Motivation and Reading Comprehension 1 Running head: EFFECTS OF

Motivation and Reading Comprehension 1

Running head: EFFECTS OF MOTIVATIONAL AND COGNITIVE VARIABLES

Effects of Motivational and Cognitive Variables

on Reading Comprehension

Ana Taboada

George Mason University

Stephen M. Tonks

Northern Illinois University

Allan Wigfield and John T. Guthrie

University of Maryland

Correspondence Contact:

Ana TaboadaCollege of Education & Human DevelopmentGeorge Mason UniversityRobinson Hall A, Room 3204400 University Drive, MS 4B3Fairfax VA 22030Phone: 703-993-9182

Email: [email protected]


Abstract

The authors examined how motivational and cognitive variables predict reading

comprehension, and whether each predictor variable adds unique explanatory power when

statistically controlling for the others. Fourth-grade students (N = 205) completed measures

of reading comprehension in September and December of the same year, and measures of

background knowledge and cognitive strategy use in December. Teachers rated internal

reading motivation of each student. Results from multiple regression analyses showed that

motivation, background knowledge, and cognitive strategy-use made significant, independent

contributions to children’s reading comprehension when the other predictor variables were

controlled. Further analyses showed the same cognitive and motivational variables predicted

growth over a 3-month period in reading comprehension. Possible explanations of the

observed relations between motivation, cognitive variables, and reading comprehension are

presented.

Key words: background knowledge, cognitive strategies, comprehension, comprehension

growth, internal motivation, questioning.


Effects of Motivational and Cognitive Variables

on Reading Comprehension

Reading instructional programs increasingly focus on comprehension skills as

children matriculate through school. Researchers and practitioners (Alexander & Jetton,

2000; Kintsch, 1998) have acknowledged the importance of students’ reading comprehension

skills to success in a variety of school subject areas as well as other achievement outcomes.

Given its importance to children’s school success, researchers are investigating what predicts

the growth of reading comprehension skills. Studies have shown that both motivational

(Chapman & Tunmer, 1995; Authors, 1999; Authors, 2006) and cognitive variables (e.g.,

Pressley & Harris, 2006) predict reading comprehension and other achievement outcomes.

However, most studies, to date, have looked either at the relation of motivation variables to

reading comprehension or the relation of cognitive variables to reading comprehension. Few

works have examined how both sets of variables predict reading comprehension when

controlling for the other set of variables. The overall purpose of this study was to examine

how both motivational and cognitive variables predict late elementary school-aged children’s

reading comprehension.

Motivation researchers have discussed how motivational and cognitive processes

interact, and how each affects achievement outcomes (Pintrich, 2003; Pintrich, Marx, &

Boyle, 1993; Author, 2006). In particular, such research has focused on how motivation

provides an activating, energizing role for cognitive processes, which in turn can impact

achievement (Pintrich; Authors, 2006). For example, Author et al. reviewed work showing

that motivational variables such as self-efficacy and intrinsic motivation predict students’

achievement in different areas such as reading ability, math, language arts, sports and

occupational choice. However, Pintrich noted that there is little specific information in the

literature about the strength of these activating processes or how they operate. For instance, it


is likely, that there are multiple motivational pathways for the energization of students’

behaviors such that some students may be motivated by their self-efficacy beliefs, whereas

others may activate cognitive processes through personal interests or contextual factors.

Research that examines the different ways that motivation relates to various cognitive

processes speaks of the need for integrated models of motivation and cognition that has been

emphasized in the motivation field (Pintrich).

In the field of reading motivation, in particular, several researchers have examined the

relations among motivation variables and literacy skills. For example, research has found

relationships of young children’s reading self-concept (assessed as students’ perceptions of

reading competence, the difficulty of reading, and their attitude towards reading) with word

recognition and reading comprehension skills (Chapman & Tunmer; 1995; Chapman,

Tunmer, & Prochnow, 2000). Findings showed that children who reported negative reading

self-concepts performed more poorly on reading-related tasks than did children with positive

reading self-concepts (Chapman et al.).

