels: Toward a classification of program types. Frontiers: The Interdisciplinary Journal of Study Abroad, 9, 1-20.

Fischer, K. (2010, October 17). Study abroad’s new focus is job skills. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/Study-Abroads-New-Focus-Is/124979/.

Francis, J., Eccles, M., Johnston, M., Walker, A., Grimshaw, J., Foy, R., Kaner, E., Smith, L., & Bonetti, D. (2004) Constructing questionnaires based on the theory of planned behaviour: A manual for health services researchers. Newcastle upon Tyne, UK: Centre for Health Services Research, University of Newcastle.

Kitsantas, A. (2004). Studying abroad: The role of college students' goals on the development of cross-cultural skills and global understanding. College Student Journal, 38(3), 441-452.

Moore, L. L., Boyd, B. L., Rosser, M. H., & Elbert, C. (2009). Developing an international agricultural leadership program to meet the needs of a global community. Journal of Leadership Education, 8(1), 118-129.

Nunnally, J. (1962). The analysis of profile data. Psychological Bulletin, 59(4), 311-319. doi: 10.1037/h0041246

Nyaupane, G. P., Paris, C. M., and Teye, V. (2011). Study abroad motivations, destination selection and pre-trip attitude formation. International Journal of Tourism Research, 13, 205-217. Doi: 10.1001/jtr.811

Randall, D. M., (1989). Taking stock: Can the theory of reasoned action explain unethical conduct? Journal of Business Ethics, 8, 873-82.

Sheehan, K. (2001). Email survey response rates: A review. Journal of Computer Mediated Communication, 6(2). doi: 10.1111/j.1083-6101.2001.tb00117.x

Sutton, R. C., & Rubin, D. L. (2004). The GLOSSARI project: Initial findings from a system-wide research initiative on study abroad learning outcomes. The Interdisciplinary Journal of Study Abroad, 10, 65-82.

Williamson, W. (2010, July 25). 7 signs of successful study-abroad programs. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/7-Signs-of-Successful/123657/

Zhai, L., & Scheer, S. D. (2002). Influence of international study abroad programs on agricultural college students. Journal of International Agricultural and Extension Education, 9(3), 23-29.

The Effects of Teacher Learning Style on Student Knowledge Gain in a Leadership Camp Setting: A Repeated-Measures Experiment

Nicholas R. Brown, Oklahoma State UniversityRobert Terry, Jr., Oklahoma State University

Abstract

While the National FFA Organization provides leadership education content through its annual national convention and conferences, such as the Washington Leadership Conference, many state associations host camps. The purpose of this split-plot factorial repeated-measures experiment was to assess the level of campers’ learning of the curriculum taught during small group breakout sessions and to study the effects of the learning style of camp Small Group Leaders on student knowledge gain of camp curriculum measured by pre-test and post-test scores. Analysis of variance was utilized to test null hypotheses using an F-ratio to determine the significance (α = .05) Although there was a significant difference between pre-test and post-test scores, the interaction of test scores and SGL learning style failed to produce a statistically significant interaction; therefore, there was no significant treatment effect by SGL learning style. Recommendations for camp leaders in response to study results include regular summative assessments of the camp experience, annual evaluations of Small Group Leaders and campers, and the formation of instructional standards and learning goals. Further research was recommended in the impact of teacher learning styles on student academic performance in informal educational settings.

Introduction and Background

DeBello (1990) defined learning style as “the way people absorb, process, and retain information” (p. 203). Much agricultural education research has been conducted pertaining to learning style and the effects of student learning style on academic achievement (Cano & Garton, 1994; Cano, Garton & Raven, 1992; Dyer & Osborne, 1996; Friedel & Rudd, 2006; Garton, Spain, Lamberson, & Spiers, 1999; Lambert, Smith, & Ulmer, 2010; Marrison & Frick, 1994; Whittington & Raven, 1995).

Whittington and Raven (1995) utilized the Group Embedded Figures Test (Oltman, Raskin, & Witkin, 1971) to study the preferred learning style of student teachers in agricultural education. The two researchers discovered that most study participants were field independent learners, meaning they value their authority and feel responsible for guiding student learning (Whittington & Raven, 1995). Cano et al. (1992) found that field independent learners achieved higher scores in a college teaching methods course. Garton et al. (1999) confirmed this finding when their research indicated that “as students moved toward a field independent learning style their achievement in the course increased” (p. 18). Lambert et al. (2010) utilized the Gregorc Style Delineator™ (Gregorc, 1982) to determine if mind styles affected the overall relational satisfaction between mentors and protégés who were participating in a new teacher-mentoring program. They determined that Mind Style™ did not impact relational satisfaction among teachers and their protégés (Lambert et al., 2010).

Numerous studies within and beyond the discipline of agricultural education have explored the relationship between teacher/trainer and student/trainee; more specifically these studies examined the effects of teacher learning style on student learning outcomes (Hansen and Stansfield, 1982; McDonald, 1984; Mehdikhani, 1983; Paradise & Block, 1984). McDonald (1984) found that matching student learning style with teacher learning style could be beneficial. Similarly,

Paradise and Block (1984) concluded that teacher learning style impacted fourth grade students’ reading achievement. While literature exists that ties teacher learning style to student learning outcomes, other studies have concluded the opposite (Hansen and Stansfield, 1982; Mehdikhani, 1983). Mehdikhani (1983) concluded that teacher learning style did not impact academic success of students in mathematics and English classes. Hansen and Stansfield (1982) examined the effects of matching student and teacher learning style. It was determined that students whose learning style matched their teachers learning style did not score significantly higher than those who were mismatched (Hansen & Stansfield, 1982).

The literature is saturated with learning style research conducted in formal education environments. Therefore, the researchers determined that further inquiry was warranted to examine the effects of learning styles in informal educational settings such as an FFA leadership camp. The researchers specifically focused on the effects of teacher learning style on student learning.

Need for the Study

The primary and historical educational purpose of the National FFA Organization is to provide informal leadership and personal growth opportunities to student members (Hoover, Scholl, Dunigan, & Mamontova, 2007). Hoover et al. (2007) concluded that the National FFA Organization, like many other youth organizations formed during the early years of the twentieth century, was originally organized to teach leadership development and reward students for their accomplishments. While the National FFA Organization provides this content through its annual national convention and conferences, such as the Washington Leadership Conference, many state associations host camps (Connors, Falk, & Epps, 2010). FFA members from 24 states benefit from unique summer camp experiences, which focus on leadership and recreation (Connors, Falk, & Epps, 2010). Connors et al. (2010) posited, “FFA camps have provided members with recreational, social, and leadership development for decades” (p. 32). Although much literature exists documenting and explaining the purposes and activities taking place during FFA camps, little research has been conducted exploring teaching and learning in the FFA camp setting (Comings, 1977; Connors, Falk, & Epps, 2010; Javornik, 1962; Keels, 2002; McCrea, 2011).

For more than 30 years, the Oklahoma FFA Association has annually hosted a summer leadership development camp (McCrea, 2011). The FFA leadership camp, heretofore to be referred to as camp, requires extensive planning, human capital, and substantial financial support for its four 4-day sessions (Kent Boggs, personal communication, May 16, 2011). Over the course of the four sessions, approximately 1400 FFA members participate in the camp. These participants, heretofore referred to as campers, earn the opportunity to attend camp through their involvement in local chapter activities (Kent Boggs, personal communication, May 16, 2011).

Since 2005, the program of the camp has included a curriculum based upon measureable learning objectives. The focus on the curriculum is typically one or more topics related to leadership and personal development. Instruction has been delivered as a part of the camp program through small group, breakout sessions taught by instructors selected by camp directors. (Kurt Murray, personal communication, June 10, 2011)

In recent years, the annual camp planning process has included recruiting 33 college-age youth to lead the small groups and teach the camp curriculum. These individuals, known as Small Group Leaders (SGLs), are each assigned to a group of approximately 12 campers. Camp planners indicated that they believe the SGLs were homogenous in their personality type and personal qualities (Kurt Murray, personal communication, June 10, 2011). Despite this perception, camp

planners and state FFA staff members were interested in learning if factors exist, such as the learning style of SGLs, that may influence any variation in the amount of information learned by campers during the breakout sessions (Kurt Murray, personal communication, June 10, 2011).

Theoretical Foundation

Kolb (1984) posited that students become more successful in academics when their learning environment matches their personal learning style. Although SGL learning style and camper learning were the primary interest of the researchers, the unique camp environment was considered when choosing the learning theory to frame this study. Three learning style instruments, all grounded in differing theories, were considered: (a) the Gregorc Style Delineator™ (Gregorc, 1982), (b) the Group Embedded Figures Test (Oltman, Raskin, & Witkin, 1971), and (c) the Paragon Learning Style Inventory (Shindler & Yang, 2003).

Ultimately, the Paragon Learning Style Inventory (PLSI) was chosen as the most appropriate instrument for this research. The PLSI is theoretically grounded in Jung’s (1971) trait theory. Jung (1971) outlined factors that affect the way people think, learn, act, and see things. First, Jung (1971) posited that there are two types of people, introverts and extraverts. The two traits are described best in Jung’s own words:

The introvert’s attitude is an abstracting one; at bottom, he is always intent on withdrawing libido from the object, as though he had to prevent the object from gaining power over him. The extravert, on the contrary, has a positive relation to the object. He affirms its importance to such an extent that his subjective is constantly related to and oriented by the object. (Jung, 1971, p. 330)

The object in question can be a person or material item. Stated differently, extraverts are often very oriented to the people around them and introverts tend to focus on their inner self (Jung, 1971). Second, Jung (1971) identified that people are either Sensates or Intuitives. Sensates are usually more patient, realistic and practical. Sensates rely heavily on their previous experience and common sense (Shindler & Yang, 2003). Intuitives tent to be more abstract and creative. Furthermore, Intuitives dislike routine and primarily focus on his or her vision of the future (Shindler & Yang, 2003). The third factor was Feeler versus Thinker (Jung, 1971). Shindler and Yang (2007) explained that Feelers focus on personal relationships and have a greater interest in people than ideas. Thinkers are fascinated by ideas and make rational decisions (Shindler & Yang, 2003). Jung’s (1971) final factor was Judger versus Perceiver. Judgers are very opinionated and are decisive. Perceivers are unplanned, curious people who thrive on spontaneity. According to Shindler and Yang (2007), the two factors that most affect how a person acts and learns are introversion/extraversion and sensation/intuition; therefore, the researchers chose to only focus on these two dimensions.

Shindler and Yang (2003) named and described the four types of learners associated with these two Jungian dimensions:

1. People who are both extraverts and sensates are classified as Action Oriented Realists. Action Oriented Realists love action packed group work. They enjoy sharing their thoughts and become impatient when tasks become too complicated or abstract.

2. Action Oriented Innovators are people who are extraverts and intuitives. They are motivated and love to work in groups on interesting projects. Action Oriented Innovators are open and enjoy sharing their thoughts, but are leery of detailed routines.

3. Introverted sensates are classified as Thoughtful Realists because of their tendency to carefully work alone or with one other. Thoughtful Realists are often unexpressive but are good with detailed work and technical concepts.

4. Finally, introverted intuitives are called Thoughtful Innovators. These learners are best at problem solving and prefer to work on their own ideas. They value expressing themselves through their thoughts and ideas and shy away from busy work or tasks they don’t value. (Shindler & Yang, 2003)

Shindler and Yang (2003), developers of the PLSI, postulated that instructors who are aware of their own personal learning style will be more successful teachers. This assertion is grounded in Jung’s trait theory (1971) and assumes students learn better when the teaching style of the instructor is tailored to match their learning style. Shindler and Yang’s (2003) assertion suggests that natural barriers exist when introverts and extraverts teach and learn from each other and when sensates and intuitives interact in a teaching and learning environment. According to Shindler and Yang (2003), “teachers who are aware of their own style and those of their students will be more successful with more types of students” (p. 6). This assertion closely aligned with the question about the influence of SGLs on learning outcomes of campers, thus establishing the need for this research.

Purpose, Objectives, and Hypotheses

The purpose of this study was to assess the impact of the learning styles of SGLs upon the campers in their groups. Specifically, this study focused upon the learning style of SGLs and campers’ increase in knowledge of the camp curriculum. This study was influenced by the recommendation of Whittington and Raven (1995) who stated, “research efforts regarding learning styles and teaching styles” are needed on both the regional and national level (p. 15). Furthermore, this study is in line with the American Association for Agricultural Education’s research priority 4: Meaningful, engaged learning in all environments (Doerfert, 2011). Three research objectives guided the study:

1. Identify the learning style of SGLs. 2. Assess the level of campers’ learning of the curriculum taught during small group

sessions.3. Determine if SGL learning style affects campers’ learning of the curriculum taught

during small group sessions.

The following hypotheses were formulated for research objectives two and three:

Objective 2H0: There is no difference between campers’ pre-test and post-test scores on a test of facts and concepts associated with the curriculum taught during small group sessions.

Objective 3H0: There is no association between test scores of campers and the learning style of their SGL.

Methodology

The design of this study is best described as split-plot factorial repeated-measures approach. Quantitative educational research is defined as “educational research in which the researcher decides who to study; asks specific, narrow questions; collects quantifiable data from

participants; analyzes these numbers using statistics; and conducts the inquiry in an unbiased, objective manner” (Creswell, 2008, p. 46). The split-plot factorial repeated-measures experimental design (Kirk, 1995) was used to measure camper learning and determine how the learning style of SGLs affected camper learning outcomes. The researcher used quantifiable data and inferential statistics to meet the three research objectives.

Population and Sampling

All campers were Oklahoma FFA members and agricultural education students who had completed the eighth grade but had not graduated from high school. Although each camper’s level of FFA involvement varied, all campers had completed at least one year of agriculture coursework and one year of FFA membership. It was determined that a census study was not feasible because of the time limitations during the data collection periods during camp sessions. As a result, the researchers randomly sampled from the population (N = 752).

Probabilistic simple random sampling procedures were employed. Creswell (2008) explained that the simple random sampling technique is the most rigorous sampling procedure and allows the researcher to generalize the findings of the experiment to the total population. In this case, results of this study can only be generalized to the campers who attended Session 3 and Session 4 of camp during the summer of 2011. Before campers arrived, each pre-registered camper was assigned a number. A simple random sample of the population was then generated using a web-based randomizer tool (random.org). The researchers utilized G*Power version 3.1, a computer software, to determine that a sample of 118 was needed to reach maximum statistical power during data analysis (Faul, Erdfelder, Lang, & Buchner, 2007). In an effort to remain as unobtrusive as possible while still achieving generalizability, the sample size was increased to (n = 218). The sample was reduced to (n = 203) due to an absence of parental consent from 15 campers who were consequently removed from the sample. Ultimately, 181 campers completed all elements of the experiment resulting in an 89% response rate for the study. Lindner, Murphy, and Briers (2001) concluded that when a response rate of 85% or greater is achieved no further procedures are necessary to control for non-response error.

Research Design

The repeated-measures for this experimental design study were a pre-test and post-test. Repeated-measures designs require study participants to participate in all levels of the experiment (Field, 2009). The study was designed to meet the three research objectives by identifying the learning styles of SGLs, determining the level of camper learning using pre-test and post-test scores, and splitting the campers into four groups based upon the learning styles of their SGL to determine if the leader’s learning style affected student learning. Data were collected from SGLs and campers who attended sessions three and four of camp during the summer of 2011.

Each of the 33 SGLs completed the PLSI. Results of the PLSI identified one of four possible learning styles for each SGL: (a) Action Oriented Realists, which are those learners who are sensing extroverts, (b) Action Oriented Innovators are learners who are intuitive extroverts, (c) Thoughtful Realists are sensing introverts, and (d) Thoughtful Innovators are those learners who are intuitive introverts.

During the registration process on the first day of camp, campers included in the sample completed a multiple choice pre-test examination created to measure their knowledge of information to be presented during small group sessions during the four-day camp. At the end of the last session of camp on day four, campers included in the study completed a post-test. The

pre-test and post-test were comprised of the same questions with the items and response choices randomly rearranged.

Treatment

Kirk (1995) stated that experimental designs must include random treatment assignments of all study participants. During the camp registration process campers were randomly assigned to a small group. For the purpose of data analysis, the 33 small groups were divided into four treatment groups based upon the learning style of the SGL. In this way, all study participants were randomly assigned to one of the four treatment levels. Small groups met in seven breakout sessions throughout the four-day camp resulting in 12 hours of instruction.

