Students characteristics, self-regulated learning...

Students’ characteristics, self-regulated learning, technologyself-efficacy, and course outcomes in online learning

Chih-Hsuan Wang*, David M. Shannon, and Margaret E. Ross

Department of Educational Foundations, Leadership, and Technology, Auburn University,Haley Center, Auburn, Alabama, USA

(Received 28 September 2012; final version received 31 May 2013)

The purpose of this study was to examine the relationship among students’characteristics, self-regulated learning, technology self-efficacy, and course out-comes in online learning settings. Two hundred and fifty-six students participatedin this study. All participants completed an online survey that included demo-graphic information, the modified motivation strategies learning questionnaire,the online technology self-efficacy scale, the course satisfaction questionnaire,and the final grades. The researchers used structural equation modeling toexamine relationships among student characteristics, self-regulated learning,technology self-efficacy, and course outcomes. Based on the results from thefinal model, students with previous online learning experiences tended to havemore effective learning strategies when taking online courses, and hence, hadhigher levels of motivation in their online courses. In addition, when studentshad higher levels of motivation in their online courses, their levels of technologyself-efficacy and course satisfaction increased. Finally, students with higher lev-els of technology self-efficacy and course satisfaction also earned better finalgrades. Based on the findings, we recommend that instructors design courses ina way that can promote students’ self-regulated learning behaviors in onlinelearning settings and that students in online classes, as in traditional classes, setaside a regular time to concentrate on the course. Also, institutions shouldprovide user-friendly online learning platforms and workshops for instructorsand students to facilitate the teaching and learning experiences.

Keywords: self-regulated learning; technology self-efficacy; course satisfaction;achievement

Distance education is an educational mode in which the students are physicallyseparated from the instructors and the institutions (Schlosser & Anderson, 1994).Because of this separation, there are many course delivery options in distanceeducation. As early as the 1800s, correspondence courses were commonly used asthe course delivery method in distance education. In the 1920s, distance educationcourses were delivered via radio. Starting from the early 1930s, they began to bedelivered as television programs. In 1993, Graziadie introduced an online computer-delivered lecture and, with the help of computer programs, allowed students and theinstructors to use computers as virtual classroom settings. This was considered to be

*Corresponding author. Email: [email protected]

© 2013 Open and Distance Learning Association of Australia, Inc.

Distance Education, 2013Vol. 34, No. 3, 302–323, http://dx.doi.org/10.1080/01587919.2013.835779

the beginning of online learning, and web-based courses were starting to beconsidered as one of the course delivery options in distance education (Bourne,1998).

The number of students enrolled in online courses has increased rapidly since1990 (Arbaugh & Duray, 2002; Armstrong, 2011; Lim, Yoon, & Morris, 2006).Based on the National Center for Education Statistics, during the 2000–2001 aca-demic year, there were 2876,000 students enrolled in the distance courses (Waits,Lewis, & Greene, 2003). By the 2006–2007 academic year, 12.2 million studentshad enrolled in distance courses (Parsad, Lewis, & Tice, 2008), while in 200, almost18 million students were taking online courses (Armstrong, 2011). As these enrol-ments continue to increase, the quality of course outcomes and student learningexperiences becomes a critical issue that needs to be addressed. The current studyfocused on the relationship among students’ characteristics, self-regulated learning,technology self-efficacy, and course outcomes.

Course outcomes

Lim et al. (2006) asserted that course outcomes can be an index for evaluating theoverall quality of distance learning programs. Course outcomes include both cogni-tive and affective variables (Paechter, Maier, & Macher, 2010). Of the cognitivevariables, learning achievement is most important, whereas course satisfaction is themost important affective variable (Lim et al., 2006; Paechter et al., 2010). Previousresearch suggested that students’ satisfaction in the online courses was correlatedwith persistence and dropout rates in online learning (Arbaugh, 2000; Billings,2000; Levy, 2007; Thurmond, Wambach, Connors, & Frey, 2002). It is also a keycomponent which leads students to success in learning (American PsychologicalAssociation, 1997; Biner, Dean, & Mellinger, 1994; Chang & Smith, 2008; Marks,Sibley, & Arbaugh, 2005). When students are more satisfied in their online course,they tend to earn higher grades (Puzziferro, 2008). Therefore, an online course issuccessful when students are satisfied with their learning experience (Marks et al.,2005) and students experience success in learning the course content (Chang &Smith, 2008; Marks et al., 2005; Puzziferro, 2008).

Self-regulated learning as a mediator

Online learning is very different from conventional learning. Students in online learn-ing settings do not physically present themselves in a classroom and do not have theopportunity to interact face-to-face with their instructors and classmates. Students inonline courses are responsible for their own learning as they decide when, where, andhow long to access the learning materials (McMahon & Oliver, 2001). Therefore,self-regulated learning behaviors are especially important when taking online courses(Wijekumar, Ferguson, & Wagoner, 2006). Pintrich and Zusho (2002) definedself-regulated learning as an active and constructive process that involves thestudents’ active, goal-directed, self-control of behaviors, motivation, and cognitionfor academic tasks (Pintrich, 1995). Students set goals for their learning, use manycognitive and metacognitive strategies to monitor, control, regulate, and adjust theirlearning to reach these goals (Pintrich, 1995, 1999; Pintrich, & Zusho, 2002).