In her study with first through fourth graders, Gottfried (1990) showed that reading

comprehension positively correlated with intrinsic motivation for reading . Research with

gifted populations has also shown that students with exceptionally high academic intrinsic

motivation performed better on various reading measures from the elementary through the

high school grades (Gottfried, Cook, Gottfried, & Morris, 2005). Also, late-elementary

school students’ task-mastery goals have been found to be associated with their use of active

(as opposed to superficial) learning strategies in literacy tasks (Meece & Miller, 1999; 2001),

and students’ intrinsic motivation has been associated with high-level, complex literacy tasks

(Turner, 1995) and reading amount and text comprehension (Authors, 1999).

In addition, research has established that specific dimensions of reading motivation

(such as involvement and curiosity) and reading comprehension are correlated (Author, 1999;


Authors, 2004). This research has contributed by specifying the multiple dimensions of

motivation, as well as demonstrating the specificity of motivation within the domain of

reading (Authors, 1999; Authors, 2004). However, little work has been done that examines

simultaneously the role of both cognitive and motivational variables on reading

comprehension. Further, there is even less work that addresses the role that both cognitive

and motivation predictors play in the growth of reading comprehension (Authors, 2007).

Given these limitations in previous literature, in this study we examine possible ways in

which cognitive and motivation variables operate in relation to reading comprehension and its

growth. We turn next to specific dimensions of motivation and how they relate to reading

comprehension.

Dimensions of Reading Motivation

Achievement motivation and motivation in specific domains such as reading are

construed as multidimensional phenomena (e.g., Author, 2002; Schiefele, 1999; Authors,

2004; Authors, 1997). Factor analysis has distinguished at least nine components of reading

motivation (Authors, 1999; Authors, 1997): (a) interest, (b) preference for challenge, (c)

involvement, (d) self-efficacy, (e) competition, (f) recognition, (g) grades, (h) social

interaction, and (i) work avoidance. Furthermore, motivations that are more internal, such as

interest or curiosity, preference for challenge, and involvement have been distinguished as

separate constructs in structural equation modeling from more external motivations such as

grades and recognition and have been found to be strongly associated with reading

comprehension not only in Caucasian students, but also in minority students and other

cultures (Unrau & Schlackman, 2006; Authors, 2004).

In this study, we focused on five related dimensions of reading motivation and argue

that they constitute a construct called internal motivation for reading. These five dimensions

of motivation are: (a) perceived control, (b) interest, (c) self-efficacy, (d) involvement, and


(e) social collaboration. We focus on internal motivation –rather than external- because

individuals who are internally motivated show greater perseverance and sustained effort in

their activities (Ryan & Deci, 2000). We focus on these five dimensions because prior

research has determined their contributions to reading comprehension and literacy skills. In

addition, empirical evidence has shown the interrelatedness of these five dimensions. For

instance, (Authors) et al., (2007) examined these constructs with fifth-graders and found that

correlations among them were statistically significant at two time points in the school year,

indicating that they are indeed related to each other. These moderate correlations indicate that

these dimensions of motivation are independent, while still related. In view of the

interrelationships among these constructs we characterize these dimensions of motivation as

constituting as representing the construct of internal motivation for reading. We describe

internal motivation as strongly related to intrinsic motivation because it comes from within

the individual and it moves the individual to pursue an activity for its own sake rather than

for external reasons (Ryan & Deci, 2000). However, we also view internal motivation for

reading as slightly different from intrinsic motivation because of the presence of self-efficacy

as a distinct, and well-researched independent construct that relates to intrinsic motivation but

it is still separate from it (Bandura, 1997). Lastly, we find support for the cohesiveness of

internal motivation for reading on the empirical evidence that has repeatedly shown

relationships between the different dimensions that comprise our measure of internal

motivation and reading comprehension at different ages. We discuss each of the dimensions

of internal motivation next.

Perceived control. Perceived control over reading refers to students’ choices and

perceptions of their own control over their reading- related activities (Authors, 2007). Skinner

and Greene (in press) describe perceived control as individuals’ interpretations of the control

they have over their experiences and the expectations that the self can produce desired and


prevent undesired outcomes. Perceived control is often operationalized in classrooms as

student choice. Perceived control and choice are associated positively with achievement in

reading (Skinner, Wellborn, & Connell, 1990; Authors, 1998).