Data Collection Instruments

Two instruments were employed to meet the objectives of the study. The Paragon Learning Style Inventory (PLSI), a nationally utilized learning style inventory, determined SGL learning style. The PLSI is a 52-item questionnaire that employs the four Jungian (Jung, 1971) dimensions: (1) extraversion versus introversion, (2) sensation versus intuition, (3) thinking versus feeling, and (4) judging versus perceiving (Shindler & Yang, 2003). For the purpose of this study, the researchers chose to utilize two of the four Jungian dimensions, extraversion (E) versus introversion (I) and sensation (S) versus intuition (N) because those are the factors that most affect teachers and students in academic environments (Shindler & Yang, 2003). Each item on the PLSI is comprised of a single stem statement or question and two dichotomous answers. The PLSI has been in use for more than 10 years and is constantly reviewed to improve validity and reliability. The most recent reliability tests indicate that the split half reliability for each dimension of the PLSI is between .90 and .94 (Shindler & Yang, 2003).

A criterion-referenced test, the Camp Communications Content Examination (CCCE), was designed in cooperation with the camp curriculum author to assess camper learning of the curriculum taught during small group sessions. The lead researcher collaborated with members of Oklahoma FFA Association state staff to identify the curriculum objectives for small group sessions. As a result, a 17-item multiple-choice exam was created. The CCCE included questions that tested campers in the areas of personal communication, family communication, and team communication.

Face and content validity of the CCCE were established through the use of a panel of experts consisting of three teacher educators, two leadership curriculum development specialists, and three high school students. Teacher education faculty members at Oklahoma State University were included on the panel because of their expertise in creating summative assessments. Additionally, the faculty members were charged with the task of reviewing the instrument for face validity, as all were published researchers in the agricultural education literature field. Two leadership curriculum development specialists were considered experts due to their involvement in writing curriculum for state and national FFA conferences such as Made for Excellence, Advanced Leadership Development, and Washington Leadership Conference. Finally, three high school students were included to ensure that all directions were clearly stated and were written at an age-appropriate reading level and to review the instrument for face validity. Wiersma and Jurs (1990) outlined eight methods to establish reliability of criterion-referenced tests. Table 1 describes the actions taken by the researchers to ensure that the CCCE was a reliable instrument. Based upon the criteria set forth by Wiersma and Jurs (1990) the CCCE was considered reliable.

Table 1

Actions Taken to Establish Reliability of the Camp Communications Content Examination (Criterion-Referenced Test)

Method Action Taken

Homogeneous Items The CCCE was created to test campers in the area of social communications. Test questions were directly linked to curriculum objectives. All test items were multiple-choice.

Discriminating Items Leadership curriculum development specialists confirmed that test items were difficult enough to be discriminative.

Enough items A test item represented each camp curriculum subject or objective. Careful attention was given to creating a test with enough items to assess student learning while recognizing time constraints for data collection at camp.

High QualityCopying and Format

Test were formatted into booklets and printed on a high quality laser printer. Three high school aged students assessed the tests for face validity and formatting problems.

Clear Directionsfor the Students

Campers were provided extensive written directions explaining how to properly respond to test items. The three high school age students were also asked to provide feedback pertaining to written test directions.

A Controlled Setting All study participants were provided a separate area monitored by the primary researcher to complete the pre-tests during the registration setting. The post-test was also administered and monitored in a controlled setting during a time set aside for students to complete the exam on the last day of camp.

Motivating Introduction Students were informed of the reason for the study and the positive implications the results would have on future camps. The information was included in the consent form signed by each student and again reread by the researcher before each test was administered.

Clear Directionsfor the Scorer

The lead researcher created a test key for scoring purposes. Furthermore, item responses for each participant were entered into SPSS version 18 to compute a test score.

Analysis of Data

SPSS for Macintosh 18.0 was utilized to analyze SGL responses to the PLSI. To reduce human error in score calculations, student pre-test and post-test scores were also computed using SPSS. Students were then assigned a treatment group code 1 – 4 determined by the learning style of their SGL. The data were analyzed using the SPSS repeated measures general linear model function. Analysis of variance (ANOVA) was used to determine differences between pre-test and post-test scores and the interaction between SGL learning style and camper test scores. A partial eta squared calculation was used to determine treatment effect size. Sphericity was not tested for because the repeated measures variable only has two levels; therefore sphericity was met (Field, 2009). All ANOVA assumptions were met.

Limitations of the Study

Learning style is a naturally occurring personal trait that cannot be assigned. As a result, the researchers were unable to control treatment group sizes. Unequal group sizes affected the overall power of the statistical analysis because of the small n found in one of the four groups.

Findings

Objective 1 - Identify the Learning Style of SGLs

As shown in Table 2, all four learning styles were represented among the 33 SGLs. Twenty-four SGLs possessed learning styles of the extravert type while nine SGLs were identified to have one of the two introvert learning styles. The most common learning style was Action Oriented Realists (f = 14; 43%). The next most common learning style was Action Oriented Innovators (f = 10; 30%). The largest introvert group was Thoughtful Realists (f = 7; 21%). Finally, the fewest SGLs were classified as Thoughtful Innovators (f = 2; 6%).

Table 2

Number of Small Group Leaders Who Possessed Each Learning Style and Number of Campers in Each Treatment Group

SGL Treatment Group Size

Treatment Group Determined by SGL Learning Style f % n %Action Oriented Realists (ES) 14 43 78 43Action Oriented Innovators (EN) 10 30 50 28Thoughtful Realists (IS) 7 21 40 22Thoughtful Innovators (IN) 2 6 13 7Total 33 100 181 100

Objective 2 - Assess the Level of Campers’ Learning From Small Group Sessions

As shown in Table 3, the mean pre-test scores for campers on the CCCE was 5.35 (32%) out of a possible score of 17. The mean post-test score for all participants was 9.91 (58%). ANOVA was utilized to test the null hypothesis using an F statistic to determine the difference between the means of the two test scores (see Table 4). There was a significant difference between pre-test and post-test scores, F(1, 177) = 309.51, p = .00. The effect size was (ηp

2 = .64), indicating that a large portion (~64%) of the variance can be attributed to the treatment.

Table 3

Mean Raw Test Scores and % Correct by Treatment Group

Group n M SD % Correct

Pre-Test Action Oriented Realists (ES) 78 5.53 2.02 33

Action Oriented Innovators (EN) 50 5.02 2.08 30

Thoughtful Realists (IS) 40 5.53 2.03 33

Thoughtful Innovators (IN) 13 5.00 1.63 29

Total 181 5.35 2.01 32

Post-Test Action Oriented Realists (ES) 78 9.97 2.62 59

Action Oriented Innovators (EN) 50 9.98 2.48 59

Thoughtful Realists (IS) 40 10.20 2.78 60

Thoughtful Innovators (IN) 13 8.38 1.56 49

Total 181 9.91 2.58 58

Objective 3 - Determine if SGL Learning Style Affects Campers’ Learning During Small Group Sessions

As shown in Table 3, the mean pre-test score for the group led by Action Oriented Realists was 5.53 (SD = 2.02) and the mean post-test score was 9.97 (SD = 2.62). The mean of the pre-test scores for the group led by Action Oriented Innovators was 5.02 (SD = 2.08) and the post-test mean score was 9.98 (SD = 2.48). The group led by Thoughtful Realists resulted in a mean pre-test score of 5.53 (SD = 2.03) and mean post-test score of 10.20 (SD = 2.78). Finally, the group led by Thoughtful Innovators had a mean pre-test score of 5.00 (SD = 1.63) and post-test mean score of 8.38 (SD = 1.56).

Analysis of variance was utilized to test the null hypothesis using an F-ratio to determine the significance (α = .05) of the four treatment levels (see Table 4). The interaction of test scores and SGL learning style produced an F(3, 177) = 1.25, p = .29. This interaction was not statistically significant; therefore, there was no significant treatment effect by SGL learning style and the researchers failed to reject the null.

Table 4

Analysis of Variance Summary Table

Source Type III Sum of Squares df MS F p ηp

2

Test Scores 1132.31 1 1132.31 309.51* .00 .64

Test Scores * SGL Learning Style 13.73 3 4.58 1.25 .29 .02

Error (Test Scores) 647.53 177 3.66*p < .05.

Although SGL learning style was not found to statistically significantly affect student learning during small group sessions, it is notable that the group led by Thoughtful Innovators scored 9% lower than the total mean post-test scores (see Figure 1).

Pre-Test Post-Test0

2

4

6

8

10

12

ESENISIN

Figure 1. Mean raw test scores by treatment group.

Conclusions, Implications, and Recommendations

Results of this study lead to the conclusion that SGLs have an extraverted learning style. In fact, this group is exceptionally extraverted in its learning style. According to the findings of Shindler and Yang (2003), normal populations are close to evenly split between extraverted (52%) and introverted (48%) learning styles. Comparatively, more than 75% of the SGLs had an extraverted learning style. These conclusions raise some interesting questions. First, does the SGL selection process favor applicants who are extraverts? It is quite possible that extraverts respond to the volunteer selection process at higher rates than introverts due to their affinity for sharing thoughts and working in action groups. The highly-charged camp environment provides that milieu in which an extrovert thrives. Second, are extraverts attracted to situations like those provided by the opportunity to lead small groups at camp? As Jung’s (1971) trait theory asserted, extraverts orient themselves to the people around them. The camp’s energetic environment, mentorship opportunities, and social setting comply with the extravert’s preferences.

The second objective of the study was to assess the level of campers’ learning from small group sessions. It is concluded that campers gained knowledge about the concepts and facts taught in small group breakout sessions. Because the difference between the average score on campers’ pre-test and post-test was statistically significant, the researchers reject the null hypothesis that there is no difference between campers’ pre-test and post-test scores on a test of facts and concepts associated with the curriculum taught during small group sessions. Although the average post-test score nearly doubled the average pre-test score, the researchers question if the amount of the content learned is satisfactory. The average post-test score was 58% correct. In the traditional educational setting, such a score would result in a failing grade. It is recommended that camp planners consider the following questions. First, is the average post-test score a satisfactory outcome of the instruction provided during small group breakout sessions? If not, what is the satisfactory score? Second, what factors influence the post-test score? More specifically, does the camp setting create or provide too many distractions to be conducive to learning? Do SGLs need

more training in the content of the curriculum and pedagogical concepts? Is the material too difficult for campers to master?

No significant differences were found to exist between test means of the four treatment groups; therefore, the null hypothesis that there is no association between test scores of campers and the learning style of their SGL is not rejected. The findings of this study do not agree with Shindler and Yang’s (2007) assertion that teacher learning style has any effect on student-learning outcomes. Furthermore, the results of this study indicate that although the group of SGLs is largely homogenous in learning style there is no negative impact on camper learning outcomes. This conclusion is in alignment with Mehdikhani (1983) who concluded that learning style of teachers did not impact academic achievement of students in English and mathematics courses. Nevertheless, caution should be observed regarding this component of this research. As mentioned earlier, the number of SGLs who possessed each learning style is a limitation of this study. The small group size (n = 13) of the fourth group of campers who were taught by Thoughtful Innovators (IN) limited the power of the statistical analysis. As a result, it is possible that a type II error was committed leading the researchers to fail to reject a false null hypothesis (Kirk, 1995).

Considering the divergence of conclusions generated from previous studies examining the impact of instructor learning style upon student achievement (Hansen and Stansfield, 1982; McDonald, 1984; Mehdikhani, 1983; Paradise & Block, 1984) the researchers recommend that similar studies of this nature be conducted. Care should be taken to assure that each test group is large enough to provide the statistical power needed. The stratified sampling technique (Creswell, 2008) would be appropriate for this situation. Stratification can be used when the population displays an imbalance of a sample characteristic (Creswell, 2008).

Based on the results of this study, it is recommended that camp planners establish a strategy to include a summative assessment of camp, SGLs and campers. If one of the goals of camp is to develop campers’ knowledge of leadership and personal development, then outcome and factors influencing it should be evaluated each year. Faculty members and research associates in the Department of Agricultural Education, Communications and Leadership at Oklahoma State University should be involved in designing and administering this evaluation plan. Data collected as a result of summative assessments will provide vital information for camp planners and curriculum directors who make budgetary and educational decisions. It is further recommended that camp planners establish learning standards and set camper learning achievement goals to serve as benchmarks to measure learning success in future camps. Further research is needed in the area of camper learning style and factors that contribute to cognitive gain in an FFA camp setting.

References

Cano, J., & Garton, B. L. (1994). The relationship between agricultural preservice teachers’ learning styles and performance in a methods of teaching agriculture course. Journal of Agricultural Education, 35(2), 6-10. doi: 10.5032/jae.1994.02006

Cano, J., Garton, B. L., & Raven, M. R. (1992). The relationship between learning and teaching styles and student performance in a methods of teaching agriculture course. Journal of Agricultural Education, 33(3), 16-22. doi:10.5032/jae.1992.03016

Comings, T. C. (1977). The FFA camping experience; Its values and future. The Agricultural Education Magazine, 49(12), 269-272

Connors, J. J., Falk, J. M., & Epps, R. B. (2010). Recounting the legacy: The history and use of FFA camps for leadership and recreation. Journal of Agricultural Education, 51(1), 32-42. doi:10.5032/jae.2010.01032

Creswell, J. W. (2008). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Upper Saddle River, NJ: Pearson Education, Inc.

DeBello, T. C. (1990). Comparison of eleven major learning styles models: Variables, appropriate populations, validity of instrumentation and the research behind them. Journal of Reading, Writing and Learning Disabilities, 6, 203-222.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Dyer, J. E., & Osborne, E. (1996). Effects of teaching approach on achievement of agricultural education students with varying learning styles. Journal of Agricultural Education, 37(3), 43-51. doi:10.5032/jae.1996.03043

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191.

Field, A. (2009). Discovering statistics using SPSS. Los Angeles: Sage.

Friedel, C. R., & Rudd, R. D. (2006). Creative thinking and learning styles in undergraduate agriculture students. Journal of Agricultural Education, 47(4), 102-111. doi: 10.5032/jae.2006.04102

Garton, B. L., Spain, J. N., Lamberson, W. R., & Spiers, D. E. (1999). Learning styles, teaching performance, and student achievement: A relational study. Journal of Agricultural Education, 40(3), 11-20. doi:10.5032/jae.1999.03011

Gregorc, A. F. (1982). An adult’s guide to style. Columbia, CT: Gregorc Associates, Inc.

Hansen, J., & Stansfield, C. (1982). Student-teacher cognitive styles and foreign language achievement. A Preliminary study. Modern Language Journal, 66, 263-273.

Hoover, T. S., Scholl, J. F., Dunigan, A. H., & Mamontova, N. (2007). A historical review of leadership development in the FFA and 4-H. Journal of Agricultural Education, 48(3), 100-110. doi:10.5032/jae.2007.03100

Javornik, J. J. (1962). Leadership training camp for future farmers can be fun, as well as educational. The Agricultural Education Magazine, 34(11), 249-250.

Jung, C. G. (1971). Psychological types. Princeton, NJ: Princeton University Press.

Keels, B. (2002). Early FFA and NFA camp history in South Carolina. AgriBiz!. Columbia, SC: South Carolina FFA Public Affairs.

Kirk, R. E. (1995). Experimental design. Pacific Grove, CA: Brooks/Cole Publishing Company

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall, Inc.

Lambert, M. D., Smith, A. R., & Ulmer, J. D. (2010). Factors influencing relational satisfaction within an agricultural education mentoring program. Journal of Agricultural Education, 51(1), 64-74. doi: 10.5032/jae.2010.01064

Lindner, J. R., Murphy, T. H., & Briers, G. E. (2001). Handling nonresponse in social science research. Journal of Agricultural Education 42(4), 43-53. doi:10.5032/jae.2001.04043

McCrea, A. (2011). Vision of blue heart of gold: A history of Oklahoma FFA. Maysville, MO: Blake & King.

McDonald, E. R. (1984). The relationship of student and faculty field dependence/independence congruence to student academic achievement. Journal of Agricultural Education, 44, 725-731.

Mehdikhani, N. (1983). The relative effects of teacher teaching style, teacher learning style, and student learning style upon student academic achievement (Doctoral dissertation). Retrieved from ProQuest. (8314893)

Oltman, P. K., Raskin, E., & Witkin, H.A. (1971). Group embedded figures test. Palo Alto, CA: Consulting Psychologists Press.

Paradise, L. V., & Block, C. (1984). The relationship of teacher-student cognitive style to academic achievement. Journal of Research and Development in Education, 17(4), 57-61

Shindler, J., & Yang, H. (2003). Paragon Learning Style Inventory [Instrument and interpretation material]. Unpublished instrument. Retrieved from http://www.calstatela.edu/faculty/jshindl/plsi/index.html

Whittington, M. S., & Raven, M. R. (1995). Learning and teaching styles of student teachers in the northwest. Journal of Agricultural Education, 36(4), 10-17. doi: 10.5032/jae.1995.04010

Wiersma, W., & Jurs, S. G. (1990). Educational measurement and testing (2nd ed.). Needham Heights, MA: Allyn and Bacon.