Pintrich (2004) pointed out that self-regulatory activities mediate the relationshipbetween personal and contextual characteristics and actual achievement or

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performance. Previous researchers have tried to link the relationship betweenpersonal characteristics and self-regulated learning, and the relationship betweenself-regulated learning and course outcomes. However, there has been no researchthat examined these variables simultaneously. Research has been limited to the typesof self-regulated learning strategies in web-based courses (Whipp & Chiarelli, 2004;Yukselturk & Bulut, 2007). In addition, motivations and the use of self-regulatedstrategies have been positively associated with students’ performance in and satisfac-tion with online courses (Artino, 2009; Artino & McCoach, 2008; Paechter et al.,2010; Puzziferro, 2008; Yukselturk & Bulut, 2007). Although research results indi-cated that there were statistically significant relationships between self-regulatedlearning and course outcomes, research results addressing the relationship betweenpersonal characteristics and self-regulated learning were not consistent. Someresearch results indicated there was no statistically significant relationship betweenpersonal characteristics and self-regulated learning (Yukselturk & Bulut, 2009),while other research indicated that there were statistically significant relationships(Lim et al., 2006).

Relationship between technology self-efficacy and course outcomes

Self-efficacy is a key competence belief in self-regulatory control processes (Schunk& Zimmerman, 2006). Bandura (1995) defined perceived self-efficacy as “the beliefsin one’s capabilities to organize and execute the courses of action required to man-age prospective situation” (p. 2). In other words, self-efficacy is the belief of thecapabilities of what one can do in a specific domain. Self-efficacy impacts taskchoice, effort, persistence, and achievement. It also influences academic motivations,learning, and achievement (Schunk & Pajares, 2002). From this point of view,students with positive self-efficacy toward learning in online courses are usuallymore motivated and perform better in these courses.

In addition to self-efficacy in the specific online course, the skills of using onlinelearning technologies are also important. These skills include, for example, the useof emails, discussion boards, and Internet searches. Students who fear computertechnologies may experience confusion, anxiety, a loss of personal control, frustra-tion, and withdrawal (Bates & Khasawneh, 2004). However, previous researchershave found conflicting results regarding the relationship between technologyself-efficacy and students’ performance and satisfaction with online courses. WhileDeTure (2004) and Puzziferro (2008) indicated that technology self-efficacy was apoor predictor of the final grade and satisfaction in online courses, other researchershave reported that technology self-efficacy is positively correlated with onlinelearning performance (Joo, Bong, & Choi, 2000; Wang & Newlin, 2002).

Students’ characteristics, motivation, and course outcomes

In addition to self-regulation and self-efficacy, some researchers have tried to estab-lish the relationship between students’ characteristics and previous online learningexperience, and their satisfaction and performance in online learning settings (Markset al., 2005; Sanders & Morrison-Shetlar, 2001; Thurmond et al., 2002). However,they found these variables do not consistently predict students’ performance andsatisfaction toward their online learning experiences. While Thurmond et al. (2002)found that the number of online courses the students has completed was positively

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correlated with their course satisfaction, it failed to reach statistical significance as apredictor. Sanders and Morrison-Shetlar (2002) investigated the impact of genderand age, reporting that females had more positive attitudes toward web-basedcourses than males and younger students had more positive attitudes in onlinecourses than older students. On the other hand, Marks et al. (2005), Yukselturk andBulut (2007), and Yukselturk (2009) reported that age, gender, educational level,and previous number of online courses taken do not statistically significantly predictthe current online course satisfaction or students’ achievement.

Finally, researchers have examined the relationships among students’ characteris-tics and motivation, and technology self-efficacy and have reported mixed findings.Busch (1995) and Imhof, Vollmeyer, and Beierlein (2007) found that there are nogender differences in college students in their perceived self-efficacy in usingcomputers; and Yukselturk and Bulut (2009) reported no gender differences in self-efficacy, self-regulated learning, and achievement. Conversely, Brown et al. (2003)found that males reported higher levels of technology self-efficacy than females, butfemales reported more academic self-efficacy than males. As for previous onlinelearning experiences, Lim et al. (2006) reported that students with previous distancelearning experience demonstrated higher levels of learning motivation andself-efficacy. In addition, Bates and Khasawneh (2004) indicated that both the train-ing provided by instructors to the students and the previous experience with onlinelearning technologies reduce the anxiety in online learning technologies and increaseonline learning technologies self-efficacy. Furthermore, online learning technologiesself-efficacy is positively related to students’ motivation to use online learningtechnologies.

In summary, researchers have provided conflicting evidence regarding the rela-tionship among students’ characteristics and previous experience in online learn-ing, self-regulated learning, technology self-efficacy, course satisfaction, andperformance. In addition, there is no prior research that has examined thesevariables simultaneously. Therefore, the current study was designed to examine ahypothesized model (Figure 1), based on previous empirical studies. More specifi-cally, the current study used structural equation modelling (SEM) to determine therelationship among students’ characteristics and previous experience in onlinelearning, self-regulated learning, technology self-efficacy, course satisfaction, andperformance.

Figure 1 provides an illustration of the relationships that was examined in thisstudy. Student characteristics include gender, educational level, and the numbers ofprevious online courses the students have taken, while course outcomes include finalgrades and course satisfaction. Based on prior research, the current study hypothe-sized that students’ characteristics would be related to their level of self-regulatedlearning, level of technology self-efficacy, and course outcomes. It also hypothesizedthat self-regulated learning and technology self-efficacy would relate to course out-comes. In addition, self-regulated learning and technology self-efficacy wereexpected to interact with each other, and serve as mediators between students’characteristics and course outcomes.

The purpose of the current research was to determine the relationship amongstudents’ characteristics, technology self-efficacy, self-regulated learning, and courseoutcomes. More specifically, the current study examines the predictors of courseoutcomes in the hypothesized model. The research hypotheses are:


(1) Students’ gender, education level, previous experience in online learning,self-regulated learning (motivation and learning strategies), and technologyself-efficacy predict course outcomes (achievement and course satisfaction)in online learning settings based on the hypothesized model.