Interest. Interest has been defined as a “relatively stable evaluative orientation toward

a certain domain” (Schiefele, 1999, p.258) and described as a personal investment in an

activity (Alexander & Murphy, 1998). Student interest has been shown to correlate with

cognitive processes such as deeper text processing of texts learning when other factors such

as text length, text genre, background knowledge and text difficulty were statistically

controlled. Interest has also been found to correlate more highly with deep-level learning than

with surface-level learning from texts (Schiefele, 1996; Schiefele & Krapp, 1996).

Involvement. Involvement can be defined as a descriptor of internal motivation that

refers to the feeling of being absorbed in reading activities and spending significant amounts

of time reading. Involvement and interest are highly related but they are still separable from

each other. Devotion of time to an activity or a task denotes the individual’s involvement in

it. Students who are highly involved in reading seem to create the opportunities that will

support long periods of sustained reading such as organizing their activities and planning for

reading time (Authors, 1997).

Self-efficacy. In both, the general motivation literature and the literature on reading

motivation, one central dimension is beliefs about one’s ability, or self-efficacy. Self-efficacy

refers to individuals’ judgments and perceptions about whether they are capable of doing well

and accomplishing a task (Bandura, 1997). Reading self-efficacy refers to individuals’

judgments or self-evaluations about their ability to do well on reading activities such as

reading a book, or reading a passage (Chapman, Tunmer, & Pochnow, 2000; Schunk &

Pajares, 2002; Author, 2006). Reading self-efficacy has been found to correlate positively


with different measures of reading such as reading comprehension (Schunk & Rice, 1993),

breadth of reading and amount of reading outside of school (Authors, 1997).

Social collaboration. Social collaboration in reading has also been studied within the

motivation literature (e.g., Authors, 1997). It consists of productive social interactions among

learners in relation to literacy tasks such as literature circles (Almasi, 1995), or idea circles

where students share conceptual ideas from different informational texts (Authors, 1997).

Collaboration among students in reading has been correlated with dimensions of intrinsic

motivation such as curiosity and reading involvement, as well as amount and breadth of

reading (Authors, 1997).

Teachers’ perceptions of student motivation. We used teacher ratings of the different

dimensions of students’ motivation as our indicator of motivation, creating an overall student

internal motivation score from these ratings. One of the reasons we used teacher ratings

(perceptions) of students’ motivation rather than student self-report was to avoid the inherent

problems of social desirability of responses to self-report measures. We also wanted to build

on previous research which has used teachers’ observations or teachers’ ratings of students’

behaviors to measure motivation. For example, Onatsu-Arvilommi & Nurmi (2000) showed

reciprocal relations between teachers’ ratings of students’ behaviors of perseverance on task

and persistence for challenging tasks and the reading skills of 6 and 7 year-olds. Further,

these investigators found that teachers’ ratings of students’ motivations predicted reading

skills at a later point even after earlier levels of reading skills, overall cognitive competence,

and reading-related specific competence were controlled for. More recent studies have also

supported the validity of teachers’ perceptions of motivation for older, later-elementary

school aged children. Specifically, external observers’ ratings of student internal motivation

on the constructs of perceived control (choice), interest, involvement, social collaboration and


self-efficacy correlated significantly with teacher’s ratings of students’ internal motivation on

the same constructs (Authors, 2007).

Activating Background Knowledge, Questioning, and Reading Comprehension.

Reading comprehension is an activity that demands high cognitive resources (Duke &

Pearson, 2002). Among these cognitive resources, the role of reading strategies in supporting

reading comprehension has been documented extensively. Research has repeatedly indicated

that strategy instruction increases text comprehension (Duke & Pearson, 2002; National

Reading Panel, 2000; Palincsar & Brown, 1984; Pearson & Fielding, 1991; Pressley, 2000;

Pressley, Wharton-McDonald, Mistretta-Hampston, & Echevarria, 1998) and it has also

highlighted the predictive power of reading strategies on reading comprehension (see

Pressley & Harris, 2006 for a review). In this study we focus on two specific cognitive

reading strategies, activating background knowledge and student questioning in relation to

text.