Examining the Agricultural Education Fishbowl: Understanding Perceptions of Agricultural Education Stakeholders in Higher Education

Marshall A. Baker, Oklahoma State UniversityDiane Montgomery, Oklahoma State University

AbstractThe purpose of this study was to better understand the perceptions of individuals in higher education regarding agricultural education to enhance collaboration. Using Q-methodology to capture subjective perceptions of agricultural education, this study identified the perspectives of 23 key stakeholders in higher education. Analysis resulted in three perspectives of agricultural education: (a) Supportive Idealist, (b) Critical Academic, and (c) Progressive Agricultural Educator. The supportive idealist typology represents an overall positive view of agricultural education that sees the benefit of the program to public schools. Critical academics, typically defined by lab scientists, believe that agricultural education lacks the academic rigor to consider itself a deliverer of core academic content, and they hold a somewhat negative view of the program as it stands today. Progressive agricultural educators value the program and recognize that agricultural education serves as a support to core content instruction; yet, not the sole provider of core math, science, and reading concepts. Using Social Judgment Theory, keys for collaboration are presented for each perspective.

IntroductionThe evolution of agricultural education has experienced an emphasis on integrating core subject content, such as science and math, into agricultural education classes nationwide. The focus on integration followed a report from the National Research Council (NRC) in 1988 that called for agriculture courses to be expanded to increase the rigor of math and science content in order to better prepare students for careers in a changing agricultural industry. More recently Roberts and Ball (2009) put forth a conceptual framework depicting the idea that agricultural education can serve as both a deliverer of agricultural content and the contextual medium for the learning of science, math, and reading, but warned that this dual-purpose model will require collaboration and a reconceptualization of agricultural education at all levels.

Integration of core academic concepts into agricultural education classrooms has been shown effective in terms of increasing student academic success. Studies have supported the notion that teaching math (Parr, Edwards, & Leising, 2006; Stone, Alfed, & Pearson, 2008), science (Chiasson & Burnett, 2001; Enderlin, Petrea, & Osborne, 1993; Myers & Dyer, 2006; Roegge & Russell, 1990; Whent & Leising, 1998) and reading (Park & Osborne, 2007) in the context of agricultural education can lead to higher academic achievement in each respective area. Thus, teacher education should focus on better preparing teachers to this end (Meyers & Dyer, 2004).

In order to better prepare teachers, research has been conducted to identify the attitudes and perceptions of agricultural teachers toward science integration (Balschweid & Thompson, 2002; Conroy & Walker, 2000; Dyer & Osborne, 1999; Myers & Thompson, 2009; Scales, Terry, & Torres, 2009; Warnick, Thompson, & Gummer, 2004). Each of the studies reported positive perceptions of agricultural educators toward the integration of science. However, many of these studies included a recommendation to augment the core academic content curriculum required at the undergraduate levels and provide additional in-service and pre-service workshops in order to enhance agricultural educators’ comfort level with academic content. Scales, Terry, and Torres (2009) found that although secondary agricultural educators were confident in their ability to teach science concepts, they did not have an acceptable level of scientific competence. The study recommended the augmentation of science-based courses into the teacher education program.

The recommendation to integrate science, math, and reading into agricultural education will require support from instructors who specialize and teach those core content courses at the college level.

Studies conducted at the secondary level (Dyers & Osborne, 1999; Pavelock, Vaughn, & Kieth, 2001; Thompson, 2001) found that principals, counselors, and superintendents held positive perceptions of the role agricultural education plays in supporting instruction in core academic areas, but felt agricultural teachers could benefit from more collaboration with core content experts in order to be more fluent and confident when teaching math, science and reading concepts. This philosophical shift would require collaboration and adjustments by everyone from higher education to local high school teachers. Reform has called for enhanced curriculum, professional development centered on integration, augmentation of teacher preparation programs to include more core content instruction, a philosophical shift towards integration by the agricultural education profession, and collaboration between core content area instructors and agricultural educators (Myers & Thompson, 2009). Collaboration at the secondary level has been examined and recommendations to enhance co-curricular efforts have been offered throughout the literature (Conroy & Walker, 2000; Dyer & Osborne, 1999; Myers & Thompson, 2009; Parr, Edwards, & Leising, 2006; Pavelock, Vaughn, & Kieth, 2001).

In order for agricultural education to move forward to the next level of the new integration, agricultural education as a field must become less independent in its research and more openly collaborative and interdisciplinary (Osborne, 2011). Myers and Thompson (2009) extend the notion of interdisciplinary collaboration explaining that teacher education programs could be a catalyst in helping the profession move forward in terms of integrating academics. The literature is clear in making the recommendation that collaboration should first begin in higher education, within the teacher preparation programs, in order for students to adopt collaborative behaviors (Grady, Dolan, & Glasson, 2010; Warnick, Thompson, & Gummer, 2004). This focus on interdisciplinary integration has been a focus of many interested in moving beyond education reform and into the transformation of America’s educational system (Futrell, 2010). Futrell argued that transformation “will require faculty to remove the silos within schools and across university campuses and collaborate with one another and key community members to prepare prospective educators who will inherit the responsibility for redesigning America’s schools for the realities of more interactive, interdisciplinary learning environments” (p. 432).

Although researchers have consistently called for collaboration among a myriad of partners within higher education, a paucity of research exists regarding the perceptions of agricultural education held by that specific population. Removing barriers of collaboration at the secondary level must begin with the modeling of collaborative behavior within communities in higher education (Conroy & Walker, 2000). In order for agricultural education to move forward in fostering collaborative relationships with academic departments in higher education it is imperative that the views of key stakeholders at the university are better known. Stephenson, Warnick, and Tarpley (2008) recognized the importance of judgment in the decision to collaborate within agriculture education and thus stated “research should focus on resolving misconceptions and superiority inculcations of academic departments and agriculture departments” (p. 116).

Research Problem and Purpose of the StudyAs the necessity to integrate core academic content into agricultural education increases, so does the need for collaboration at all levels as indicated by research priority five in the National AAAE Research Agenda (Doerfert, 2011). Two research questions drove the study: (a) what is the various perception typologies held by individuals in higher education regarding agricultural

education? (b) what views comprise the latitude of rejection, acceptance, and non-commitment (Social Judgment Theory, Brunswik, 1952) for the identified perception typologies?

Theoretical FrameworkThe theoretical framework used for the study was Social Judgment Theory (Hammond, Rohrbaugh, Mumpower, & Adelman, 1977), which highlights the concept that individual’s judgments and decisions play a large role in their attitude and willingness to participate in collaborative efforts. SJT assumes that people rarely have direct access to the true state of what they are asked to judge (Hammond, Rohrbaugh, Mumpower, & Adelman 1977). In the context of the study, higher education faculty members are asked to judge the secondary agricultural education program without direct access. Instead, the environment gives rise to a number of cues such as interaction with agricultural education staff, with agricultural education students, or through lived experiences that are of imperfect validity and reliability but serve as the base for inferences. The zone of ambiguity lies between the cues and the true and judged states. It is this space that evokes different judgment processes and that makes judgment tasks difficult. Cooksey (1996) expounded, “this zone [of ambiguity] represents the region of entangled probabilistic relationships with which a decision maker must cope in order to successfully achieve in the decision task” (p. 11). Hammond, Stewart, Brehmer, and Steinmann (1975) add that the zone of ambiguity “is the source of the human judgment problem, as well as the source of the misunderstanding and disputes that occur when judgments differ” (p. 275).

SJT was grounded in Brunswik’s (1952) “lens theory” and was later expanded by Hammond, Kaplan and Schwartz (1975) and Cooksey, Freebody, and Davidson (1986) in order to expand its use to include the study and description of how human judgments are formed that relate to decision-making. SJT assumes that a person’s own attitude serves as a judgmental standard and anchor. Sherif, Sherif, and Nevergall (1965) explained that opinions on any subject are placed on a continuum in reference to that judgment standard. Opinions that most characterized the individual’s own opinions are in the latitude of acceptance. Opinions that are determined to be most objectionable by the judger are placed in the latitude of rejection, and the latitude of non-commitment consists of opinions that are neither accepted nor rejected. The greater the discrepancy between the judger’s opinion and the opinion being presented, the less change in attitude occurs. SJT demonstrates the importance of people’s prior attitudes as they seek to collaborate. Unique to this study, interpretation was extended to include the identification of the latitudes of acceptance, rejection, and non-commitment based on the SJT theoretical framework (see Figure 1). Statements located in the 9, 10, and 11 columns were interpreted to determine the latitude of acceptance, and statements located in the 1, 2, and 3 columns were interpreted to determine the latitude of rejection. All statements falling between the 4 and 8 columns were used to interpret the latitude of non-commitment.

Figure 1. Form board as interpreted by the Social Judgment Theory

MethodologyQ methodology was determined to be the best research design to describe the subjective views of stakeholders from different disciplines in relation to one another. Subjectivity in Q method allows each point of view to be expressed through a sorting procedure (McKeown & Thomas, 1988). Q methodology, which was developed by William Stephenson in 1935, is a research method that seeks to study points of view on a specific topic resulting in viewpoint typologies as a result of factor analysis. Q methodology draws on both qualitative and quantitative analyses to understand in depth the points of view on a subject (Tuler, Webler, & Finson, 2005). Unlike traditional factor analysis where the correlations between items are of importance, Q methodology utilizes factor analysis to systematically correlate the individuals who complete a sort (Brown, 1980). Individuals are asked to represent their own frame of reference by sorting statements that reflect possible opinions on a subject. Through purposive selection of individuals with unique points of view, Q researchers can reveal patterns of thought regarding any given subject, in this case, higher education stakeholders’ perceptions of agricultural education.

Instrument DevelopmentThe concourse represented the possible perceptions of university faculty toward agricultural education and was compiled through a review of literature (Balschweid & Thompson, 2002; Dyer & Osborne, 1999; Myers, Thoron, & Thompson, 2009; Pavelock, Vaughn, & Kieth, 2001; Thompson, 2001; Warnick, Thompson, & Gummer, 2004) as well as through ten naturalistic interviews with various stakeholders in higher education at Oklahoma State University. Interviews were conducted via social media channels as well as through direct interviews. Sampling the concourse for the Q set of statements for participants to sort yielded 41 statements. The statements were organized to reveal five homogenous concept groups: (a) content, (b) context, (c) affective effects, (d) social development, and (e) other. Heterogeneity was then sought within each concept in order to present different ways of approaching the overall concept. The form board is the forced distribution where participants place cards in relation to the others to identify the statements that resonate most like or most unlike their reactions personally. Factors that emerge from this process represent shared perspectives that exist within a particular group of people (Brown, 1980).

Participant and ProcedureIn Q method, the participants of the study are referred to as the P-set. The P-set for this study consisted of 23 individuals, 14 males and 9 females, who were involved in higher education and specifically in areas that are of value to the agricultural education teacher preparation program. The participants were purposively chosen to provide an understanding of the perceptions held by individuals in higher education in relation to the secondary agricultural education program. Among the 23 participants, 20 identified themselves as white, two as American Indian, and one as Latino. These individuals were professors, student affairs faculty, university staff, and graduate students from both within and outside of the academic unit of agricultural education at Oklahoma State University. Within agricultural education was defined as individuals who employed through the Department of Agricultural Education, Communications, and Leadership at Oklahoma State University. Outside of agricultural education included other faculty and staff of Oklahoma State University who had exposure to agricultural education, but were not directly employed or involved with the agricultural education department. Individuals both inside and outside of the department were selected in order to better understand the congruence of the perceptions held by both groups. Purposive sampling was employed in order to collect perspectives of key personnel associated with collaboration between agricultural education and higher education. Each participant was given a description of the study and, if they were willing to participate, completed a consent form (approved through the Internal Review Board) before data was collected. Appointments were then arranged in order to conduct the Q sorts.

Data were collected over the spring academic semester of 2011. The sorting process began with the laying out of the form board and the 41 statements placed on small cards. Participants were then read the condition of instruction: “In your opinion, what is agricultural education?” and were given the sorting cards. As prescribed by McKeown and Thomas (1988), participants were first asked to read through the cards to become familiar with them, and to sort the cards into three piles: (a) on the far right, statements they agreed with, (b) on the far left, statements they disagree with, and (c) in the middle statements that they are either neutral or uncertain about. Next, participants were asked to identify the five statements that were most unlike their opinion and place them in 1 (-5) and 2 (-4) columns, and the five cards most like their opinion to be placed in the 10 (+4) and 11 (+5) columns. Participants were then asked to continue to fill in the form board moving back and forth from most like to most unlike their opinion, leaving the middle column to be filled in last. Participants were encouraged to share any of their ideas about the sort and/or their individual opinion on the condition of instruction, which was captured by the researcher and would later help in factor interpretation. Three sets of statistical procedures were used, including correlation of all statements within a sort to all other sorts, factor analysis of the correlation matrix, and the computation of factor scores. These statistical procedures were conducted using PQmethod 2.11, software specially designed for the analyses used in Q studies and downloaded free from qmethod.org. Finally, each individual was asked if they would volunteer to be contacted by phone in order to conduct member checks of factor interpretation. Seven follow-up interviews were conducted involving high and pure loaders in each of the three perspectives.

Following the statistical analyses resulting in the factor arrays, factor interpretation helped bring meaning to the resultant viewpoints. Once the interpretive process began, the factors were referred to as arrays. Brown (1980) shared that the importance of a factor cannot be determined by statistical criteria alone, but must take into account the social and political setting to which the factor is organically connected” (p. 42). To this end, each resultant array is organized based on the calculation of a z-score associated with each statement. Each statement is then placed on the form board to create a visual representation of the array. The resulting form board assists in the

interpretation of the true nature of each array. The interpretive process involved the researchers analyzing each statement, in relation to others and interpreting the given viewpoint. Peer debriefing was utilized in order to gain feedback on the emergent themes.

ResultsThe chosen solution involved a three-factor principal component analysis followed by a varimax rotation. Examination of the factor matrix (see Table 1) for the purpose of finding the sorts that best define the final factor array was done by choosing sorts that were statistically-

Table 1Factor Matrix with Bold Marking Defining Sorts

Sort#/Gender

Age Yrs. Exp.

Professional Area Factor Loadings1 2 3

1-male 33 11 Ed. Faculty 0.7325 0.1249 0.22882-female 29 2 Ed. Faculty 0.8229 0.0408 0.34933-male 47 17 Ed. Faculty 0.7127 -0.2155 0.12534-female 52 14 Ed. Faculty 0.8450 0.1155 0.16615-male 63 NA Student Affairs 0.7145 -0.0523 0.39467-female 36 14 Ed. Staff 0.8016 0.0898 0.11788-female 50 30 Ed. Staff 0.7897 -0.1372 0.09519-male 56 25 Ed. Faculty 0.7650 -0.3267 0.183410-female 40 4 Ed. Staff 0.6863 -0.0914 0.381111-male 46 5 Ed. Staff 0.5415 0.0126 -0.039912-female 64 27 Ed. Faculty 0.7647 -0.3365 0.086617-female 53 32 Ag Sciences Faculty 0.7652 0.2787 -0.099719-female 24 0 Ag. Ed. Staff 0.6804 0.1073 0.336721-male 31 7 Ag. Ed. Staff 0.4857 -0.0572 0.429313-male 58 37 Science Faculty 0.2290 0.5262 -0.314414-male 50 31 Ag. Ed. Staff -0.1753 0.6871 0.392416-male 60 36 Ag Sciences Faculty 0.0824 0.7695 -0.059318-female 48 25 Ag Sciences Faculty -0.3287 0.7242 0.108020-male 49 25 Ag. Ed. Staff 0.4283 0.0913 0.491322-male 36 11 Ag. Ed. Staff -0.0417 -0.0645 0.78126-male 35 1 Ed. Faculty 0.4708 0.4366 0.460715-female 31 3 Ag Sciences Faculty 0.5084 0.1486 0.660523-male 47 23 Ag. Ed. Staff 0.6273 0.0375 0.6106Number of sorts defining a factor 14 4 2Explained Variance 38% 11% 13%significant for only one factor. A participant’s sort was considered to define a factor if the correlation of the sort to the factor was statistically significant as determined by the equation: (1/√number of statements) * 2.6 (McKeown & Thomas, 1988). Using this equation as a guideline, 0.43 was determined to be the standard by which sorts were determined to be significant to the study. The distributions of the sorts across the factors are reported in Table 1.

Factors that are in boldface met the criteria and are used when defining the factor and its meaning. The factor score demonstrates the level of similarity. For example, sorter number seven would be considered a high and pure loader as she loaded relatively high on the first factor and low on the other two. The sorts of high and pure loaders are expected to most closely define the sort, and as such, these individuals were contacted in order to confirm the interpretation of the factors. If an individual was somewhat similar to more than one view, like sorter number six, the

sort was not used to define the resulting arrays and was considered to be a confounding sort. If a sort did not meet the statistical level of significance on any of the factors, it was not considered to be a defining sort. In this study, fourteen sorts defined the first factor, four defined the second, and two defined the third. None of the sorts were non-significant in this study and three were considered confounded. Another statistic important to note is the correlation of the three identified factors to each other. This analysis provided an indication of how different or how alike the chosen factors were to one another. In this study correlations were r = -0.10 (1-2); 0.29 (1-

3); and 0.03 (2-3), indicating that the solution chosen represented different viewpoints.