(2) Students’ levels of motivation, learning strategies, and technologyself-efficacy in online learning settings are different based on their gender,educational level, and previous experience in online learning.

(3) (A) With higher levels of motivation and more effective learning strategies,students have higher levels of achievement and course satisfaction in onlinelearning settings.(B) With higher levels of technology self-efficacy, students have higherlevels of achievement and course satisfaction in online learning settings.(C) Students’ motivation, learning strategies, and technology self-efficacyinteract.

(4) Students’ motivation, learning strategies, and technology self-efficacy are themediators between students’ gender, educational level, previous experiencein online learning, achievement, and course satisfaction.

In the current research, the exogenous variables are gender (male or female),educational level (undergraduate or graduate), and number of previous onlinecourses taken. The endogenous variables are technology self-efficacy, motivation,learning strategies, final grade of the most recent online course, and coursesatisfaction.

GFI = .90,CFI = .89,NFI=.85, RMSEA=.09

x2/df = 3.14,

Figure 1. Results for hypothesized model. *Coefficient is significant at the 0.05 level(2-tailed). **Coefficient is significant at the 0.01 level (2-tailed). ***Coefficient is significantat the 0.001 level (two-tailed).


Method

Participants

An exhaustive sample of 2139 undergraduate and graduate students at a southeasternuniversity was invited to participate in the current study. These students were identi-fied in taking online courses during Fall 2008, Spring 2009, Summer 2009, and Fall2009. Among these invitation emails, only 2124 emails were successfully sent out.Two hundred and fifty-six completed surveys were returned, with the response rateat 12.05%. The returned responses comprised 121 males (47.30%) and 135 females(53.10%), of whom 95 (37.11%) of them were graduate students and 161 (62.89%)were undergraduate students. Goodness-of-fit Chi-square test results indicate that thegender distribution of the returned responses was similar to the overall target popula-tion (χ2(1) = 1.27, p = .26), but undergraduate students were more willing tocomplete the survey than graduate students (χ2(1) = 30.48, p < .001). Most of theresponding students were enrolled in the College of Business (32.81%), Education(26.95%), and Engineering (18.36%). Other colleges in which students wereenrolled included Liberal Arts (9.77%), Human Science (3.90%), Science andMathematics College (3.13%), Agriculture (2.74%), and other colleges (2.34%).

Procedures

The current study used an electronic survey hosted on SurveyMonkey.com. Theparticipants’ email addresses were obtained from the rosters of online courses taughtduring Fall 2008, Spring 2009, and Summer 2009 with the permission of the Officeof Institutional Assessment. Participants received the research invitations throughthe university email system. The invitation email included a link to access the onlinesurvey website. Participants received a friendly pre-notification via email a weekbefore the formal survey, and three friendly reminders each week after theparticipants received the formal survey. All participants were at least 19 years old.All data were collected anonymously. No names or other identifying informationwas captured. To address potential order effects and eliminate systematic error, aLatin square design was used to generate seven different forms of the instrumentwith the same items in different order. These forms were randomly assigned topotential participants.

Measures

Demographic questionnaire, course satisfaction questionnaire (CSQ), modifiedmotivation strategies for learning questionnaire (modified MSLQ), and onlinetechnology self-efficacy scale (OTSES) are the instruments used in the current study.

CSQ

CSQ consisted of 21 self-report items to measure students’ overall satisfaction withthe online courses (Frey, Yankelov, & Faul, 2003). Students responded to each itemusing a seven-point Likert-type scale, ranging from completely dissatisfied (1) tocompletely satisfied (7) with a possible score range of 21–147. Higher scores indi-cated a higher level of satisfaction toward the online courses. Frey et al. (2003)reported an internal consistency Cronbach’s alpha of .97. Cronbach’s alpha for thecurrent sample was also estimated at .97 (Table 1).


Modified MSLQ

The MLSQ (Pintrich, Smith, Garcia, & McKeachie, 1993) has been used in previousstudies. It included two subscales, motivation and learning strategies. The develop-ment of the motivation subscale was based on a general social-cognitive model ofmotivation, whereas the learning strategies subscale was based on a general cogni-tive model of learning and information processing (Pintrich et al., 1993). Hence,there are two different constructs when measuring self-regulated learning by usingthe MSLQ. However, it is a measure of self-regulated learning for the conventionalclasses, not online classes.

In order to make the MSLQ more applicable, Artino and McCoach (2008)modified the original MSLQ to measure self-regulated learning in online learningsettings. The modified MSLQ includes two major subscales: motivation (task value,self-efficacy, and test anxiety), and learning strategies (elaboration, critical thinking,metacognitive self-regulation, and time/study environmental management). Themotivation section consists of 19 items and the learning strategies section includes31 items. Participants responded to each item using a seven-point Likert-type scale,ranging from not at all true of me (1) to very true of me (7). Higher scores indicatehigher level of motivation and use of appropriate learning strategies. Exploratoryfactor analysis results in the current study indicated that all items fell into the samefactor structure as the original research (Table 2).

Artino and McCoach (2008) reported Cronbach’s alphas for task value,self-efficacy, and test anxiety subscales were .90, .93, and .80, respectively. Forelaboration, critical thinking, metacognitive self-regulation, and time/study environ-mental management, the alpha coefficients were .75, .80, .79, and .76, respectively.In the current study, the Cronbach’s alphas for task value, self-efficacy, and test

Table 1. Course satisfaction questionnaire (CSQ).