Background knowledge has been consistently identified as having a significant role in

forming an organized, coherent mental representation of text (e.g., Kintsch, 1998,

McNamara, 2001; Salmerón, Kintsch, & Cañas, 2006; van den Broek,Rapp, & Kendeou,

2005). Seminal studies indicated that comprehension is strongly influenced by the degree of

overlap between the reader’s background knowledge and the text content (e.g., Brown,

Palincsar & Ambruster, 1984). Later cognitive research has explored more specific roles that

background knowledge plays in reading comprehension such as its interaction with text

coherence for traditional printed texts and for hypertexts (McNamara, 2001; McNamara, E.

Kintsch, Songer, & W. Kintsch, 1996; Salmerón et al., 2006).

Student questioning is defined as self-generated questions in relation to a text, topic or

domain (Authors, 2004) and has been characterized as a self-regulatory strategy that fosters

reading comprehension (Collins, Brown, & Newman, 1990; Palincsar & Brown, 1984).


Research on student questioning has shown that teaching students questioning strategies, such

as distinguishing between good questions from poor questions (Cohen, 1983), asking main

idea versus detailed questions (Dreher & Gambrell, 1985; Palincsar & Brown, 1984; Wong &

Jones, 1982), or asking questions in relation to different expository text structures (Feldt,

Feldt, & Kilburg, 2002) is linked to improved reading comprehension. These instructional

effects of student questioning on reading comprehension have been shown in students across

the age span from third grade through college (Cohen, 1983; King & Rosenshine, 1993; Nolte

& Singer, 1985; Raphael & Pearson, 1985; Scardamalia & Bereiter, 1992; Singer & Donlan,

1982; Taylor & Frye, 1992). In their extensive review of instructional studies on question

generation Rosenshine, Meister, and Chapman (1996) concluded that the majority of the

authors attributed the benefits of questioning on comprehension to the fact that questioning

fosters active processing of text and comprehension-monitoring. More recent work, has

proposed that when it comes to expository texts, students’ questions enhance reading

comprehension to the extent that their questions support the conceptual knowledge structure

of the text (Authors, 2006).

The Present Study

Even though questioning and background knowledge-activation have been studied

repeatedly as cognitive variables in relation to reading comprehension, and, by the same

token, several dimensions of motivation have been examined in relation to reading

comprehension, these cognitive and motivational variables have not been studied

simultaneously in relation to text comprehension. Given the prominent role of each of these

variables the aim of this study was to examine the relative predictive power of internal

motivation, background knowledge activation, and student text-based questioning on the

outcomes of reading comprehension and reading comprehension growth


We expect that student internal motivation, and student use of cognitive strategies will

independently contribute to variance in reading comprehension and reading comprehension

growth. Our expectation is based on cognitive accounts of reading comprehension that

highlight the role of cognitive processes in reading comprehension and on accounts of the

significant role that motivation plays in reading comprehension. According to leading

theorists, the goal of reading comprehension is to form an organized, coherent mental

representation that is similar to the structure of the text that is being read (Gernsbacher,

Varner, & Faust, 1990; Kintsch, 1998). The use of reading strategies such as background

knowledge activation and student questioning contributes to the building of such a coherent

mental text-representation. Further, extensive research has emphasized the positive effects

that students’ use of cognitive reading strategies have on reading comprehension (e.g., Duke

& Pearson, 2002; Pressley & Harris, 2006; Authors, 2006). Similarly and as previously

stated, motivation for reading has been repeatedly related to reading comprehension and other

reading achievement outcomes (e.g., see Author, 2006 for a review).

We addressed the following research questions:

1. Does motivation account for significant variance in reading comprehension when

student questioning and background knowledge are statistically controlled?

2. Does background knowledge account for significant variance in reading

comprehension when student questioning and motivation are statistically controlled?

3. Does student questioning account for significant variance in reading

comprehension when background knowledge and motivation are statistically controlled?

4. Does motivation account for significant variance in reading comprehension growth

when student questioning and background knowledge are statistically controlled?

5. Does background knowledge account for significant variance in reading

comprehension growth when student questioning and motivation are statistically controlled?


6. Does student questioning account for significant variance in reading

comprehension growth when background knowledge and motivation are statistically

controlled?