Research Question One Three factors emerged from the analysis. Each of these factors represent a perspective one in higher education may hold in regards to agricultural education at the secondary level. Each perspective will be described in narrative form to describe the perspective of those who defined the specific perception. Specific statements will be provided to support the concepts that drive the narrative as the perspectives are based on the reconstructed factor arrays (noted in parentheses with the statement number, z-score, and array position noted in that order). The array position of every statement discussed includes array positions and z-scores to allow statement comparisons for each perspective.

Perspective A: Supportive Idealist. A supportive idealist sees the agricultural education cup as half full. With the exception of two agricultural education staff, this perspective represents a view from outside the agricultural education fishbowl. Defining sorts included six faculty members from the College of Education, a professional within the College of Education, three staff members from the College of Education, a student affairs administrator, one faculty in agricultural sciences, and two staff members in agricultural education. Six males and eight females made up this perspective. Ages ranged from 29 to 64 years and years of work experience ranged from 0 to 30 years. Much of their exposure is through students they have worked with, agricultural education teacher education faculty, or through intermittent exposure in the rural communities of which they live and work. These individuals make up the majority of the “fishbowl” referred to in this study.

Supportive idealists overwhelmingly support the first concept that agricultural education is a valuable part of any secondary school. As administrators, school faculty, and community members make decisions regarding how to best develop their students, agricultural education is an item worthy of attention and funding (27, 1.19, +4). Attending livestock exhibitions, career development events, leadership seminars, and other activities specific to the program, provide a hands on approach to learning and are of value (40, 1.79, +5). Most importantly, those activities can have value in augmenting the school curriculum (39, - 1.60, -4). Though agriculture has changed a great deal, agricultural education remains relevant and necessary (9, -1.84, -5). Agricultural education programs are important to communities and bring together a number of people who are interested in the education of an area’s youth (30, 1.22, +4). As one participant shared, “agricultural education is many kids’ ‘thing.’ Students are involved in band, sports, art, and … ag. It is really important for those students. It is the way some students express their gift. It is a great program for kids.”

The personal growth of students is a second concept of particular interest. An individual whose sort defined this perspective shared that, “I can almost always identify which students were a part of 4-H or FFA within a couple of days. It is really amazing how much they stand out. I’ve always thought it was such a great program.” Anybody who feels that agricultural education develops poor habits within students has a limited understanding or experience with the program (35, -1.95, -5). In general, agricultural education contributes to the holistic growth of students.

Though the magic is really in the development of the whole student, an excellent by-product is the support of academic success, which was the third concept to emerge. Secondary agricultural educators have a unique opportunity, through hands-on experiential activities, to support math, science, and language arts skills through contextual learning (40, 1.79, +5; 15, -1.64, -4; 8, -1.64, -4). Many students that struggle to be successful in the standard text book learning environment of today’s high schools, find that agricultural education is where concepts are more relevantly applied to real-world contexts (13, 1.54, +5).

Perception B: Critical Academic. Critical academics are usually professors in some type of hard science. These professors are both within and outside of the college of agriculture and play an important role in teaching agricultural education students core science and math classes such as biology, agronomy, and agricultural engineering. Specifically, this perspective included one faculty in science, two faculty members in agricultural sciences, and one faculty in agricultural education. Thus, this perspective included individuals viewing agricultural education from both inside and outside of the fishbowl. The age range of those defining this perspective was 48 to 60, and years of experience ranged from 25 to 37 years. Three males and one female defined this perspective.

Concept one, highlights the idea that critical academics are not sold on the academic rigor associated with the secondary agricultural education program. Agricultural education is the study of agriculture and anyone who identifies it as a rigorous math or science class has clearly not had adequate exposure to the curriculum or do not have adequate awareness of what academic rigor involves (10, -2.28, -5). Agricultural education has a clear purpose, but it is not to enhance core academic content (16, -1.41, -4). This lack of rigor is further validated through standardized test scores, of which, agricultural students do not score significantly higher than their peers not in the program (11, -1.63, -4). Let us all be honest, agricultural education is about teaching leadership and citizenship to students (31, 1.14, +4) and the program remains primarily vocational. One such critical academic shared that she had taught biology at one point in her career and said, “I simply didn’t see the rigorous science that I taught in the agricultural education programs I got to witness. It has it’s place but not as a science class.”

A second concept is that those of this perspective are not impressed by the culture of agriculture education. Agricultural education community is close-minded and lacks diversity in both thought and demographics (28, 2.18,+5). It is rarely interested in collaboration (37, 1.30, +5), which is unfortunate because there is real value in the refreshing experiential approach agricultural education brings to the academic table. If an administrator had to make tough decisions regarding programs to include in a high school, agricultural education wouldn’t be a priority in a school (38, -1.29, -4). Finally, this perspective is built on the concept that the agricultural program does have value – one cannot deny that. Students love the opportunity to get outside, work with their hands, learn experientially, and compete in various contests (40, 1.22, +4). However, high achieving students are not drawn to agricultural education (23, -2.32, -5), which is a result of the lack of rigor and vocational nature of the program. Agricultural education is a place where lower achieving students can really find a place in high schools (24, 1.12, +4).

Perspective C: Progressive Agricultural Educator. Progressive agricultural educators are unique in that they acknowledge what is instead of idealizing what agricultural education should be. One individual whose sort defined the progressive agricultural educator array shared that, “throughout the sort I was thinking of the ought versus is debate – what ought the program become and what is the program. I have an idea of what it ought to be in my mind, but that is not what it is currently.” Two males defined this perspective, ages 36 and 49, with 11 and 25 years

of professional experience. Both individuals were faculty in the agricultural education department at Oklahoma State University.

One concept that was foundational to this perspective is that agricultural education holds value for a diverse student population. This perspective believes that all students, regardless of race, academic ability, hometown demographics, socio-economic status, interest, or career choice can benefit from agricultural education, as indicated by strong position of statement 6 (6, 1.53, +5). One of the most important components of agricultural education really lies in the opportunity for students to connect on a more personal level with an adult educator while in school (20, 1.41, +4). Though academics are always a focus of an educational setting, it is acceptable for students to let their hair down and have fun at times (19, 1.51, +4) as this holds academic value in and of itself. Everyone is affected by agriculture and thus, it is an important concept for all students (6, 1.53, +5). Confining agricultural education to rural and/or technical schools is a mistake as it can be molded to fit students in all settings (1, - 2.06, -5; 4, -1.84, -5). Agricultural education is not just for college-bound students, but can support students in choosing a number of paths, including college and/or vocational options (12, -1.20, -4).

A second concept emphasizes the idea that agricultural education supports academic performance in a myriad of disciplines. Agricultural education is truly a support system for core academic instruction (13, 1.31, +4). Supporting the instruction of the core academic curriculum should always be encouraged, but agricultural education is not a rigorous math, science, or language arts class (10, -1.19, -3) and to make it that is a diversion from the true purpose of the program (16, -1.51, -4). Progressive agricultural educators believe that the myriad of activities offered through agricultural education contribute to the overall success and growth of a student (39, -1.52, -4). As researchers, they have seen evidence that agricultural education enables students to perform better on standardized exams (11, 1.53, +5), but it is more a result of overall student development, motivation, mentorship, and contextual learning.

Research Question Two Regarding the second research question, concepts were derived through interpretive analysis of the collection of statements found within the zone of rejection, zone of non-commitment, and zone of acceptance (according to Figure 1). Those concepts were then used to develop collaboration strategies for each perspective.

Supportive Idealist Collaboration Strategy. Three concepts were identified for the zone of rejection, zone of non-commitment, and zone of acceptance, which provided the foundation for the collaboration strategy. When seeking to collaborate with a supportive idealist it is important to capitalize and focus on the idea that agricultural education develops students both intellectually and personally (18, 1.18, +4; 35, -1.95, -5). They believe it is a good investment of funds, and thus, are willing to discuss and explore ways to integrate agricultural education into high schools (27, 1.12, +4). One idea that supportive idealists will accept and act upon is the idea that there is value in the varied method of instruction utilized by agricultural education (40, 1.79, +5). Agricultural education serves a certain population of students and stakeholders should be proud of that (13, 1.54, +5). When working with supportive idealists, it is important to avoid discussing the idea that agricultural education is becoming out of date and impractical (35, -1.95, -5). Individuals who hold this view are made uncomfortable by the idea that agricultural educators are unfit to teach core concepts (15, -1.64, -4; 8, -1.84, -4), and do not support the notion that agricultural education is best for rural communities only (4, -1.03, -3). This violates their “idealistic” view of agricultural education. Those that hold this perspective are not interested in academic rigor (10, 0.22, 0), discussion of college preparation (12, .32, 0), or the idea that agricultural education is the answer to low standardized test scores (11, -.49, -1). They

see agricultural education as a support to core academic content, but won’t commit to the idea that the program is a science, math, or language arts course (17, .05, 0). Finally, these individuals are indifferent about the idea that agricultural education is the best program for students, as they see choice as a positive thing (2, .53, +1; 3, .09, 0).

Critical Academic Collaboration Strategy. Collaboration with a critical academic can be difficult, but is possible. A friendly and humble demeanor is key to the latitude of acceptance. Critical academics hold the opinion that agricultural education is not interested in collaboration and are close-minded (37, 1.30, +4; 28, 2.18, +5). Acknowledging that as a weakness and demonstrating a desire to partner in order to enhance rigor can foster more positive attitudes around collaboration. Discussions around the important role agricultural education can play in a community (30, 1.08, +3) and in the development of students (25, 1.08, +3) could gain traction, as these are key positive perceptions of a critical academic. Focus first on the strengths that agricultural education offers as an elective (41, .91, +3). Critical academics do not buy into the idea that agricultural education is about core academic rigor (10, -2.28, -5; 11, -1.63, -4; 16, -1.41, -4). One runs the risk of paralyzing collaborative efforts by starting with the idea that agricultural education is currently a rigorous math or science course in the context of agriculture as this concept is clearly in the latitude of rejection for this perspective. Convincing this group that the academic achievers are found in agricultural education will be met with heavy resistance. These individuals are not interested in the “feel good” benefits of the program such as meaningful relationships with teachers (20, -.19, 0), drive (21, -.11, 0), society-ready citizens (33, .34, +1), and the idea that FFA represents what is right with today’s youth (29, -.32, -1). The various contests, events, conferences, and traditions that those involved in agricultural education hold dear, are not of interest to this group (39, .28, 0) as their focus is on academics and rigor.

Progressive Agricultural Educator Collaboration Strategy. Collaboration with those of this perspective should begin with the idea that agricultural education develops the whole person, which leads to growth both academically and personally. It is important to broaden one’s perspective when discussing whom the program can impact, because progressive agricultural educators believe all students can benefit (6, 1.53, +5; 9, -1.18, -3). One unique discussion point within the latitude of acceptance is the idea that there is value in the “fun” that students have while enrolled in an agricultural education course (19, 1.51, +4). Those of this perspective are proud life-long supporters of the program, but understand it is not perfect. They find value in being proud of what agricultural education does and whom it serves, but are always looking for ways to move agricultural education forward. While collaborating with a progressive educator, do not label agricultural education as a math, science, or language arts course (10, -1.19, -3; 16, -1.51, -4), but find common ground in the idea that the program supports the core academic teachers in the learning process. The most prominent theme in the latitude of rejection for progressive agricultural educators is the idea that the program is for rural communities focused on production agriculture. This idea fails to value the power of agricultural education for all students (1, -2.06, -5; 4, -1.84, -5). These individuals are not interested in discussing the current culture of the profession, as they are looking to the future (28, .34, +1). The idealized view is not of interest to these individuals as they have been around long enough to know what is, and is not, occurring in secondary agricultural education programs (40, -.13, 0; 29, .00, 0). The power of this perception is not necessarily in the need for collaboration, as only those in agricultural education define it. However, by juxtaposing the latitudes of rejection and acceptance of both the critical academics and the progressive agricultural educators, promising areas of collaboration arise which will be further explained in the conclusion section that follows.

Conclusion

Although some may have noted territorial contention and competition between departments (Stephenson, Warnick, & Tarpley, 2008), the findings of this study suggested that is not the case when working with those of the supportive idealist view. It is important to note that a number of professors within agricultural education share this viewpoint with faculty in other departments. This is beneficial in terms of healthy discussions around collaboration and the tearing down of the departmental silos discussed by Futrell (2010) in order to move towards school transformation. Research should be conducted in order to better understand whether the idealist view is stable. Are these individuals simply restating agricultural education rhetoric they have been exposed to or are these beliefs and perspectives anchored in lived experiences? Bottom line, the fact that so many partners of agricultural education hold such a supportive and idealistic view, whether stable or not, of agricultural education is encouraging as the profession seeks to build the collaboration within higher education.

Critical academics represent a much different perspective that warrant careful attention as agricultural educators seek to enhance the science and math coursework within the agricultural education teacher preparation program. The literature has consistently called for augmented science and math courses at the college level (Grady, Dolan, & Glasson, 2010; Scales, Terry, & Torres, 2009) and critical academics are important in making that recommendation a reality. It is imperative that efforts be devoted to rebuilding a positive, collaborative relationship with individuals of this opinion, as they are key stakeholders in helping agricultural educators be more proficient in the integration of core content. They view agricultural education as lacking rigor, close-minded, and not interested in collaboration. However, there are areas of the program they see as valuable such as the refreshing, experiential, hands-on approach to learning provided by the program. These individuals are not impressed by the traditions and “feel good” elements of agricultural education, but can be drawn back into collaboration through healthy and honest conversations around the need to enhance rigor. Supportive idealists may further the frustration of critical academics through the sharing of an idealist view of core content integration, but progressive agricultural educators have a unique perspective that could be valuable in repairing this important partnership.

Progressive agricultural educators have a sincere belief in the program and support it whole-heartedly, but they are more realistic in their description of agricultural education. This perception could be valuable, as mentioned above, in forming collaboration with hard scientists that have concerns of the rigor of agricultural education curriculum. This view shared the sentiment provided by Scales, Terry, and Torres (2009) that, “the conventional wisdom of integrating more science, mathematics, and reading into the secondary agriculture curriculum must be carefully considered” (p. 109). Progressive agriculturists agree that this shift towards rigorous science integration must be carefully evaluated and would disagree with Pavelock, Vaughn, and Kieth (2001) who suggested that an increase in academic rigor is needed in spite of the fact that experiences like “showing livestock and judging contests” (p. 481) would receive less emphasis. Those of this perspective would argue that experiences are not merely extracurricular, but are experiential learning activities, when framed correctly, and hold great value in terms of personal and academic development. Research around the true benefits of these experiences that have always been integral to agricultural education should be conducted to better understand the effect they have on personal development and academic achievement in core content areas.

Progressive agriculturists see the value of increasing the focus and attention given to the integration of core academic contents, but don’t neglect to honor the other elements of agricultural education that may be what truly has a positive effect on students. An honest and forthright discussion of what agricultural education is, and what it ought to be, would help foster

collaboration between stakeholders in higher education. This debate is occurring nation-wide within the profession, and thus, it is recommended that a Q study be conducted to understand the various perspectives within leaders of agricultural education that exist in terms of what agricultural is, and what it ought to be. Examining the fishbowl from within would expose valuable perceptions that could foster conversations leading to a strengthened voice and concerted effort in ensuring that agricultural education remains viable in today’s changing society.

References

Balschweid, M. A., & Thompson, G. W. (2002). Integrating science in agricultural education: Attitudes of Indiana agricultural science and business teachers. Journal of Agricultural Education, 42(2), 1-10. doi: 10.5032/jae.2002.02001

Brown, S. R. (1980). Political subjectivity: Applications of Q-methodology in political science. London, UK: Yale University Press.

Brunswik, E. (1952). The conceptual framework of psychology. Chicago, IL: University of Chicago Press.

Chiasson, T. C., & Burnett, M. F. (2001). The influence of enrollment in agriscience courses on the science achievement of high school students. Journal of Agricultural Education, 42(1), 61-71. doi: 10.5032/jae.2001.01061

Conroy, C. A., & Walker, N. J. (2000). An examination of integration of academic and vocational subject matter in the aquaculture classroom. Journal of Agricultural Education, 41(2), 54-64. doi: 10.5032/jae.2000.02054

Cooksey, R. W. (1996). Judgment analysis: Theory, methods, and applications. San Diego, California: Academic Press.