Item Item

1 The amount of interaction between you and your instructor2 The quality of interaction between you and your instructor3 The cooperation between you and your classmates4 The manner in which the syllabus was distributed5 The logical organization of the course content6 The reminders given to you about assignments due7 The manner in which guidelines were given on the completion of assignments8 The lecture notes provided to you9 The extra learning resources provided to you (e.g., extra handouts, online resources,

list of frequently asked questions, online discussion groups, online weekly quizzes)10 The format of the different assignments11 The learning value of the assignments12 The options available to you to hand in assignments13 The time it took for your instructor to provide feedback on graded assignments14 The quality of the feedback provided on graded assignments15 Access to your grades during the semester16 The teaching style of your instructor17 The assistance given by the instructor in completing the course successfully18 The instructor in terms of his devotion to the course19 The accommodation of your approach to learning in the way this course was taught20 The increase in your knowledge and/or skills as a result of this course21 The increase in your confidence in using the knowledge and/or skills as a result of this

course


Table 2. Modified motivation strategies for learning questionnaire (Modified MSLQ).

Item Item

MotivationSelf-efficacy1 I believe I will receive an excellent grade in this class4 I’m certain I can understand the most difficult material presented in the readings for

this course7 I’m confident I can learn the basic concepts taught in this course9 I’m confident I can understand the most complex material presented by the instructor

in this course12 I’m confident I can do an excellent job on the assignments in this course.13 I expect to do well in this class18 I’m certain I can master the skills being taught in this class19 Considering the difficulty of this course, the teacher, and my skills, I think I will do

well in this classTest anxiety3 When I take a test I think about how poorly I am doing compared with other students5 When I take a test I think about items on other parts of the test I can’t answer8 When I take tests I think of the consequences of failing11 I have an uneasy, upset feeling when I take an exam17 I feel my heart beating fast when I take an examTask value2 I think I will be able to use what I learn in this course in other courses6 It is important for me to learn the course material in this class10 I am very interested in the content area of this course14 I think the course material in this class is useful for me to learn15 I like the subject matter of this course16 Understanding the subject matter of this course is very important to me

Learning strategiesElaboration5 When I become confused about something I’m reading for this class, I go back and

try to figure it out11 When I study for this class, I pull together information from different sources, such as

readings, online discussions, and my prior knowledge of the subject17 I try to relate ideas in this subject to those in other courses whenever possible18 When reading for this class, I try to relate the material to what I already know22 I try to understand the material in this class by making connections between the

readings and the concepts from the online activities25 I log in to Blackboard/WebCT for this class regularly26 When studying for this course I try to determine which concepts I don’t understand

well31 I try to apply ideas from course readings in other class activities such as online

discussionsTime management2 I usually study in a place where I can concentrate on my course work6 I make good use of my study time for this course10r I find it hard to stick to a study schedule19 I have a regular place set aside for studying23 I make sure that I keep up with the weekly readings and assignments for this course28r I often find that I don’t spend very much time on this course because of other

activities30r I rarely find time to review my notes or readingsMetacognitive and self-regulation3 When reading for this course, I make up questions to help focus my reading.7 If course readings are difficult to understand, I change the way I read the material

(Continued )


anxiety subscales were .95, .95, and .85, respectively. For elaboration, critical think-ing, metacognitive self-regulation, and time management alpha coefficients were.87, .84, .81, and .82, respectively.

OTSES

OTSES consists of 29 items to measure technology self-efficacy of students whoenrolled in online courses (Miltiadou & Yu, 2000). Participants responded to eachitem using a four-point scale (from 1=not confident at all to 4=very confident). Thehigher score represents the higher level of self-efficacy. Cronbach’s alpha in theoriginal study was .95. After exploratory factor analysis, two factors, the generaltechnology self-efficacy with 17 items, and the online learning platform technologyself-efficacy with 12 items, were retained in the current study (Table 3), andCronbach’s alphas were .96 and .94, respectively.

Demographic questionnaire

The demographic questions include age, sex, education level (undergraduate orgraduate), number of online courses the students have taken, and the grade for themost recent online course. The grade for the most recent online course is coded asA = 4, B = 3, C = 2, and D = 1.

Statistical analyses

The Statistical Package for Social Science (SPSS) 18.0 and AMOS 18.0 were usedas the statistical software to analyze the data. More specifically, a covariance-based

Table 2. (Continued).

Item Item

12 Before I study new course material thoroughly, I often skim it to see how it isorganized

13 I ask myself questions to make sure I understand the material I have been studying inthis class

14 I try to change the way I study in order to fit the course requirements and theinstructional methods used in this class

16 I try to think through a topic and decide what I am supposed to learn from it ratherthan just reading it over when studying for this course

21 When I study for this course, I write brief summaries of the main ideas from thereadings and online discussions

27 When I study for this class, I set goals for myself in order to direct my activities ineach study

Critical thinking4 I often find myself questioning things I hear or read in this course to decide if I find

them convincing8 When a theory, interpretation, or conclusion is presented in the online discussions or

in the readings, I try to decide if there is good supporting evidence9 I treat the course material as a starting point and try to develop my own ideas about it20 I try to play around with ideas of my own related to what I am learning in this course24 Whenever I read an assertion or conclusion in this class, I think about possible

alternatives


SEM with maximum likelihood estimation was used to examine the hypothesizedmodel and answer the research questions. SEM is a multivariate technique. Theadvantages of using SEM in the current study include (a) it can examine the rela-tionship among multiple latent variables, and construct the relationships amonglatent variables and manifest variables at the same time (Gall, Gall, & Borg, 2003;Hair, Black, Babin, Anderson, & Tatham, 2006); (b) when the relationships amongfactors are examined simultaneously, the measurement error is also estimated andminimized; and (c) it can be used to examine the mediator processes (Tabachnick &Fidell, 2007).