Method

Participants

Fourth-grade students (N = 205) from four schools in a small mid-Atlantic city school

district participated with parental permission. Table 1 shows descriptive statistics for the

sample. In regards to ethnicity, our sample was somewhat more diverse than the school

district as a whole, where the proportions are as follows: 8% African American, 2% Asian,

87% Caucasian, 2% Hispanic, and 1% other. With regard to students’ socioeconomic status,

approximately 20% qualified for free and reduced-price meals; the district-wide average was

13%.

Measures

Five measures were used in this study: (a) background knowledge, (b) student

questioning, (c) multiple-text reading comprehension, (d) Gates-MacGinitie Reading Test,

and (e) internal motivation. The first three measures, (a), (b), and (c) were accompanied by a

researcher-designed reading packet. We administered three alternative forms of the reading

packet, each with a different theme: Oceans and Forests (Form A), Ponds and Deserts (Form

B), or Rivers and Grasslands (Form C). The three reading packets were parallel in content

difficulty, text structure, text difficulty and length per section, number of relevant sections

and distracters, and number and type of illustrations (e.g., biome versus animal illustrations).

Each 75-page reading packet contained 22 sections. Reading packets contained an equal

number of easy (Grades 2-3) and difficult (Grades 4–6) texts, representing nine ecological

concepts and defining information on the biomes. Texts were compiled from life science

trade books and they all cover the content of ecological knowledge within life science. To


ensure counterbalanced administration of text packets, students within classrooms were

randomly assigned one of the three reading packets so that an equal number of students

within each classroom received each packet.

The two reading comprehension measures, multiple-text reading comprehension and

the Gates-MacGinitie reading comprehension test were administered in September and

December of the school year (Times 1 and 2). Data for the measures of background

knowledge, student questioning, and internal motivation were collected in December only

(Time 2) so as to examine the impact of these variables on reading comprehension growth.

Teachers administered assessments in their classrooms during four 60-minute periods. Each

measure is described next.

Activation of background knowledge. The measure assessed students’ activation of

background knowledge on a given pair of biomes (e.g., ponds and deserts, rivers and

grasslands, or oceans and forests) before reading about the topic. Students wrote what they

knew about plant and animal life in their assigned biome in response to a general prompt to

elicit their knowledge in the topic. This was a 15-minute, open-ended writing activity.

Responses were coded using a six-level rubric (see Appendix A for the complete version of

the rubric). Levels in the rubric were hierarchically organized from lower to higher levels,

with lower levels including minimal or inaccurate information and higher levels including

more accurate information organized in relation to a set of nine, pre-defined ecological

concepts (e.g., respiration, feeding, locomotion, communication, defense, reproduction etc.).

For example, at Level 1 students wrote minimal statements with very few characteristics of a

biome or an organism living in the biome. These statements included neither the central

ecological concepts nor definitions of the biomes. In the intermediate levels (Levels 2 and 3)

students included characteristics of one or more biomes, or they presented several organisms

correctly classified to a one or both biomes. However, at these levels definitions and


ecological concepts were not always present. At higher levels (Levels 4 and 5) students

included some knowledge of ecological concepts, and relationships among different

organisms and their biomes. The highest level (Level 6) was characterized by background

knowledge statements that were sufficiently elaborated to denote knowledge of

interrelationships among several organisms and their habitats and biomes (see Appendix A

for examples of each level). Interrater agreement for 26 responses on this measure was 100%

adjacent and 77% exact. A third rater resolved differences.

Student questioning. Student questioning assessed students’ self-generated questions

in relation to text. After browsing the reading packet for a few minutes, students had 20

minutes to write questions about their assigned biomes and the animals and plants living in

them. Questions were coded based on a four-level rubric (see Appendix B, Questioning

Rubric). Question levels varied in terms of the complexity and elaboration of the requested

answer. Lower level questions (Level 1) required factual or yes/no answers. Level 2

questions requested information about ecological concepts, thus they elicited at least a simple

explanation about a central concept. Level 3 questions were also conceptual in their requests,

but were characterized by expressing some background knowledge in the question itself. The

highest question level (Level 4) consisted of questions asking about relationships among

ecological concepts for a given organism or for specific relationships among organisms and

their biomes or habitats.

Students wrote 0–10 questions and were given a rubric score of 1–4 for each question

and a score of zero if they wrote no questions. On the basis of 10 possible questions, a

student’s score could range from 0–40. The mean score for each student was used for data

analysis. Interrater agreement on 100 questions for 25 students was 100% for adjacent and

90% for exact coding.