Cooksey, R. W., Freebody, P., & Davidson, G. R. (1986). Teachers’ predictions of children’s early reading achievement: An application of social judgment theory. American Education Research Journal, 23, 41-63.

Doerfert, D. L. (Ed.) (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

Dyer, J. E., & Osborne, E. W. (1999) The influence of science applications in agriculture courses on attitudes of Illinois guidance counselors at model student-teaching centers. Journal of Agricultural Educaiton, 40(4), 57-66. doi: 10.5032/jae. 1999.04057

Enderlin, K. J., Petrea, R. E., & Osborne, E. W. (1993). Student and teacher attitude toward and performance in an integrated science/agriculture course. Proceedings of the 47th Annual Central Region Research Conference in Agriculture Education, St. Louis, MO.

Futrell, M. H. (2010) Transforming teacher education to reform America’s P-20 education system. Journal of Teacher Education, 61(5), 432-440. doi: 10.1177/0022487110375803

Grady, J. R,, Dolan, E. L., & Glasson, G. E. (2010) Agriscience student engagement in scientific inquiry: Representations of scientific processes and nature of science. Journal of Agricultural Education, 51(4), 10-19. doi: 10.5032/jae.2010.04010

Hammond, K. R., Rohrbaugh, J., Mumpower, J., & Adelman, L. (1977). Social judgment theory: Applications in policy formation. In M. Kaplan & S. Scwartz (Eds.), Human judgement and decision processes in applied settings (pp. 1-30). New York, NY: Academic Press.

Hammond, K. R., Stewart, T. R., Brehmer, B., & Steinmann, D. (1975). In M. Kaplan & S. Schwartz (Eds.), Human judgment and decision process (pp. 272-309). New York, NY: Random House.

McKeown, B., & Thomas, D. (1988). Q methodology. Thousand Oaks, CA: Sage.

Myers, B. E., & Dyer, J. E. (2004). Agriculture teacher education programs: A synthesis of the literature. Journal of Agricultural Education, 45(3), 44-52. doi: 10.5032/jae.2004.03044

Myers, B. E., & Dyer, J. E. (2006). Effects of investigative laboratory instruction on content knowledge and science process skill achievement across learning styles. Journal of Agricultural Education, 47(4), 52-63. doi: 10.5032/jae.2006.04052

Myers, B. E., & Thompson, G. W. (2009). Integrating academics into agriculture programs: A Delphi study to determine the perceptions of the national agriscience teacher ambassador academy participants. Journal of Agricultural Education, 50(2), 75-86. doi: 10.5032/jae.2009.02075

Myers, B. E., Thoron, A. C., & Thompson, G. W. (2009) Perceptions of the national agriscience teacher ambassador academy toward integrating science into school-based agricultural education curriculum. Journal of Agricultural Education, 50(4), 120-133. doi: 10.5032/jae.2009.04120

National Research Council. (1988). Understanding agriculture: New directions for education. [Electronic Version]. Committee on Agricultural Education in Secondary Schools, Board on Agricultural Education in Secondary Schools, Board on Agriculture, and the National Research Council. Retrieved from National Academics Press: http://www.nap.edu/catalog.php?record_id=766

Osborne, E. W. (2011). Taking agriculture education to the next level: Distinguished lecture presented at the 2010 Annual Conference of the American Association for Agricultural Education, Omaha, Nebraska, May 25, 2010. Journal of Agricultural Education, 52(1), 1-8. doi: 10.5032/jae.2011.01001

Park, T. D., & Osborne, E. (2007). A model for the study of reading in agriscience. Journal of Agricultural Education, 48(1), 20-30. doi: 10.5032/jae.2007.01020

Parr, B. A., Edwards, M. C., & Leising, J. G. (2006). Effects of a math-enhanced curriculum and instructional approach on the mathematics achievement of agricultural power and technology students: An experimental study. Journal of Agricultural Education, 47(3), 81-93. doi: 10.5032/jae.2006.03081

Pavelock, D., Vaughn, P., & Kieth, L. (2001) Perceptions and perceived knowledge levels of Texas public school superintendents regarding the agricultural science and technology program. Proceedings of the 28th Annual National Agricultural Education Research Conference, 471-484. New Orleans, LA.

Roberts, T. G., & Ball, A. L. (2009). Secondary agricultural science as content and context for teaching. Journal of Agricultural Education, 50(1), 81-91. doi: 10.5032/jae.2009.01081

Roegge, C. A., & Russell, E. B. (1990). Teaching applied biology in secondary agriculture: Effects on student achievement and attitudes. Journal of Agricultural Education, 31(1), 27-31.

Scales, J., Terry, R., Jr., & Torres, R. M. (2009). Are teachers ready to integrate science concepts into secondary agriculture programs? Journal of Agricultural Education, 50(2), 100-111. doi: 10.5032/jae.2009.02100

Sherif, C. W., Sherif, M. S., & Nebergall, R. E. (1965). Attitude and attitude change. Philadelphia: W.B. Saunders.

Stephenson, L. G., Warnick, B. K., & Tarpley, R. S. (2008). Collaboration between science and agriculture teachers. Journal of Agricultural Education, 49(4), 106-119. doi: 10.5032/jae.2008.04106

Stone, J. R., Alfeld, C., & Pearson, D. (2008). Rigor and relevance: Enhancing high school students’ math skills through career and technical education. American Educational Research Journal, 45(3), 767-795. doi: 10.3102/0002831208317460

Thompson, G. (2001). Perceptions of Oregon secondary principals regarding integrating science into agricultural science and technology programs. Journal of Agricultural Education, 42(1), 50-60. doi: 10.5032/jae.2001.01050

Thompson, G. W., & Schumacher, L. G. (1998). Selected characteristics of the National FFA Organization’s agriscience teacher of the year award winners and their agriscience programs. Journal of Agricultural Education, 39(2), 50-60. doi: 10.5032/jae.1998.02050

Tuler, S., Webler, T., & Finson, R. (2005). Competing perspectives in public involvement: Planning for risk characterization and risk communication about radiological contamination from a national laboratory. Health and Risk, and Society, 7(3), 247-266.

Warnick, B. K., Thompson, G. W., & Gummer, E. S. (2004). Perceptions of science teachers regarding the integration of science into the agricultural education curriculum. Journal of Agricultural Education, 45(1), 62-73. doi: 10.5032/jae.2004.01062.

Whent, L. S., & Haskell, L. J. (1989). Identifying staff development needs of Cooperative Extension faculty using a modified Borich needs assessment model. Journal of Agricultural Education, 30(2), 26-32. doi: 10.5032/jae.1989.02026

Greenhouse Facility Management Experts Identification of Competencies and Teaching Methods to Support Secondary Agricultural Education Instructors:

A Modified Delphi Study

Edward A. Franklin, University of Arizona

Abstract

In this study the Delphi technique has been used to develop a list of educational competencies for preparing secondary agricultural education instructors to effectively manage their school greenhouse facilities. The use of specialized facilities in agricultural education requires appropriate preparation of agricultural education teachers. The Delphi technique uses an anonymous panel of experts for suggestions and assessments aiming at consensus. Thirteen experts from multiple schools and universities took part in the investigation. The study used a series of three web-based questionnaires to determine competencies that teachers need to know, to be able to perform, and to identify effective teaching methods for teachers to obtain these competencies. The first round instrument consisted of three open-ended questions, and a series of questions to validate the background of the members of the panel. In the second round, respondents were asked to rate each competency and teaching method using a seven-point Likert-type scale. Median scores and interquartile values were calculated. Panel members were sent a copy of their individual responses as well as the group responses for review. In the third round, panel members were requested to indicate their level of agreement with each item using a five-point Likert-type scale.

Introduction

Agricultural education teachers have access to specialized facilities and laboratories for teaching the content of the many curriculum areas. Specialized facilities and laboratories are often used to teach concepts and skills related agricultural mechanics, aquaculture, biotechnology, computers, forestry & natural resources, livestock, plant nurseries, and greenhouses (Newcomb, McCracken, McCracken, Warmbrod, & Whittington, 2004). Laboratory activities are viewed as “learning experiences in which students interact with materials and/or models to observe and understand the nature of agriculture and its underlying biological, physical, and social science components” (Myers, 2005, p.14). Identifying the needs of teachers to effectively manage facilities and laboratories is important to the successful management of the local program. Roberts, Dooley, Harlin, and Murphrey (2006) recognize that managing, maintaining, and improving laboratories was a program planning and management competency of successful agricultural science teachers. Numerous studies have examined the agricultural mechanic laboratory management competencies of high school agricultural education teachers (Johnson & Schumacher, 1989; Saucier, Terry, & Schumacher, 2009). However, fewer studies in the agricultural education literature address the issue of determining what teachers need to know, and what competencies they to acquire to successfully manage greenhouse laboratory facilities at their local programs (Lamberth, 1983).

Ornamental horticulture has emerged as one of the rapidly growing areas of production agriculture across the nation (Franklin, 2008; Lamberth, 1983; Watkins & Miller, 1984) and is gaining popularity among secondary agricultural education students (Galan, Lasanske, Warner, & DeLay, 2009). According to the National Agricultural Statistics Service (USDA, 2007) census of agriculture report, more than fifty thousand U.S. farms were classified as horticultural operations. The value of sales from horticulture was nearly six percent of the total value of agricultural products sold in the U.S. in 2007. “Greenhouse, nursery and floriculture operations account for 2.5 percent of all U.S. farms but employ 4.9 percent of hired farm workers, and pay 13.3 percent of farm labor expenses” (USDA, 2007).

Research on greenhouse use in agricultural education has addressed topics such as comparing knowledge scores of students with a greenhouse experience to students without a greenhouse experience (Rothenberger & Stewart, 1995), determining the horticulture coursework requirements in preservice agricultural education programs (Boone, 2002), identifying the technical agriculture inservice topics needs of traditional and alternatively certified agriculture instructors (Roberts & Dyer, 2004), the use of a greenhouse facility for supervised agriculture experience (SAE) opportunities as a source of student motivation (Lasanske & Warner, 2008), and a description of the use of greenhouses by agriculture instructors in Arizona (Franklin, 2008). Lamberth (1983) conducted a study to identify and validate competencies needed by Tennessee horticulture instructors to manage greenhouse and landscape design. Thirty-five high school teachers participated in the Delphi study. Lamberth recommended that findings from research should be used for developing inservice training programs for teachers. In a study conducted by Franklin (2008), the majority (76%) of local agriculture programs in Arizona had or planned to have greenhouse facilities as part of their instructional program, but over half of the study respondents either had no formal university preparation experience related to horticulture (nearly 30%) or had completed six or less post secondary units of instruction related to horticulture (28%). A lack of knowledge and experience with working with a greenhouse in a high school agricultural education program was one of the top-ranked barriers identified by the researcher as preventing teachers from being effective managers of their facilities. A recommendation from the researcher was that “professional development in the form of short courses should be developed to provide assistance to teachers with existing facilities to learn to become more proficient users” (2008, p.44). Can the effective use of a greenhouse contribute to student achievement? Rothenberger and Stewart (1995) conducted a study to assess the effectiveness of instruction in horticulture using and not using a greenhouse laboratory experience with the traditional classroom lecture/discussion technique. Findings of their research were that students who received a greenhouse laboratory experience scored significantly higher on a knowledge test than

did students who were taught the same lessons, without a greenhouse laboratory experience.

Theoretical Framework

For a teacher to be successful in managing a specialized facility, they must acquire and develop proficiency in specific competencies. The theoretical framework for competency education may be found in the “Novice-Expert” literature (Chambers, Gilmore, Maillet & Mitchell, 1996; Dreyfus, 2004) where the teacher develops proficiency by going through stages of development. A five-stage model for skill acquisition (Dreyfus, 2004)

The progression from novice to expert is divided into five stages, each with its own set of rules for learning and performance: novice, beginner, competence, proficiency, and expert or mastery (Chambers et al., 1996; Dreyfus, 2004). “Competency is the midpoint on a continuum of professional growth that normally extends over a period of 10-12 years” (Chambers et al., 1996, p. 615). A student or teacher with little to no experience managing a greenhouse may label themselves as a novice. With increased knowledge and experience, the individual moves through the five stages until they have achieved the level of “competence” on their way to the level of “expert”. Undergraduate students in a teacher-preparation program or secondary teachers with no previous horticulture experience (Franklin, 2008) are likely to be in the novice and beginner stages. Over time, with experience and training, the teacher moves through the stages of competence and proficiency to the level of expert or mastery. This is accomplished with the aid of a mentor who has achieved the level of expert or mastery Chambers et al, 1996; Dreyfus, 2004).

Could identifying what teachers need to know and specific experiences to perform to effectively manage a greenhouse laboratory facility in an educational setting aid teacher preparation programs identify appropriate courses? If so, what are effective methods for communicating this information to future teachers? If teachers can be moved from “novice to expert” in their knowledge and experiences can they be effective managers of greenhouse laboratory facilities?

Research Priority Five of the National Research Agenda for Agricultural Education and Communication (Osborne, 2007.) addresses the needs for research related to Agricultural Education in Schools: 5.4 Prepare and provide an abundance of fully qualified and highly motivated agriscience educators at all levels. Preparing teachers to manage specialized facilities begins with identifying appropriate knowledge and skills (Johnson & Schumacher, 1989; Saucier, Terry, & Schumacher, 2009). Should the preparation to teach horticulture-related courses and managing a greenhouse be included in the undergraduate coursework for future agricultural education teachers? The findings of this study will provide university teacher educators with information to prepare fully qualified agriscience educators to use a specialized facility in the delivery of horticulture

Purpose and Objectives

The purpose of the Delphi survey study was to gather greenhouse laboratory management experts’ perspectives on the development of a list of greenhouse laboratory management competencies needed by secondary agricultural education instructors, and preferred teaching methods to help teachers obtain the competencies. The specific objectives were:

(1) To identify the greenhouse laboratory management competencies teachers must know to effectively manage their laboratories;

(2) To identify the greenhouse laboratory management competencies teachers must be able to perform to effectively manage their laboratories;

(3) To determine effective methods of providing experiences for secondary agricultural education instructors to develop greenhouse laboratory management competencies.

Methodology

The Delphi method was selected for this study. It has come into extensive use within research and education, and there has been a review of use of this technique in agricultural education (Martin, 1998). Several examples of its use can be found in agricultural education research (Dyer, Breja, & Ball, 2003; Myers, Dyer & Washburn, 2005; Park & Rudd, 2005). Within competency development, the method has been used to identify lists of competencies need by secondary agricultural education teachers (Camp & Sutphin, 1991; Hudson, 1983; Johnson & Schumacher, 1990; and Miller & Foster, 1985).

There were several reasons for the selection of the Delphi method including anonymity (Figley & Nelson, 1988 as reported by Jenkins & Smith, 1994), geographical distance (Jenkins & Smith, 1994), cost, time, and the opportunity for participants to view the opinions of others. Buriak and Shinn (1989) suggested the Delphi method be used where a study progresses in phases, “each phase moving closer to satisfying objectives” (p.14). The phases of this research study are described below.

Phase I: Identification of the Expert Panel

The use of greenhouse facilities in an educational setting may not necessarily reflect the practices of found in the industry. Greenhouses may be used part-time to reflect the academic year instead of year-round (Franklin, 2008). The expert panel should be made up of individuals with knowledge and experience in managing greenhouse structures and facilities in an academic setting where the application of theory is the focus. In order to identify an appropriate panel of experts the researchers relied on nominations from members of the American Association for Agricultural Education (AAAE). An email invitation was sent to the AAAE-listserv requesting the nomination of professional colleagues or teachers experienced in greenhouse management in the education setting. A total of 29 names were submitted to the researchers.

This Delphi-method followed Dillman’s Total Design Method (2007) and employed multiple points of contact in an attempt to obtain an acceptable response rate. The panelists were engaged in teaching greenhouse management in the university or high school setting in institutions located across the nation. All 29 were invited contacted via electronic mail and invited to serve on the panel. If the respondents accepted the invitation to participate, they could click on the link embedded in the email and be directed to the web site hosting in the questionnaire. The first page of the questionnaire invited the nominees to participate and asked for their consent. A total of 15 nominees (52%) agreed to participate and completed the first round instrument. When the number of members on the panel is 13 or higher, the reliability of the method will be at least 0.80 (Dalkey, 1969).

Phase II: Collection of Opinion

In the first round, the expert panel was asked to offer their responses to three open-ended questions. Two broad categories used were knowledge and skills. The first two questions focused on the knowledge and skills teachers would need to obtain to effectively manage greenhouse facilities. The third open-ended question asked panelists to identify effective methods of acquisition for obtaining the competencies. To establish and validate the expertise of the panel, participants were requested to complete a set of selected demographic questions. It was decided in advance to use only three rounds in this research study in order to avoid the dropouts that could be expected if more rounds were used (Edgren, 2006). Since limiting the number of rounds could prevent total consensus, 75% agreement was chosen as the consensus level. The results from round one were collected and checked for content validity by a panel of experts which university greenhouse management research specialists and a graduate student in controlled environment agriculture. The resulting list consisted of 54 knowledge competencies, 50 ability competencies, and 32 methods of acquisition.