The SEM uses model fit indices to evaluate the fit of the model. The fit indicesinclude goodness-of-fit test (Chi-square statistic), goodness-of-fit index (GFI),

Table 3. Online technologies self-efficacy scale (OTSES).

Item Item

General technology1 Opening a Web browser (e.g., Netscape or Explorer)2 Reading text from a Web site3 Clicking on a link to visit a specific Web site4 Accessing a specific Web site by typing the address (URL)5 Bookmarking a Web site6 Printing a Web site7 Conducting an Internet search using one or more keywords8 Downloading (saving) an image from a Web site to a disk9 Coping a block of text from a Web site and pasting it to a document in a word

processor14 Loading on and off an e-mail system15 Sending an e-mail message to a specific person (one-to-one interaction)16 Sending one e-mail message to more than one person at the same time (one-to-many

interaction)17 Replying to an e-mail message18 Forwarding an e-mail message19 Deleting messages received via e-mail21 Saving a file attached to an e-mail message to a local disk and then viewing the

contents of that file22 Attaching a file (image or text) to an e-mail message and then sending it offOnline learning platform technology10 Providing a nickname within a synchronous chat system (if necessary)11 Reading messages from one or more members of the synchronous chat system12 Answering a message or providing my own message in a synchronous chat system

(one-to-many interaction)13 Interacting privately with one member of the synchronous chat system (one-to-one

interaction)20 Creating an address book23 Signing on and off an asynchronous conferencing system24 Posting a new message to an synchronous conferencing system (creating a new

thread)25 Reading a message posted on an asynchronous conferencing system26 Replying to a message posted on an asynchronous conferencing system so that all

members can view it27 Replying to a message posted on an asynchronous conferencing system so that only

one member can view it (reply to sender)28 Downloading (saving) a file from an asynchronous conferencing system to a local disk29 Uploading (sending) a file to an asynchronous conferencing system


comparative fit index (CFI), normed fit index (NFI), and root mean square error ofapproximation (RMSEA). The researchers expected a good model fit; therefore, anonstatistical significance in Chi-square test is preferred. However, the Chi-squaretest is very sensitive to sample size. A larger sample size usually leads to statisticalsignificance very easily. Hence, the researchers need to take other fit indices intoconsideration. The general rule of thumb for the cut-off value of fit measures is .90for GFI, CFI, and NFI and .1 for RMSEA (Meyers, Gamst, & Guarino, 2006).

If the model is not a good fit, there are two ways to improve it: adding pathsbased on the modification indices or deleting nonsignificant paths based on trimmingprocess (Hox & Bechger, 1998; Kline, 2010; Ullman & Bentler, 2013). Theresearchers need to conduct a sequence of model modification, add or remove onepath at a time, and re-evaluate the model until they reach an adequate fit (Hox &Bechger, 1998).

Results

Table 4 summarizes descriptive statistics for all scales. The researchers computedthe bivariate Pearson correlation coefficients to investigate the linearity between theindicator variables and their latent variables, and among the latent variables. Thecorrelation coefficients between each indicator and its latent variable ranged from.40 to .93, indicating that the linearity assumption between indicator and latentvariables was not violated. In addition, the correlation coefficients among latentvariables ranged from .29 to .66, indicating that there was a linear relationshipamong latent variables.

Hypothesized model

The initial results for the hypothesized model indicated an unacceptable fit betweenthe hypothesis model and the observed data (Figure 1 and Table 5). The Chi-squaretest was statistically significant (χ2 = 197.94, df = 69, and p < .001), and the GFI,the CFI, and the NFI values were .90, .89, and .85, respectively, indicating arelatively poor fit. The RMSEA yielded a value of .09, indicating a moderate fit of

Table 4. Descriptive statistics for all variables (N = 256).

Measure Cronbach’s alpha M SD

Motivation 100.14 16.73Self-efficacy .95 46.83 8.59Test anxiety .85 18.39 7.66Task value .95 34.92 7.19Learning strategies 138.77 24.56Elaboration .87 44.61 8.22Time management .82 35.09 7.92Self-regulated learning and metacognition .81 35.81 8.96Critical thinking .84 23.26 6.13Technology self-efficacy 111.46 9.43General .96 66.34 4.96Online .94 45.12 5.28Course satisfaction .97 116.00 24.82Performance (GPA) 3.67 0.68


the model. Overall, this initial model was not acceptable. Not all the path coefficientreached statistical significance (p < .05) and practical significance (β > 0.3).

Final model

Figure 2 and Table 6 summarize the final model, after deleting nonsignificant pathcoefficients one by one, and adding paths based on modification indices. The resultsindicate an acceptable fit between the hypothesized model and the observed data.While the Chi-square test was statistically significant (χ2 = 88.35, df = 41, andp < .001), the GFI, the CFI, the NFI, and the RMSEA values were .95, .96, .93, and.07, respectively, indicating a good model fit. Overall, the model was acceptable. Allthe path coefficients were statistically significant (p < .05) and some paths alsodemonstrated practical significance (β > 0.3). The endogenous variable of learningstrategies accounted for a small-to-moderate amount of variance with R2 = .03,motivation accounted for a large amount of variances with R2 = .69, and technologyself-efficacy also accounted for a large amount of variance with R2 = .21.

Table 5. The estimation for regression weights of hypothesized model.