Multiple-text reading comprehension. This measure of comprehension assessed

knowledge built from text. In an open-ended, constructed-response task, students wrote what

they knew after reading the packet and taking notes on its content. They were given 30

minutes to respond to text and express their knowledge, with two statements of

encouragement after 7 and 13 minutes. Written responses were coded based on the same six-

level rubric that was used for the measure of background knowledge (see Appendix A).

Knowledge built from text was assessed by examining organization of information in

response to key concepts and supporting facts. Thus, lower levels of reading comprehension

included knowledge statements with few and non-essential characteristics of biomes and

organisms living in them. Whereas, higher levels of reading comprehension included biome

definitions and ecological concepts with specific supporting facts organized in a coherent

statement. Interrater agreement for 20 responses was 100% for adjacent and 80% for exact

coding. A third rater resolved differences.

Gates-MacGinitie Reading Test. Alternative forms of the Gates-MacGinitie Reading

Test, Comprehension subtest (Level 4) were administered in a 50-minute period and the

extended scale score was used for data analysis. The Comprehension subtest consists of

fiction and non-fiction passages from various content areas for which students answer

multiple choice questions. Some of the questions require answers to information that is

explicitly stated in the passage, whereas others require constructing answers based on implicit

information. Across-time reliability (September to December) was r (205)= .75, p < .001.

Internal motivation. The internal motivation measure used in this study consisted of

five items that measured the five dimensions of internal motivation described earlier (see

Appendix C). Teachers answered five items about each student in their class. The purpose of

the internal motivation measure was to assess the extent to which each student was a

motivated reader within the classroom, according to the teacher’s perception. Teachers rated


the students in their classrooms on the following items: (a) reads favorite topics and authors

(interest); (b) thinks deeply about the content of texts (involvement); (c) is a confident reader

(self-efficacy); (d) enjoys discussing books with peers (social collaboration); and (e) often

reads independently (perceived control in reading). Teachers rated their students in a 20-

minute session, after repeated observations of students’ behaviors and attitudes towards

reading and reading activities. The response format was Not True (1) to Very True (5) and

student received a score between 5 and 25. Cronbach’s alpha reliability of all items was .90

for this sample (N = 205), which indicates very high reliability.

Results

The means and standard deviations of all the variables are reported in Table 2, while

Table 3 reports correlations among the variables. Note that the two reading comprehension

measures, the Gates-MacGinitie (GM) and the multiple-text reading comprehension (MTC),

were administered at Times 1 and 2. Data for the rest of the variables were collected at Time

2. Correlations between the Time 2 reading comprehension measures and the predictors of

background knowledge, internal motivation, and questioning are all statistically significant (p

< .001). Correlations among the three predictor variables were also significant: prior

knowledge and internal motivation, r(205) = .35, p < .001; internal motivation and

questioning, r(205) = .20, p < .01; and background knowledge and questioning, r (205) =

.27, p < .001. Because these variables are significantly correlated, it is unclear whether or not

they will contribute independently when predicting reading comprehension. Therefore, the

remaining analyses test whether they independently account for variance in reading

comprehension and in growth in reading comprehension.

To examine all six research questions, we conducted a series of multiple regression

analyses. In each analysis, the dependent variable was one of our measures of reading

comprehension, either the GM or the MTC Time 2. Tables 4 and 6 show the results of


multiple regressions using GM Time 2 as the dependent measure of reading comprehension.

Tables 5 and 7 display the results of regressions using MTC Time 2 as the dependent measure

of reading comprehension. The independent variables in each regression were prior

knowledge, internal motivation, and questioning. In order to test our research questions and

estimate the contribution of each variable we entered the independent variables in alternate

order .

We now report the results of the analyses organized by their corresponding research

questions. Research questions 1, 2, and 3 relate to whether each of our predictor variables

(i.e., prior knowledge, internal motivation, and questioning) accounts for significant variance

in reading comprehension when the other two variables are statistically controlled. Whereas,

research questions 4, 5, and 6 ask whether each independent variable accounts for significant

variance in growth of reading comprehension when the two other predictors are statistically

controlled.