In the second round, participants were asked to score the importance of the items

on a Likert-type scale with “1” being least important, “2” being of little importance, , “3” being somewhat important, “4” moderate important “5” being important, “6” being very important, and “7” being extremely important. Results from round two were compiled and a new instrument was compiled for round three.

The questionnaire for round three contained three lists of the 54 knowledge, 50 ability, and 32 method acquisition competencies, and listed the median group rating, interquartile range (IQR) value for each competency, and the individual respondent’s rating for each competency. The respondents were asked to rate each competency now knowing the mean group rating and their previous rating for each competency. Respondents were able to provide comments and change their ratings.

Questionnaire three contained all three lists of competencies and listed the median group rating and IQR value for each knowledge, ability, and methods acquisition competencies. In an attempt to reach final group consensus, the respondents were asked

to indicate their level of agreement with each competency using a Likert-type scale with “1” being strongly disagree, “2” being disagree, “3” being uncertain, “4” being agree, and “5” being strongly agree. To determine the most agreed upon competencies, researcher-defined criteria were employed. Competencies that met the criteria of reaching a group mean score of 3.5 or higher were retained and are presented in the tables in the findings section.

Stone Fish and Busby (2005) recommend analyzing Delphi data using median and interquartile ranges to identify rates of group agreement and consensus. The use of interquartile ratings (IQR) provides the researcher with information “… about the variability in the data without being affected by extreme scores” (p. 247).

Phase III: Analysis of Data

Data were downloaded into Microsoft Excel ®, recoded and entered in SPSS 17.0 for Windows. The following anchors were used to describe the means for levels of agreement for all three lists of competencies: strongly disagree = 1.00 – 1.49; disagree = 1.50 – 2.49; uncertain = 2.50 – 3.49; agree = 3.50 – 4.49; strongly agree = 4.50 – 5.00.

Findings

Validating the Expert Panel

Members of the expert panel were from the states of Arizona, Arkansas, California, Connecticut, Georgia, Idaho, Kentucky, Ohio, Oklahoma, Missouri, and Texas. The make-up of the panel was mostly male (62%) holding a doctorate degree (62%). The reported number of year’s experience working with greenhouses ranged from less than five years (23%), six to ten years (15%), 11 to 19 years (15%), to 20 years and more (46%). Current rank or positions were self-identified as secondary agricultural educators (7%), university greenhouse specialists (7%), assistant professors (15%), associate professors (15%), full professors (15%), and greenhouse managers/coordinators (23%). All respondents were currently engaged in managing a greenhouse facility at an educational institution (100%).

Knowledge Competencies

The first objective of this study was to determine what teachers need to know to be able to effectively manage their greenhouse facilities. An open-end question posed was “What does a teacher need to know to effectively manage a greenhouse facility?” The panel responded with a list of 54 knowledge competencies. In the second round each panel member reviewed their individual responses and the group responses to each competency. Changes made were sent back to the researcher. For clarity of presentation, the competencies were grouped into clusters. For the knowledge competencies, the clusters were identified as safety practices, horticultural practices, facility maintenance, certification, program management, and marketing. In Table 1, panel members believe that knowledge competencies clustered as safety practices are important for teachers to

know to manage greenhouse facilities – rating safety practices with a mean of 4.77. Panel members identified two safety competencies with a mean range of 4.77 to 4.38. These were related to student safety around the greenhouse facility. Knowledge of horticultural practices related to factors associated with production of plants. Table 1 is a listing of each competency including, mean agreement rating and standard deviation.

Table 1

Delphi Round Three: What Teachers Need to Know to Effectively Manage a Greenhouse Facility (n=15).Knowledge M SDSafety Practices Appropriate safety practices in the greenhouse 4.77 0.44 When to let students work in the greenhouse after spraying 4.38 0.77Horticultural Practices Plant growth. 4.62 0.51 Environmental factors affecting plant growth. 4.38 0.65 Pest management for common greenhouse pests. 4.23 0.60 Basic plant anatomy. 4.23 0.93 Disease management. 4.15 0.55 Propagation skills. 4.15 0.80 Weed control 4.08 0.64 Pruning and pinching skills. 4.00 0.71 Fertilization 3.92 0.76 Plant identification. 3.85 0.69 Nutrient deficiency symptoms. 3.85 0.69 Nutrient solutions for specialty crops/systems. 3.77 0.73 Soil media. 3.69 0.85 Plant physiology 3.69 1.18 Chemistry of nutrient solutions 3.67 1.15 Growth schedule of plants for year round production. 3.62 1.26 Pour thru method and testing for EC, pH. 3.54 1.13Facility Maintenance Basic greenhouse equipment operation/maintenance. 4.54 0.52 Appearance of greenhouse is a direct reflection of teacher. 4.31 0.63 Greenhouse irrigation systems. 4.23 0.73 Operation of heating system 4.15 0.55 Operation of cooling system 4.15 0.55 Nutrient delivery control system and how it works, and troubleshoot. 4.15 0.55 Electrical systems in a greenhouse. 4.08 0.64 Automated greenhouse control systems. 4.00 0.71 Troubleshooting problems with environmental control systems. 3.92 0.64 Automated emergency sensor systems 3.92 0.76 Shade cloth use and installation 3.54 0.88Certification State & Federal laws concerning pesticides. 4.00 0.71

Qualify for state pesticide applicator's license. 4.00 0.58 State & federal laws concerning labor & labor safety. 3.92 1.04Program Management Basic math skills: greenhouse production computations. 4.31 0.48 Which crops students can be successful growing within one semester. 4.23 0.60 Balancing classroom instruction with greenhouse work time. 4.08 0.64 Budget planning and analyses. 4.00 0.71 Scheduling timing for planting to ensure desired plant size at defined

times.3.92 0.86

Knowledge of maintaining an equipment & supply inventory. 3.83 1.03Institutional policies of ethics and harassment. 3.77 1.09Human resource management(volunteers, staff, etc) 3.69 0.85Trends in the horticulture industry 3.62 1.04Ordering and acquiring seeds, plugs and stock plants. 3.62 1.12

Marketing Pricing plant product 3.77 0.93 Popular crops that will sell to the public. 3.54 1.20 Marketing of greenhouse crops. 3.54 1.27

Note: agree = 3.51-4. , strongly agree = 4.51-5.00

Ability to Perform

Question two of the first round instrument asked the panel to provide a list of competencies that teachers need to be able to perform to effectively manage their greenhouse facilities. The panel responded with a list of 51 competencies. Each panel member reviewed their individual responses and the group responses to each competency. Changes made were sent back to the researcher. Table 2 is a listing of each competency including the median rating, interquartile value, mean agreement rating, standard deviation, and rating descriptor.

Table 2

Delphi Round Three: Abilities Considered Essential to Effectively Manage Greenhouse Laboratories (n=13).Ability to Perform M SDHorticultural Practices Recognize plant stress 4.92 0.28 Identify pests and how to control them. 4.69 0.48 Grow a plant of good quality to maturity 4.69 0.48 Watering and fertilizing effectively 4.62 0.51 Scout biweekly and correctly identify insects and diseases. 4.38 0.65 Identify plant health problems. 4.38 0.51 Properly fertilize material. 4.31 0.48 Calculate correct fertilizer concentrations for injector. 4.31 0.63 Successfully grow a salable, blooming plant, not overgrown on sale day. 4.31 0.95 Prepare or control the nutrition program needed for the crop. 4.23 0.44

Diagnose and correct nutrition problems 4.23 0.44 Effective use of pest control methods 4.23 0.83 Perform typical cultural practices (pruning, training, etc.) for the plants

grown.4.23 0.60

Set up growing systems (pots, potting soil, hydroponics, etc). 4.23 0.73 Perform propagation of plant material. 4.15 0.69 Identify plant material 4.08 0.76 Implement IPM program that does not use harsh chemicals. 4.00 1.08 Observe changes in plants and operating systems 3.77 1.01Safety Practices Apply safety practices and procedures. 4.85 0.38 Maintain and repair a respirator and other PPE. 4.00 0.91Pedagogy Use the greenhouse for hands-on experiments 4.46 0.66 Connect careers to the greenhouse. 4.23 0.60 Balance the use of the greenhouse (i.e., who gets to use it for specific

classes.4.15 0.80

Teach students about all aspects of the greenhouse. 4.00 0.82Facility Management Program the environmental control system. 4.54 0.52 Manage temperatures in the greenhouse. 4.46 0.52 Initiate emergency cooling and heating during power failures. 4.31 0.48 Basic plumbing skills (assembly) 4.23 0.44 Repair irrigation equipment. 4.23 0.73 Repair broken irrigation lines. 4.23 0.73 Utilize and adjust control system to ensure proper environments /lighting. 4.23 0.44 Program the nutrient delivery system. 4.15 0.55 Program climate control systems with the set-points needed. 4.08 0.49 Calibrate fertilizer injector 4.00 0.91 Perform basic equipment maintenance: change a fan belt, replace cooling

pads.3.85 0.55

Select necessary sensors & control systems needed for the greenhouses facility.

3.77 0.73

Manipulate greenhouse coverings. 3.77 0.93Planning & Management Schedule work for students and volunteers 4.31 0.63 Set up and keep good records (repairs, pest problems, plant growth, etc.) 4.31 0.63 Generate funds from plant sales 3.92 0.86 Assemble a basic drip irrigation system. 3.69 0.63 Budget for replacement costs for equipment or glazing. 3.69 0.63 Organize space usage. 3.62 0.77 Be on-call 24/7 as long as the greenhouse is in operation. 3.54 1.45Communications Interact with faculty, staff, and students 4.69 0.63 Gain the support of school and district administration. 4.31 0.63Marketing

Determine selling prices for crops that will provide enough funding to purchase plants/supplies for next year but still sell in their local market.

4.00 0.91

Effectively run a plant sale. 3.85 0.90 Create public relations from greenhouse 3.85 0.99Note: Agree = 3.50-4.49, Strongly Agree = 4.50-5.00

Effective Teaching Methods

The members of the panel were asked to respond to the open-ended question, “What is the best method for teachers to obtain these competencies?”. The panel provided 31 responses. During the second round, the panel was asked to rate each method using the 7-point Likert-type scale. The median and interquartile (IQR) values were calculated. Each panel member was sent a copy of their individual responses, as well as the group median responses to review and make changes. After receiving feedback from the panel, a subsequent round was posted. Each method was ranked by median value and IQR. The panel was invited to review the list and express their level of agreement on a five-point Likert-type scale. Table 3 presents the findings of the panel.

Table 3

Delphi Round Three: Methods to Acquire Competencies to Manage Greenhouse Laboratories (n = 13).Method of Acquisition M SDCollege Coursework Enrolling in horticulture courses 4.38 0.65 Completing a college course of study related to greenhouse

horticulture and management,4.15 1.07

Completing a student teaching experience - learn by observing others in the field.

4.15 1.14

Workshops/InserviceAttending greenhouse-related short courses. 4.15 1.07 Participating in teacher inservice training 4.08 0.49 Participating in summer training programs 4.08 0.49 Attend day seminars related to greenhouse production. 4.08 0.49 Attend agriculture teacher state and regional meetings that offer related workshops.

4.08 0.64

Attending workshops on pest management 4.00 0.0 Attend workshops provided by greenhouse manufacturers. 3.69 0.75 Attend workshops provided by greenhouse control systems

companies3.69 0.75

Internship Experience Completing work experience in a greenhouse 4.54 0.52 Serving an internship before graduation with reliable greenhouse

operator.3.92 1.12

Working part-time in a greenhouse facility. 3.92 0.76 Shadowing a greenhouse production manager as an internship 3.77 0.83

Experiential To actually grow plants or crops 4.54 0.66 Conduct student project experiments in the school greenhouse. 3.85 0.80 Trial & error method; keep records and learn from mistakes. 3.69 0.85Field Trips Visit a neighboring agriculture department with greenhouses to get

ideas.4.23 0.60

Attend horticulture trade and garden shows 3.69 0.85 Attend industry trade shows and workshops 3.62 0.96

Note: Agree = 3.50-4.49, Strongly Agree = 4.50-5.00

There were several teaching methods which were identified and rated by the panel as most effective for helping teachers to obtain competencies needed to effectively manage their greenhouse facilities. Items receiving a median value of “5”, “6” or “7” were considered moderately to extremely effective and retained from second round to the third round for approval. The lower interquartile values indicate little variance among responses. For the purpose if interpreting the levels of agreement, real limits were applied (5.00 – 4.50 = strongly agree; 4.49 -3.50 = agree; 3.49-2.50 = uncertain; 2.49-1.50 = disagree; and 1.49-1.00 = strongly disagree).

Conclusions

One assumption of utilizing the Delphi method is that the respondents serving on the panel are knowledgeable of the subject (Johnson & Schumacher, 1989). For this study, the respondents must have experience in managing a greenhouse in an educational setting, rather than a commercial setting. Based an analysis of the demographics reported by the respondents, the researchers felt this assumption was met.

The knowledge-based competencies identified by the panel are their perceptions of what teachers need to know to be effective greenhouse facility managers. The panel reached consensus on 48 of 54 (89%; agree or strongly agree) of the competencies. The ability-based competencies identified by the panel are their perceptions of what teachers should be able to perform to effectively manage their greenhouse facilities. The panel reached consensus on 49 of 50 (98%; agree or strongly agree) of the competencies. For methods –acquisition of the competencies the panel reached consensus on 21 of 30 (70%; agree or strongly agree) methods.

Knowledge of safety related to students was the highest rated competency. Horticultural practices related to the environmental factors affecting the growth and production of plants rated highly with the panel. Operation and maintenance of the greenhouse facility were considered important for teachers to know, as well as certification requirements when dealing with pests and chemicals. Knowledge of program planning and management related to teaching classes, budgeting, scheduling, budgeting and product marketing was the remainder of identified competencies.

Ability competencies consisted of how to perform horticultural practices related to setting up a greenhouse for plant production including: media preparation, planting, water, fertilizing, thinning, pruning, harvesting, and packing. Facility operation and

maintenance skills included the environmental system (heating and cooling) mixing and applying liquid fertilizers, shading, and troubleshooting and repair of electrical and water lines. Pedagogical skills included how to teach students using the greenhouse, developing lessons and activities related to the operation of the greenhouse, managing the greenhouse on the academic calendar, budgeting and purchasing, communicating with school staff and the public and marketing greenhouse products.

The methods which are most effective for providing teachers with a means of acquiring the competencies needed to effectively manage their greenhouse facilities are those that provide on-site experiences with hands-on learning activities. College-level course work combined with an internship experience (i.e., industry internship, or student-teaching experience with a teacher experienced in greenhouse operation and management) will help move teachers through the early stages of competence. Professional development inservice opportunities are effective methods of learning will guide from novice and beginner stages to the competence stage. Years of experience and guidance from veteran teachers will help move teachers to the later stages toward expert or mastery. Specific topics should include: the operation of specific environmental (heating and cooling) systems, irrigation and fertilization systems, pest identification, prevention, and elimination, and plant physiology (understanding the needs and responses of the plant to the environment), and successful marketing of greenhouse products.

Implications

Are greenhouse laboratories as common a structure in secondary agricultural education programs as agricultural mechanic laboratories? Teacher-educators and state staff should assess their local programs to determine the number of such facilities in their states. States with local programs using greenhouse laboratories may be faced with the issue that teachers are ill-prepared to effectively manage their facilities (Franklin, 2008; Lamberth, 1983). Student achievement in horticulture can be enhanced by effective use of greenhouse (Rothenberger & Stewart, 1995) therefore; the preparation of future teachers must include technical coursework which includes a combination of horticulture courses and greenhouse facility operation. For teachers in the field, the professional development needs to focus on both operation and maintenance of existing facilities, and an understanding of plant physiology and growth. Local programs may have a greenhouse, but how are they being used and to what extent? Experienced teachers should be identified and recruited to mentor newer and less-experienced teachers in the field; a series of professional development workshops should be offered during the summer or on weekends. The workshop topics should build upon each other and give teachers an opportunity to experience different teaching greenhouse scenarios.

University teacher-educators should attempt to identify if horticulture and greenhouse facility management courses are available for pre-service students. The faculty responsible for delivery of the courses should be consulted to determine the appropriateness of the content of the courses for preparing future teachers to operate greenhouse facilities for educational purposes. If not, are similar courses available at local community colleges, and can the courses count for transfer credit?