Estimate S.E. C.R. p Standardized coefficient

MOTI ← Gender .081 .996 .081 .935 .006MOTI ← Edu Level −1.175 2.043 −.575 .565 −.084MOTI ← Course −.001 .224 −.004 .997 −.001LS ← Gender .586 .915 .640 .522 .038LS ← Edu Level .382 .948 .404 .687 .024LS ← Course −.128 .094 −1.369 .171 −.082TECH_SE ← Gender 5.365 115.771 .046 .963 .618TECH_SE ← Edu Level 16.534 325.738 .051 .960 1.844TECH_SE ← Course 1.959 41.477 .047 .962 2.231CSQ ← MOTI 1.732 .419 4.136 *** .475Grade ← MOTI −.007 .013 −.514 .608 −.068CSQ ← LS .357 .359 .993 .321 .110Grade ← LS .011 .011 1.066 .287 .129CSQ ← TECH_SE .404 .366 1.104 .270 .071Grade ← TECH_SE .027 .011 2.379 .017 .175SE ← MOTI 1.000 .790TA ← MOTI .087 .074 1.175 .240 .077TV ← MOTI .986 .065 15.074 *** .934EL ← LS 1.000 .937TM ← LS .481 .062 7.733 *** .467Metacog. ← LS .832 .062 13.430 *** .714Critical ← LS .541 .043 12.504 *** .680Online ← TECH_SE 1.000 .829General ← TECH_SE .945 .120 7.885 *** .831TECH_SE ← MOTI −.817 38.305 −.021 .983 −1.282MOTI ← TECH_SE 1.000 .638TECH_SE ← LS −8.300 165.513 −.050 .960 −14.683LS ← TECH_SE 1.000 .565LS ← MOTI 1.000 .886MOTI ← LS .497 1.013 .491 .624 .561Grade ← CSQ .007 .002 3.571 *** .264CSQ ← Grade 1.000 .027

*** p < .001


The manifest variable course satisfaction accounted for a large amount of varianceswith R2 = .39, while grade accounted for a small-to-moderate amount of varianceswith R2 = .17. Table 7 presents the comparisons of fit indices for the hypothesizedand final models.

Based on the final model, the number of previous online courses taken affectedthe learning strategies of online learning. Students with more experiences in takingonline courses used more effective learning strategies. In addition, the use of learn-ing strategies influenced the student levels of motivation. More specifically, studentswho reported more effective learning strategies also reported a higher level ofmotivation toward their online courses.

GFI = .95,CFI = .96,NFI=.93, RMSEA=.07

x2/df = 2.15,

Figure 2. Results for final model. *Coefficient is significant at the 0.05 level (two-tailed).**Coefficient is significant at the 0.01 level (two-tailed). ***Coefficient is significant at the0.001 level (two-tailed).

Table 6. The estimation for regression weights of final model.

Estimate S.E. C.R. p Standardized coefficient

LS ← Course .258 .100 2.570 .010 .167MOTI ← LS .743 .061 12.265 *** .831CSQ ← MOTI 2.253 .221 10.172 *** .621TECH_SE ← MOTI .286 .049 5.847 *** .454Self-efficacy ← MOTI 1.000 .796Task value ← MOTI .963 .062 15.502 *** .916Elaboration ← LS 1.000 .930Time manage. ← LS .485 .063 7.735 *** .468Metocog. ← LS .844 .062 13.518 *** .720Critical think ← LS .550 .044 12.618 *** .687Online ← TECH_SE 1.000 .817General ← TECH_SE .979 .125 7.843 *** .851Grade ← CSQ .009 .002 5.217 *** .315Grade ← TECH_SE .029 .011 2.778 .005 .187

***p < .001


Levels of motivation influenced students’ levels of course satisfaction as well astheir technology self-efficacy. That is, students with a higher level of motivationindicated a higher level of course satisfaction and a higher level of technology self-efficacy when taking online courses. Furthermore, levels of course satisfaction andthe levels of technology self-efficacy affected final grades. More specifically,students with a higher level of course satisfaction and a higher level of technologyself-efficacy toward their online courses tended to achieve a higher final grade.

Finally, learning strategies acted as a mediator between the number of previousonline courses taken and the motivation, whereas the motivation mediated the rela-tionship between the learning strategies and the course satisfaction, and between thelearning strategies and the technology self-efficacy. The technology self-efficacy andthe course satisfaction mediated the relationship between the motivation and thefinal grade.

The findings of the current study are as follows:Research hypothesis 1: Students’ gender, educational level, previous online

learning experiences, self-regulated learning (motivation and learning strategies),and technology self-efficacy predict course outcomes (achievement and coursesatisfaction) in online learning settings based on the hypothesized model.

The results indicate that students with greater prior online course experience usu-ally made more effective use of learning strategies in their online courses. With theuse of more effective learning strategies, students had higher levels of motivation,which then led to higher levels of course satisfaction and higher levels of technologyself-efficacy. Students with higher levels of course satisfaction and technologyself-efficacy got better grades in online courses.

Research hypothesis 2: Students’ levels of motivation, learning strategies, andtechnology self-efficacy in online learning settings are different based on theirgender, educational level, and previous experience in online learning.

The results indicate that only the number of previous online courses takendirectly influenced the effectiveness of learning strategies used in online learningsettings. The more previous online courses the students had taken, the more effectivelearning strategies they used in online learning.

Research hypothesis 3A: With higher levels of motivation and more effectivelearning strategies, students have higher levels of achievement and coursesatisfaction in online learning settings.

Based on the final model, the effectiveness of learning strategies directly influ-enced the levels of motivation. The level of motivation directly influenced the levelof course satisfaction and the levels of technology self-efficacy. In addition, the levelof technology self-efficacy directly influenced the final grade of the most recentonline course. Furthermore, the level of course satisfaction directly influenced the

Table 7. Fit indices comparisons between the hypothesized and final models.

Fit indices Hypothesized model Final model

χ2 197.94 88.35df 63 41p <.001 <.001GFI .90 .95CFI .89 .96NFI .85 .93RMSEA .09 .07


final grade of the most recent online course. In other words, when students usedmore effective learning strategies in their online learning setting, they tend to havehigher levels of motivation. Finally, with the higher levels of motivation, studentstended to also have higher levels of course satisfaction and higher levels of technol-ogy self-efficacy.

Research hypothesis 3B: With higher levels of technology self-efficacy, studentshave higher levels of achievement and course satisfaction in online learning settings.

Based on the results in this study, technology self-efficacy directly influenced thefinal grade of the most recent online course. Higher levels of technologyself-efficacy were associated with better final grades.

Research hypothesis 3C: Students’ motivation, learning strategies, andtechnology self-efficacy interact.

Based on this study, students’ self-regulated learning and technology self-efficacydid not interact with each other. Instead, the effectiveness of learning strategies influ-enced the levels of motivation directly, whereas the level of motivation influencedthe levels of technology self-efficacy directly. Motivation was the mediator betweenthe learning strategies and the technology self-efficacy.

Research hypothesis 4: Students’ motivation, learning strategies, and technologyself-efficacy are mediators between students’ gender, educational level, previousexperience in online learning settings, achievement, and course satisfaction.

Based on the results of this study, the effectiveness of learning strategies was themediator between the number of previous online courses taken and level of studentmotivation. In addition, the level of motivation was the mediator between the effec-tiveness of the learning strategies and the level of course satisfaction, and betweenthe effectiveness of the learning strategies and the levels of technology self-efficacy.Furthermore, the level of course satisfaction was a mediator between motivation andthe final grade of the most recent online course. Therefore, students’ self-regulatedlearning and their technology self-efficacy were the mediators between the previousonline learning experience and the course outcomes.

Discussion

The purpose of this study was to examine the relationship among students’ charac-teristics, self-regulated learning, technology self-efficacy, and course outcomes inonline learning settings. The researchers hypothesized a model based on previousresearch. The results indicated that the initially hypothesized model was not accept-able. After model re-specification, the researchers obtained an adequate final model.Based on the results from the final model, students with more prior online learningexperiences tended to have more effective learning strategies when taking onlinecourses, and hence, had higher levels of motivation in their online courses. Whenstudents had higher levels of motivation in their online courses, their levels of tech-nology self-efficacy increased, and their levels of course satisfaction also increased.To the extent that their levels of technology self-efficacy and course satisfactionincreased, their final grade tended to be better than that of the students who did nothave experiences in taking online courses.

Course outcomes

The current study suggests that students’ course satisfaction influences the finalgrade in online course. According to Bean and Bradley (1986), course satisfaction


has a significant effect on performance but performance does not have a strong posi-tive effect on course satisfaction. Results from this research support their conclusion.Further, Astin (1993) suggested that satisfaction is an important intermediate out-come between students’ level of motivation and their performance. Results from thecurrent study support his contention that the course outcome acts as a mediatorbetween students’ levels of motivation and their performance.

Self-regulated learning

Results for the current study were also consistent with previous research that foundself-regulated learning as a predictor of course satisfaction and performance (Artino& McCoach, 2008; Paechter et al., 2010; Puzziferro, 2008). More specifically,results in this study indicate that by using more effective learning strategies, oneincreases his/her levels of motivation, and the increased levels of motivation towardonline courses lead to higher levels of course satisfaction and better performance.According to Zimmerman’s (2000) model of self-regulation, motivational beliefsshould underlie each phase of the self-regulatory process, which means that motiva-tion influences learning strategies. The results of this study do not support Zimmer-man’s model. A possible explanation is that students with more experience in takingonline courses were also more familiar with the online learning settings. Therefore,they had more effective learning strategies in taking online courses, which then ledto the higher levels of motivation toward their online courses.

Pintrich (2004) suggested that self-regulatory activities mediate the relationshipbetween personal and contextual characteristics and actual achievement or perfor-mance. However, there is limited research focusing on this characteristic of self-reg-ulated learning. The results of this research support this pattern in relationships bysuggesting that self-regulated learning acts as a mediator between the numbers ofprevious online courses taken and the course outcomes.

Technology self-efficacy

The results of this study do not support DeTure (2004) and Puzziferro’s (2008)perspective that technology self-efficacy does not affect the course outcomes. Incontrast, the results suggest that students with higher levels of technology self-effi-cacy tend to receive better grades. Based on this study, the technology self-efficacyincluded two different dimensions, general computer self-efficacy and online learn-ing platform-related self-efficacy. This suggests that students who want to succeedin online learning should have confidence in general computer skills as well as inusing online learning platforms.

In addition, the number of previous online courses taken was the antecedent vari-able which influenced the levels of technology self-efficacy through self-regulatedlearning. The results also suggest that students with more previous experiences intaking online courses have higher levels of technology self-efficacy. Arbaugh (2004)found that students’ perception of online courses statistically significantly changedbetween their first and second online courses. Therefore, instructors should pay moreattention to those students in their first online course to encourage them to partici-pate and persist. Furthermore, the perceived usefulness and perceived ease of use ofa technology influence students’ attitudes toward the technology and their


willingness to adopt the technology. These also suggest that technology self-efficacycan be promoted by the instructors. If lecturers provide an introduction to using anonline learning platform, such as using the discussion board, checking grades online,how to download/upload documents, as well as sending/receiving emails, studentswill be more comfortable in using the online learning platform. In addition, an intro-duction to conducting an Internet search through a search engine and using onlinelibrary databases also evokes students’ ability in finding useful resources in takingonline courses. Finally, online courses should be conducted under a user-friendlyplatform to encourage students’ persistent in online courses.