Recommendations

Are greenhouse laboratories as common a specialized teaching facility in the local secondary programs as agricultural mechanic laboratories? Future research should be conducted to determine how important the identified greenhouse facility management skills are to secondary teachers. Follow up research should be conducted to determine if secondary teachers possess the competencies to manage greenhouse facilities, and to what extent do they practice the competencies. The use of an instrument such as the Borich Mean Weighted Discrepancy Scale (MWDS) (Borich, 1980) should be used to measure the importance of each of the knowledge and ability competencies. The findings should guide university technical course development and future professional development program needs for teachers of secondary programs with greenhouse laboratory facilities.

References

Boone, Jr., H. N. (2002). The current status of preservice agricultural education programs in the United States. Proceedings of the Annual National Agricultural Education Research Meeting, 29. Retrieved from http://aaaeonline.org/uploads/allconferences/210802.proceedings.doc

Borich, G. D. (1980). A needs assessment model for conducting follow-up studies. Journal of Teacher Education 31, (3), 39-42. doi:10.1177/002248718003100310.

Buriak, P., & Shinn, G. C. (1989). Mission, initiatives, and obstacles to research in agricultural education: A national Delphi using external decision makers. Journal of Agricultural Education 30, (4), 14-23. doi:10.5032/jae.1989.04014.

Camp, W. G., & Sutphin, H. D. (1991). Integrating microcomputers and related technologies in agricultural education. Journal of Agricultural Education 32, (1), 41-46. doi: 10.5032/jae.1991.01041

Chambers, D. W., Gilmore, C. J., Maillet, J.O., & Mitchell, B.E. (1996). Another look at competency based education in dietetics. Journal of American Dietetic Association 96, (6) 614-617.

Dalkey, N. C. (1969). The Delphi Method: An Experimental study of group opinion. Santa Monica: Rand Corporation.

Dillman, D. (2007). Mail & Internet Surveys: The Tailored Design method (2nd ed.). Hoboken, NJ: John Wiley & Sons Inc.

Dreyfus, S. E. (2004). The five-stage model of adult skill acquisition. Bulletin of Science, Technology & Society 24, (3), 177-181. doi:10.1177/020467604264922.

Dyer, J. E., Breja, L.M. & Ball, A. (2003). Problems in recruiting students into agricultural education programs: A Delphi study of agriculture teacher perceptions. Journal of Agricultural Education 44, (2), 86-95. doi:10.5032/jae.2003.02075

Edgren, G. (2006). Developing a competence-based core curriculum in biomedical laboratory science: A Delphi study. Medical Teacher 28, (5), 409-417.

Franklin, E. (2008). Description of the use of greenhouse facilities by secondary agricultural education instructors in Arizona. Journal of Agricultural Education, 49(3), 33- 45. doi: 10.5032/jae.2008.03034

Galan, A. J., Lasanske, D., Warner, W. J., & De Lay, A. M. (2009). Preparing to teach horticulture: A graduate course for future agriculture teachers. Proceedings of the American Association for Agricultural Education National Research Conference, Louisville, KY. Retrieved from http://www.aaaeonline.org/uploads/allconferences/AAAE_conf_2009/Posters/Preparing%20to%20Teach%20Horticulture.pdf .

Johnson, D. M., & Schumacher, L. G. (1989). Agricultural mechanics specialist identification and evaluation of agricultural mechanics laboratory management competencies: A modified Delphi approach. Journal of Agricultural Education, 23-28. Doi:10.5032/jae.1989.03023.

Jenkins, D. A., & Smith, T. E. (1994). Applying Delphi methodology in family therapy research. Contemporary Family Therapy, 16(5), 411-430.

Lamberth, E. E. (1983). Technical competencies in greenhouse management and landscape design needed by high school teachers of vocational horticulture in Tennessee. Research Report Series No. 5 ERIC Document ED 233 211.

Lasanske, D., & Warner, W. (2008, May/June). Sustainability of SAE via horticulture in urban agriculture programs. The Agricultural Education Magazine, 80 (6), 17-18, 24.

Martin, A. G. (1998). The Delphi technique:  An informal history of its use in agricultural education research since 1984. Journal of Agricultural Education, 39(1), 73-79. DOI: 10.5032/jae.1998.01073.

Miller, W. M., & Foster, R M. (1985). An assessment of microcomputer competencies needed by

vocational agriculture instructors in Nebraska. The Journal of the American Association of Teacher Educators in Agriculture, 26 (l), 30-38.

Myers, B.E. (2005). Incorporating science, math, and reading into the agriculture classroom: The role of the laboratory. The Agricultural Education Magazine, 77(5), 14-15.

Myers, B. E., Dyer, J. E., & Washburn, S. G. (2005) Problems facing beginning agriculture teachers. Journal of Agricultural Education 46, (3), 47-55. doi:10.5032/jae.2005.03047.

Newcomb,L. H., McCracken, J. D., Warmbrod, J. R. Whittington, M. S. (2004). Methods of Teaching Agriculture. Upper Saddle River, NJ: Prentice-Hall Publishing.

Osborne, E. W. (Ed.) (2007). National research agenda: Agricultural education and communications, 2007-2010. Gainesville: University of Florida Department of Agricultural Education and Communications, 20.

Park, T. D., & Rudd, R. (2005). A description of the characteristics attributed to students’ decisions to teach agriscience. Journal of Agricultural Education 46, (3), 82-94. doi: 10.5032/jae.2005.03082.

Roberts, T., G., Dooley, K., Harlin, J., & Murphrey, T. (2006). Competencies and traits of successful agriculture science teachers. Journal of Career and Technical Education, 22(2), 1-11.

Rothenberger, B. H., & Stewart, B. R. (1995). A greenhouse laboratory experience: Effects on student knowledge and attitude. Journal of Agricultural Education, 36(1), 24-30. DOI: 10.5032/jae.1995.01024.

Saucier, P. R., Terry, Jr. R., & Schumacher, L. G. (2009). Laboratory management in-service needs of Missouri agriculture educators. Paper presented at the 2009 Southern Region of the American Association for Agriculture Education Conference, USA, 176-192.

Smith, K. S., & Simpson, R. D. (1995). Validating teaching competencies for faculty members in higher education: A national study using the Delphi method. Innovative Higher Education, 19(3), 223-234.

Stone Fish, L., & Busby, D. M. (2005).The Delphi method. In D. H. Sprenkle & F. P. Piercy (Eds.). Research Methods in Family Therapy (2nd ed.) (pp. 238-253). Retrieved from http://site.ebrary.com.ezproxy2.library.arizona.edu/lib/arizona/docDetail.action?docID=10172280.

United States Department of Agriculture, (2007) Greenhouse, nursery and floriculture Operations. 2007 Census of Agriculture. Retrieved from http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/Fact_Sheets/nursery.pdf

Watkins, L. & Miller, L. E., (1984). Perceptions of the value of extended service in horticulture. Summary or Research Series, Ohio State University, Columbus, OH. Eric Document ED 239 083.

Texas Wind Energy Workforce Gap Analysis:Implications for Secondary and Higher Education Programs in the Agricultural

Sciences

Matt Baker, Texas Tech UniversityAndy Swift, Texas Tech University

Claire Henkhaus, The Wind AllianceKeith Plantier, Texas State Technical College – West Texas

Abstract

Although Texas leads the nation in wind power generation, there is a concern that an inadequately trained workforce may limit future industry expansion. Consequently, the purpose of this gap analysis was to determine the perceptions of wind energy industry leaders and academic leaders in public two-year community and technical colleges and public and independent four-year colleges and universities. Most industry leaders predict substantial short-term, off-shore development in the Gulf of Mexico and rapid onshore expansion. In terms of full-time employees, they are predicting almost a 36% increase in their Texas workforce within the next five years. Collectively, the academic leadership anticipates an expansion of current programs and the development of new programs. However, higher education will likely not be able to keep pace with the rapid industry growth. Based upon these and other findings, specific implications and recommendations were formulated related to higher education (including dual credit programming being facilitated by high school agricultural science teachers) programs in the Agricultural Sciences.

Introduction/Theoretical FrameworkTexas leads the nation in wind power generation (Perryman Group, 2010) with over 10,000MW of capacity. Iowa follows Texas power generation with less than 4MW of capacity (American Wind Energy Association, 2011). It is estimated that 10MW of installed wind power meets the needs of 300-400 average households (Swift, 2011). The American Wind Energy Association (2011) indicates that wind is the major contributor to new US power installations, with six of the seven largest wind farms located near Abilene, Texas. Wind power in Texas means jobs. In 2011, the American Wind Energy Association estimated that Texas had a workforce of over 10,000. However, in 2010 the Perryman Group estimated the total economic impact of a large-scale energy transmission project in Texas scheduled for Phase I completion in 2011, combined with wind energy manufacturing, and new and existing power generation would range from a minimum of 61,682 to a maximum of 1,259,915 jobs, controlling for the price of other fuel sources for power generation.It is clear that a key component for sustained growth in this critical energy sector is workforce development. As a field of study, Agricultural Education has a long history in career and technical education programs. In fact, one of the six research priorities of the 2011-2015 National Research Agenda (Doerfert, 2011) is focused upon the development of a scientific and professional workforce that addresses the challenges of the 21st

Century. Energy needs are explicitly referenced in this National Research Agenda priority area.High school energy workforce preparation in Texas is included in the Agricultural, Food and Natural Resources Career Cluster (R. Whitson, personal communication, November, 28 2011). Although most high school agricultural educators do not have a great deal of subject matter depth in Wind Energy, they can serve as the point of contact within their local school district to facilitate dual credit programming from regional community colleges and technical schools. In Fall, 2011, Texas State Technical College – West Texas (located in Sweetwater, TX), reported an enrollment of 23 10th grade students in their dual credit technical certificate wind energy workforce program (K. Plantier, personal communication, November 28, 2011). The subject matter is being delivered online and through short-term, intensive laboratory sessions on the Sweetwater (TX) campus. After completion of the two-year program and successful passage of the Wind Certificate I Exam, graduates are credentialed for workforce entry-level requirements as a turbine technician. Many students then pursue either a second-level certificate program or continue their education in a two-year Associates of Applied Science degree program.Consequently, it is prudent for university Agricultural Educators to inquire into workforce opportunities related to renewable energy. The theoretical framework underpinning this workforce assessment is based upon the Occupational Information Network’s Content Model (O*Net Resource Center, n.d.). This model blends together worker orientation and job orientation into a holistic workforce development framework. The worker orientation consists of three components: (1) worker characteristics which include abilities, interests, values, and work styles; (2) worker requirements such as basic skills, cross-functional skills, knowledge and education; and (3) experience requirements involving experience and training, basic entry skills for the position, basic cross-functional skills for the entry-level employee and licensing. The job orientation also includes three components: (1) occupational requirements, including general work activities, detailed work activities, organizational context and work context; (2) workforce characteristics, which include labor market information and occupational outlook, and (3) occupational-specific characteristics, including tasks, tools and technology. For this investigation, the researchers sought to determine perceptions of the current wind energy industry workforce and employment projections through 2016 (workforce characteristics). In terms of occupational requirements, work context was examined. Finally, through the lenses of both industry representatives and academic leaders in Texas, worker requirements were examined per selected knowledge and education characteristics.

Purpose and ObjectivesThe purpose of this gap analysis was to determine the perceptions of Texas wind energy industry leaders and academic leaders in public two-year community and technical colleges and public and independent four-year colleges and universities. The following research objectives were developed to guide the study: (1) describe the industry respondents based upon position title, organizational scope by industry segment, focus (on-shore/off-shore), and geographic location; (2) identify industry respondents based upon percentage of full-time wind energy workforce, educational background of current

workforce, and wind energy workforce by job title; (3) ascertain the importance of selected educational programs as perceived by industry respondents; (4) describe the higher education respondents based upon position title, geographic region, and institutional academic enrollment; (5) identify the wind energy or renewable energy academic programs that exist in higher education institutions based upon program title and type, as well as current and anticipated enrollments; and (6) ascertain the degree to which higher education institutions anticipate beginning new academic programs in Wind Energy or related fields of study.

Methods and ProceduresThe research design used in this multi-population study is described by Campbell and Stanley (1966) as pre-experimental. This design is unconcerned with correlations between variables (characteristics or attributes); meaning it limits the ability to rule out spurious causes to the phenomena that are described. It does, however, provide irrefutability in terms of describing a phenomenon at any single point in time (Ary, Jacobs, & Razavieh, 2002).

Three populations were used for this series of census studies. At the encouragement of the program sponsors within the Texas Workforce Commission, the industry population frame development began with a researcher-purchased list of (N=265) wind energy-related businesses in Texas. The vendor for the purchased directory was InfoGroup (Office of the Governor, 2011). InfoGroup utilized the following sources to compile their directory: (1) membership in the Texas Renewable Energy Industries Association; (2) membership in the Wind Coalition; and (3) vendor-specific supplied contacts. InfoGroup states: “the wind energy industry includes companies primarily engaged in manufacturing, services, utilities operation, wind farm operation, R&D and other related activities” (n.p.#). The researchers reviewed the business websites and contacted businesses by phone to acquire e-mail addresses for this population. As a result, 138 usable email addresses were validated for this subcomponent of the frame. The researchers added 381 additional addresses to the InfoGroup frame from a database obtained from The Wind Alliance, being careful to control for frame error. Of the 519 total business addresses used in the study, 100 bounced or were blocked after the initial notification, which resulted in a total research frame of 419 wind energy-related businesses.

The final two populations consisted of academic leaders in public two-year and technical colleges and four-year public and independent colleges and universities. Frames for these populations were obtained from lists maintained by the Texas Higher Education Coordinating Board (two-year N=79 and four-year N=75). The researchers developed email contacts of the chief academic officers at each institution by contacting the institutions by phone and or email to verify that the contacts were accurate.

Three questionnaires were developed by the research team members for the three populations referenced above. Face and content validity were established by use of a panel of experts unique to each audience. The industry panel consisted of representatives from the National Institute for Renewable Energy, the Wind Alliance, and Texas Tech University. The four-year panel consisted of academic leaders from the University of

Nebraska, Lincoln, Virginia Tech University, Pennsylvania State University, and Utah State University. The two-year panel consisted of community college leaders from Tulsa Community College, Clovis (NM) Community College, Chief Dull Knife College (MT), and New Mexico Junior College (Hobbs).

All data were collected using Constant Contact, a secure, online survey research service. Subjects participating in all three questionnaires were allowed to skip any particular question which that they did not care to respond to, and Constant Contact was programmed to allow the respondents to move forward to the subsequent question.

An industry pre-notice announcement was delivered on July 18, 2011 to 419 wind-energy related businesses. In an effort to fully explain the importance of the study, this message included a streamed video by members of the research team explaining the importance of the study to the future of the wind energy industry [http://www.depts.ttu.edu/uc/windenergy/workforce-assessment-training.php]. On July 19, the research team invited potential respondents with questions or concerns to participate in a conference call. It should be noted that a number of very large organizations contacted the research team, indicating that although they were supportive of the study in spirit; corporate policy prohibited them from participation in the study. The industry questionnaire was delivered on July 20. Invitation reminders were sent on July 28, August 2, and August 11. In terms of the academic leaders, a pre-announcement was sent on July 11, 2011. The questionnaires were submitted for data collection purposes on July 13. Invitation reminders were sent on July 20, July 28, and on August 2. A final reminder was sent on August 11. One of the decisions each reader will have to make is the degree to which the target populations were represented by the accessible populations (those respective individuals who responded to our questions). According to Ary, et al., (2002) generalizing from a nonrandom accessible population to a target population typically involves risk. These experts go on to state that: [t]he confidence that you can have in this step depends on the similarity of the accessible population to the target population” (p. 164). Consequently, the researchers begin the findings by profiling the accessible populations. Therefore, it is up to each reader to make his/her own determination as to whether or not the profile of the accessible populations reflects the target populations.A total of 59 useable questionnaires were returned initially from the industry representatives. However, three of the respondents indicated that their wind energy business did not currently operate in Texas, nor did they anticipate operating in Texas by 2016. Consequently, the researchers removed these three contacts from the database given the statewide focus of the study. This resulted in a 12.65% response rate by industry. A total of 32 useable questionnaires were returned from the four year college and university population, resulting in a 42.66% response rate. A total of 33 useable questionnaires were returned from the two year postsecondary college population resulting in a 41.77% response rate.

Data were analyzed using the IBM SPSS statistical package Version 19 and Microsoft Excel software. Descriptive statistics consisting of means, standard deviations, percentages and frequencies were used to describe all data.

FindingsThe initial objective of the study was to describe the industry respondents based upon position title, organizational scope by industry segment, focus (on-shore/off-shore) and geographic location. About 13% of the respondents worked in business development, followed by almost nine percent in human resources. Over 15% of the respondents identified their positions as president/owner or vice president. Almost one-half of the respondents chose not to respond to this particular question. One-fifth of the respondents indicated that their organization as a whole focused upon wind energy, while about one-third of the respondents indicated that their particular unit within a larger organization had a strictly wind energy focus.