Students’ characteristics

The results of this research support previous research that there is no gender differ-ence or educational-level difference in self-regulated learning and technology self-efficacy (Bates & Khasawneh, 2004; Busch, 1995; Imhof et al., 2007; Lim et al.,2006; Yukselturk & Bulut, 2007). However, previous online learning experienceinfluences self-regulated learning directly. The number of previous online coursestaken influenced effectiveness of learning strategies directly, and affected the levelsof motivation through the effectiveness of learning strategies.

Pintrich and Zusho (2002) discussed the moderating role of gender and ethnicity.They stated that girls had lower academic competence than boys, whereas Asian-American students exhibited lower self-efficacy than African-American students.Even though girls reported a lower level of self-efficacy, they tended to use moreself-regulatory strategies than boys. In addition, they stated that different ethnicgroups may apply different self-regulatory learning strategies. However, they alsopointed out that researchers have not systematically investigated the role of genderand ethnicity in self-regulatory processes. Therefore, further studies in understandingthe role of gender and ethnicity related to self-regulated learning in online learningsetting are needed.

Limitations

The results of this study should be considered within the context of the followinglimitations. This study employed a non-experimental quantitative research designwith self-report survey measures. Therefore, caution should be exercised in the inter-pretation and generalization of the results to other populations. The response rate inthis study was 12.05%, which was low. Based on Kaplowitz, Hadlock, and Levine(2004), the response rate for web-based survey was more than 20% for collegestudents. The possible explanation is that the research materials are delivered viauniversity email system. It is possible that potential participants ignored or did notcheck their emails. It is also possible the survey was identified as junk mail or theemail address was incorrect, which may account for the low response rate in thisresearch. Finally, the grades were self-reported, which might not be the actual gradesthe participants received. Further, the grades were also recorded in letter form (A, B,C, D, or F), which led to a small variance which influences the accuracy of estima-tion. Further research should use numerical grades (e.g., 100-point scale or gradepoint average), in representing grades instead of A, B, C, and D categories, ifpossible.


Implications

Self-regulated learning strategies can be promoted by the instructor or instruction(McMahon & Oliver, 2001; McLoughlin, 2002; Yang, 2006). McMahon and Oliver(2001) suggested that a well-designed online environment should take both affectiveand cognitive processes into account in order to enhance students’ self-regulatedlearning. They also provided suggestions regarding the integration of learner activi-ties, learner supports, and learning resources in the online learning environment.Therefore, instructors can design course activities in a way that can also helpstudents improve their self-regulated learning strategies and their levels of technol-ogy self-efficacy. For example, instructors can ask students to keep a learningjournal, to participate in the discussion on the discussion board at least certain timesa week, or assign projects which require collaborative work.

In addition, instructors have to be familiar with the online learning environmentand platform so they can help students to participate in online courses. In order todo so, they can provide introductory sessions which include the information studentsneed to take online courses at the beginning of the class, and provide prompt feed-back when students have problems. Further, instructors have to pay attention tostudents who are taking their first online course by encouraging them to participateand persist in their online courses.

To succeed in online classes, students have to approach online courses as if theywere taking traditional courses. In other words, students have to set up a specifictime or even a specific place so they can concentrate on the learning materials andassignments of the online courses.

Institutions also play important roles in online learning environments. They canprovide a friendly and easy-to-use online learning platform to increase students’willingness in taking online courses and their levels of online learning technologyself-efficacy. They can also provide workshops or training sessions to both instruc-tors and students to help them become familiar with the online learning platform.

Since there has been limited research examining self-regulated learning as themediator between students’ characteristics and course outcomes, future researchcould consider investigating the mediator effect of self-regulated learning, and com-paring its role in traditional and online learning settings.

In addition, the specific self-regulated learning strategies students used in tradi-tional learning environments and online learning settings should continue to be afocus of researchers in the future to understand which strategies are most effectiveand how they can be encouraged or promoted.

The present study did not include ethnicity as an observed variable in students’characteristics latent variable. Previous research regarding the moderating role ofethnicity in self-regulated learning was limited in online learning settings. Therefore,further research focusing on the role of ethnicity in traditional learning environmentsand in online learning settings is needed.

Despite these concerns, the findings from the current study provide a model forunderstanding the relationships among students’ characteristics, technology self-effi-cacy, self-regulated learning, and course outcomes. By using SEM, the current studyexamined all variables simultaneously, as well as the mediation effect, which alsoprovides more reliable estimations and a more holistic understanding of the relation-ship among students’ characteristics, self-regulated learning, technology self-efficacyand course outcomes. Moreover, the results of the current study suggest that course


designers and instructors can help students, especially those who have little ornegative experiences in their previous online learning courses, develop their self-reg-ulated learning strategies and their self-efficacy in using technology so they canexperience success in their online course.

Notes on contributorsChih-Hsuan Wang is an assistant professor in educational research, measurement, and analy-sis. She teaches classes in measurement, research methods, and statistics. Her research isfocused on motivation and assessment within online learning environments and the impact ofonline learning on the teaching and learning process.

David M. Shannon is a professor in educational research, measurement, and analysis. He tea-ches courses in assessment, measurement, program evaluation, research methods, and statis-tics. His research focuses on teacher assessment, methodological issues, and programevaluation.

Margaret E. Ross is a professor and program coordinator in educational research, measure-ment, and analysis. She teaches courses in assessment, measurement, program evaluation,and statistics. Her research interests include program evaluation and the effects of the inter-play among assessment, teaching, and environmental variables on student learning and moti-vation.

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