In open-ended responses related to industry scope, a number of the respondents indicated that their primary organization focused on areas other than wind energy. Some of these respondents indicated that they were employed in multinational organizations. Others also indicated that their organizations were involved in additional energy sectors including oil and gas exploration, wave energy, coal power, hydro power, and/or nuclear power. Yet others indicated that their organizations were either more manufacturing-oriented or service-oriented (e.g. energy transport/transmission).

Respondents were asked to indicate whether or not their wind energy organization or unit within a larger organization participated in a number of industry segments. Over 40% of the representatives indicated participation in the following segments: (1) developer, owner, operator; (2) project development (resource analysis, siting, planning); (3) turbine components: manufacturing; and (4) operations and maintenance (including warranty inspection). Additionally, over 30% of the representatives identified the following sectors: (1) turbine components: new product engineering and design; (2) turbine components: transportation (land &/or marine); (3) turbine components: construction and installation; (4) energy transmission, storage, or grid integration; (5) safety, regulation, or permitting; (6) business, finance, or banking; (7) wind meteorology or atmospheric science; and (8) natural resources management or environmental impact.Most of the respondents represented organizations with an on-shore wind energy focus (51.85%), while 40.75% represented organizations with both on-and off-shore activity. About eight percent of the organizations focused on off-shore only. By 2016, two-thirds of the respondents felt that their organization would be involved in on- and off-shore activity. About 29% suggested that their organizations would keep an on-shore focus, and seven percent would focus only off-shore.

Most wind energy businesses headquartered in Texas were clustered on the I-35 (Dallas to San Antonio) and I-45 (Houston to Dallas) corridors. Twenty-five percent were located on the Gulf Coast, while 19% were located in the Prairies and Lakes region including the Dallas-Ft. Worth Metroplex. Over 13% were located in the Panhandle Plains, the region of the greatest power generation, while an additional 13% reported

their primary location as being outside of Texas. In terms of wind energy operations in the state, not surprisingly, 24% was in the Panhandle Plains and 21% on the Gulf Coast. The second research objective was to identify participating organizations based upon percentage of fulltime wind energy workforce, educational background of current workforce, and wind energy workforce by job title. In terms of full-time workforce, the respondents reported an average 116 employees with a large spread throughout the population (SD=403.34). In Texas alone, they anticipated almost 36% growth through 2016, and out-of-state they anticipated almost a 35% increase in their full-time workforce. In terms of full-time workforce segmented by educational attainment, the data reveal that there exists a positively skewed distribution with the baccalaureate degree holders representing the greatest area of the distribution (around 60%), with one-and two-year degree or certificate recipients and master’s degree recipients, roughly one standardized unit from the center, followed by high school or less and doctoral degree holders two standardized units from the center.Common position titles in the wind energy workforce (by responding ‘yes’ or ‘no’ to a list provided) were: (1) sales and marketing; (2) safety and environmental health; (3) project manager; (4) supply chain developer; (5) wind specific business analyst and planner; (6) financial analyst; (7) contract negotiator/manager; (8) O&M supervisor/manager; (9) legal and regulatory specialist; and (10) marketing wind power and wind farm specialist. The third objective was to ascertain the importance of selected educational programs. One such program involves preliminary development of a combined high school and community college program of study for dual credit. This effort is being led by a statewide team of energy-related career and technical community college faculty commissioned by the Texas Higher Education Coordinating Board and the Texas Education Agency. A common career foundation pathway consisting of 26-27 credit hours has been proposed. The industry representatives revealed that their organizations were inclined to employ workers with a traditional two-year associate degree or certificate and would also be very open to employing individuals who receive part of their education in high school as dual credit students. The respondents were asked their perceptions as to the importance of 12 common postsecondary career clusters (using a 5 point Likert-type scale with 1= Very Low Importance and 5= Very High Importance). The most important clusters were safety (M=4.09, SD=1.24), science, technology, engineering, mathematics (M=3.88, SD=1.19) and energy (M=3.88, SD=1.11). Data collected from wind energy industry representatives (using a similar Likert-type scale) shows that in terms of four-year baccalaureate degree program clusters, engineering was perceived as being most important (M=3.97, SD=1.11), followed closely by applied science and mathematics (M=3.94, SD=1.16), and business (M=3.78, SD=1.10).The industry representatives were asked their perceived importance of four general industry knowledge areas/educational experiences related to wind energy (using a similar 5-point Likert-type scale). The most important was knowledge/experience in the energy industry (M=3.81, SD=1.06), followed by knowledge/experience in the utility industry (M=3.59, SD=1.01). Internship experiences specific to wind energy were also valued (M=3.31, SD=1.28).

The respondents were also asked the importance of 19 “soft skills” often referenced in the higher education literature. Although all 19 were perceived as being moderately important on a continuum (using a 5-point Likert-type scale), the most important soft skills were: (1) ethics (M=4.47, SD=0.84), (2) verbal communication (M=4.39, SD=0.88), (3) written communication (M=4.34, SD=0.90), and (4) the ability to work in a team (M=4.31, SD=0.90).

The fourth research objective was to describe the higher education respondents based upon position title, geographic region and current academic enrollment. In terms of four-year institutions, almost 60% of the respondents served as provost and/or as vice presidents for academic affairs. These individuals are typically the chief academic officers at an institution. Almost 13% did not identify their title, while a little over 9% served as associate or assistant provosts or vice presidents. In terms of two-year institutions, almost 64% of the respondents served as vice president or associate vice president. Over 12% of the respondents served as dean, while an additional 12% did not identify their titles.

Of the four-year respondents, over 70% were located in the Panhandle/Plains, the Prairies and Lakes, and the Gulf Coast regions. About two-thirds of the respondents represented public universities and one-third private universities. In Fall 2010, the public universities participating in the study enrolled 399,140 of the 557,500 students in Texas, with almost 60% being classified as research or emerging research universities. The doctoral university classification represented another 26% of the participating public institutions.

As for the two-year respondents, most represented colleges or college systems in the Panhandle Plains region, the Prairies and Lakes region, and the Gulf Coast region of Texas. Responding institutions enrolled a little over one-half of the 743,000 total students enrolled in two-year institutions in Texas, with very large colleges (a classification of the Texas Higher Education Coordinating Board) representing over two-thirds of the institutions.

The fifth research objective was to identify the current wind energy or renewable energy academic programs that exist in higher education based upon program title and type, as well as current and future enrollments. In an effort to baseline capacity in the state, the researchers compiled a list of ABET-accredited engineering undergraduate degree programs at the institution level. Over 17% of the accredited undergraduate engineering programs are in electrical engineering, followed closely by mechanical engineering (16%), and computer and software engineering (11.54%). The university representatives were asked to identify non-engineering programs in wind energy or renewable programs with a significant wind energy component. The data revealed that seven programs exist in the state, ranging from an undergraduate minor to a Ph.D.. The programs are all unique per the institutions and collectively enroll 105 students. In five years, the programs anticipate enrolling 350 students, representing a 335% enrollment increase.

Over one-third of the two-year respondents indicated that their institutions had some type of wind energy or renewable energy program. For baseline purposes, data pertaining to

common USDE CIP codes and programs statewide were examined, as reported by the Texas Higher Education Coordinating Board. Of the five most commonly used program titles (General Engineering, Electromechanical Technology/Electromechanical Engineering Technology, Electrical and Power Transmission Installers, Electrical, Electronics, and Communications Engineering, Solar Energy Technology/Technician), almost 80% of the enrollment was in general engineering. An additional 12% of the enrollment was in electromechanical technology/electromechanical engineering technology.

The differing types of wind energy and/or renewable energy-related programs reported by respondents ranged from Alternative/Renewable Energy programs, to Wind Energy programs. Collectively they can be classified as A.A.S. programs and certificate programs. Many reported anticipated declines in enrollments during the next five years. This may be a result of the poor economy and decreases in state support for public community college programs. Many programs were new or recently added; consequently some of the academic leaders did not feel comfortable in projecting enrollments within the next five years. A few programs, however, anticipated significant enrollment growth.

The sixth research objective was to ascertain the degree to which higher education programs anticipate beginning new academic programs in wind energy, identifying the new programs by title and type. Of the participating four-year colleges and universities, over 68% indicated that they anticipate offering a wind energy or renewable energy program by 2016. Two of the respondents indicated an interest in beginning a degree program in Electrical Engineering, while four of the respondents indicated an interest in a Renewable Energy degree. At least two undergraduate concentrations were mentioned, one related to business administration and one to environmental science. One university expressed an interest in launching a master’s degree in wind energy.

In terms of two-year institutions, almost 83% of the respondents perceived that their colleges would be launching new wind or renewable energy programs within the next five years. Most of those responding positively to this question did not feel comfortable at this point in time in identifying the program by title or type, until further along in the program development process.

Conclusions/Implications

Wind energy is a multidisciplianry field of study ranging from research and development to component manufacturing of towers, turbines, and blades. There are also natural resource issues related to environmental impact, safety, regulation, and permitting, as well as financial considerations. It is important that students understand the breadth of the industry. In terms of organizational scope, in larger energy-related organizations wind energy is one of a number of energy sectors. Consequently, students need to learn how to work successfully in multidimensional organizations with many common and/or competing interests ranging from manufacturing turbine components, to generation, transmission, storage and/or grid integration, to consumption.

Two-thirds of the industry representatives perceived that off-shore generation is coming to the Gulf Coast Region by 2016. This finding is supportive of a Gulf Coast study by The Wind Alliance (n.d.). Curriculum designers can learn best practices from large scale off-shore generation in Europe’s North Sea. It is also likely that many practices used in Gulf Coast off-shore petroleum production may be transferrable to off-shore wind energy generation, particularly in areas of worker transport, worker safety, construction, and hurricane mitigation.

Around 60% of the current wind energy workforce in Texas has earned a baccalaureate degree. However, at many institutions career and technical courses are not transferrable into B.S. programs. There are at least two solutions to this transfer issue. First, universities can develop a series of credit by exams and study guides in an effort to grant and transcript academic credit. A second consideration is offering applied baccalaureate degrees that allow for the transfer of career and technical course credit and/or blocks of credit earned as a result of prior learning experience (on-the-job). In Texas, at least one Agricultural Education program is the administrative home to an applied baccalaureate degree (D. Ulrich, personal communication, December 12, 2011). Another similar degree that the researchers are familiar with is administered by a department with an Agricultural Education program is at Utah State University (B. Miller, personal communication, December 12, 2011).

Wind energy representatives were supportive of dual credit programming enrolling upper division high school students into college-level wind energy courses. Dual credit programs in Texas qualify for the “double-dipping” of funding (R. Whitson, personal communication, November 28, 2011). Public schools offering such programs are awarded full formula funding, as are the higher education institutions offering the college-level credit (usually two-year institutions).

At the baccalaureate level, engineering was considered the most important program cluster. However, majors in the applied science and mathematics fields were perceived as being almost as important. One university in Texas has developed a Bachelor’s of Science in Wind Energy (BSWE) degree (Swift, 2011). This program is multidisciplinary and includes coursework in atmospheric science and wind forecasting/prediction, wind farm development, wind farm finance, the human dimension of wind energy, and environmental impacts of wind energy. Other universities in the state should consider applied or multidisciplinary science and mathematics programs similar to the BSWE, in an effort to prepare the workforce for a rapidly growing industry.

In terms of soft skills, industry respondents identified ethics, verbal and written communications, and teamwork as being important. These findings are consistent with the recent findings in the Agricultural Sciences (Crawford, Lang, Fink, Dalton, & Fielitz, 2011). However, research in the agricultural context revealed that student and faculty perceptions of soft skills often differ from industry perceptions.

Over 17% of the accredited undergraduate engineering programs were in electrical engineering, followed closely by mechanical engineering (16%), and computer and

software engineering (11.54%). According to the U.S. Bureau of Labor Statistics (n.d.), overall engineering employment is good, with an anticipated growth demand at the national level through 2018 at 11%. However, determining the short-term capacity of existing engineering programs in Texas was beyond the scope of this study.Seven non-engineering baccalaureate- or graduate-level wind energy programs were reported. Academic leaders anticipate a 335% enrollment increase by 2016. However, even by achieving enrollment targets the number of workforce-ready graduates will likely not keep pace with anticipated industry growth and demand. Two-Year academic leaders with existing programs were not optimistic about growth in the wind energy sector. This may be a result of difficulties they were facing at the time of data collection related to across-the-board decreases in state funding. Collectively, the two-year academic leadership was more bullish on launching future programs for the wind energy workforce. However, they were unwilling to speculate on program titles until further stakeholder input was obtained.The four-year leadership had given greater thought to the types of degrees that they might offer than the two-year leadership. It is likely that four-year academic leaders were more keenly aware of the growth of the industry due to their research missions and related funding opportunities for R&D in this critical energy sector.

Recommendations1. The higher education wind energy curriculum should provide students with a

foundational understanding of the breath of the wind energy industry. 2. Wind energy programs in higher education must include content related to the

uniqueness of working in large, multinational business organizations. Coursework in cross-cultural communications and foreign language acquisition are important components for working in such environments.

3. Higher education program graduates must develop an appreciation of how wind energy integrates into the greater utility sector, as well as how renewable energy is affected by societal acceptance, production and market value of other energy sectors providing power for electrical generation.

4. With the anticipated off-shore generation in the Gulf coast region, the on-shore higher education curriculum must be redesigned to include the complexities of operations in a marine environment.

5. Universities should explore sustainable financial models for developing and maintaining credit by exams in the context of workforce (career and technical training). In terms of Engineering programs, ABET should be involved to ensure accreditation guidelines are followed.

6. Universities should also consider whether or not awarding applied or multidisciplinary baccalaureate degrees are within their missions, and if so, in which academic colleges they best fit. Agricultural deans with faculty expertise in applied economics, applied agricultural mechanical systems, natural resources management, and/or landscape architecture should consider whether this expertise should and could be leveraged into an academic home for such programs.

7. High school agricultural science teachers in Texas should consider serving as the school district point of contact for students seeking dual credit programs for entry into energy fields. The state Agricultural Education leadership should propose a revenue model to share with local superintendents that would allow the funding to flow back into the Agricultural Science program budget for providing this administrative service.

8. Stakeholders (academic, industry, and state or federal departments/agencies) should plan and participate in an annual Higher Education Wind Energy Workforce Summit for the purpose of encouraging the growth of workforce and academic programs.

9. The United State Department of Energy should develop a Higher Education Challenge Grant Program, similar to the USDA program, for the purpose of encouraging growth and innovation in higher education programming.

10. A comparative analysis of soft skills should be conducted to ascertain similarities and differences that students and/or faculty may hold in relationship to industry. The curriculum should be vetted to ensure that essential soft skills are included.

11. Texas engineering programs need to be assessed to determine future capacity as it pertains to developing a workforce for the wind energy.

12. The wind energy industry should be proactive in providing scholarship funding to support students and programs.

ReferencesAry, D., Jacobs, L.C., & Razavieh, A. (2002). Introduction to research in education (6th

ed.). Belmont, CA: Wadsworth/Thompson Learning.American Wind Energy Association. (2011). US wind energy year end 2010 market

report. Retrieved from: http://www.awea.org/learnabout/publications/upload/4Q10-market-outlook-public.pdf

Campbell, D.T., & Stanley, J.C. (1966). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally.

Crawford, P., Lang, S., Fink, W., Dalton, R., & Fielitz, L. (2011). Comparative analysis of soft skills: What is important for new graduates? Retrieved from: http://www.aplu.org/document.doc?id=3414

Doerfert, D.L. (Ed.). (2011). National research agenda: American Association for Agricultural Education’s research priority areas for 2011-2015. Lubbock, TX: Texas Tech University, Department of Agricultural Education and Communications.

O*Net Resource Center. (n.d.). The O*Net content model. Retrieved from: http://www.onetcenter.org/content/html/4.D

Office of the Governor. (2011). Texas wind energy industry company directory. Omaha, NE: InfoGroup.

Perryman Group. (2010). Winds of prosperity: The impact of the competitive renewable energy

zone (CREZ) investment in transmission infrastructure and the potential impacts on renewable generation, electricity cost savings, and economic development. Retrieved from: http://www.lmci.state.tx.us/shared/PDFs/Green_Collar_Workers2.pdf

Swift, A. (2011). Workforce for the wind industry. Committee on emerging workforce trends in the US energy and mining industries, Washington, D.C.: Keck Center of the National Academies.

The Wind Alliance. (n.d.). A comprehensive R&D plan to accelerate industrial off-shore wind development. Houston, TX: The Wind Alliance. Retrieved from: http://thewindalliance.org/Documents/OffshoreWind-GulfofMexico-The-Wind-Alliance.pdf

U.S. Bureau of Labor Statistics.(n.d.). Occupational outlook handbook (2010-2011 ed.). Retrieved from: http://www.bls.gov/oco/ocos027.htm