Predictors of Reading Comprehension

Our first research question asks whether motivation accounts for significant variance

in reading comprehension when questioning and prior knowledge are statistically controlled.

The first analysis addressing this question is Regression 1 in Table 4. The dependent variable

was the GM (Time 2). To statistically control for prior knowledge and questioning, we

entered these variables in the first and second steps, respectively. Then, internal motivation

was entered on the third step. All together, the three variables explained 36.3% of the

variance in the GM. Internal motivation accounted for 11.8% of the total variance, which was

a statistically significant contribution, F∆ = 37.19, p < .001. The second analysis, seen in

Regression 1 in Table 5, showed a similar result with the dependent variable of MTC (Time

2). The total variance explained by all three variables was 26.9%. When internal motivation

was entered last, after background knowledge and questioning, this variable added 7.6% to


the regression equation, which was also significant, F∆ = 20.79, p < .001. These data allow us

to answer affirmatively to our first research question such that: internal motivation accounted

for significant variance in reading comprehension after the cognitive variables were

controlled for.

Our second research question asked whether prior knowledge accounted for a

significant variance in reading comprehension when questioning and motivation were

controlled. Regression 2 in Table 4 and Regression 2 in Table 5 test this second question. In

both analyses, questioning was entered first in the regression equation, followed by

motivation, and then by prior knowledge. Table 4 shows that background knowledge

contributed a statistically significant amount of variance when predicting GM, ∆R2 = .027,

F∆ = 8.39, p < .01. Similarly, Table 5 shows that the contribution of background knowledge

to the variance in MTC was statistically significant, ∆R2 = .059, F∆ = 16.24, p < .001.

Our third research question asked whether questioning accounted for significant

variance in reading comprehension when prior knowledge and motivation were statistically

controlled. Regression 3 in Table 4 and Table 5 show the results for tests addressing this

question. In these regressions, questioning was entered in the third step, and internal

motivation and prior knowledge were entered in the first two steps as statistical controls. Our

results show that questioning explained a significant amount of variance in the GM (Table 4),

∆R2 = .074, F∆ = 23.20, p < .001, and in MTC (Table 5), ∆R2 = .018, F∆ = 4.88, p < .05.

These analyses support an affirmative answer to our first three research questions. That is,

background knowledge, internal motivation, and questioning each added significantly to the

variance in each of two measures of reading comprehension after controlling for the other

two variables in the regression equation.


Cognitive and Motivational Predictors of Reading Comprehension Growth

Research questions 4, 5 and 6 asked whether each of the cognitive variables and the

internal motivation variable explained variance in reading comprehension growth. We

operationalized growth by first entering a Time 1 measure of reading comprehension into the

regression equation prior to the other three independent variables. Such a test provides an

extremely strong statistical control in that, a large portion of the variance in the dependent

variable is explained by the variable of prior reading comprehension in the first step of the

regression equation. This procedure has been used in previous research (Allen, Cipielewski,

& Stanovich, 1992; Onatsu- Arvilommi & Nurmi, 2000). It is based on the assumption that

when a measure of reading achievement administered at an earlier date (in this case Time 1),

acts as a control for a measure of reading achievement administered at a later date (in this

case Time 2), then a third variable that was associated with the later measure of reading

achievement can be said to be a predictor of growth in reading comprehension. In this case,

the variables of background knowledge, student questioning, and internal motivation were

tested for their association with growth in reading comprehension using both a standardized

measure (GM), and an experimenter-designed measure of reading comprehension (MTC).

Our fourth research question asked whether motivation accounted for a significant

amount of variance in reading comprehension growth when questioning and prior knowledge

are statistically controlled. GM Time 2 was the dependent variable in the first test of this

research question. As seen in Regression 1 of Table 6, we first entered GM Time 1 into the

regression equation, which accounted for 56.1% of the variance in the outcome measure.

After entering background knowledge and questioning, internal motivation still added

significantly to the variance in reading comprehension, ∆R2 = .009, F∆ = 4.58, p < .05.

Regression 1 of Table 7 shows the parallel test with MTC measure Time 2 as the dependent

variable. In the first step, MTC Time 1 accounted for 16.8% of the variance, and in the last


step internal motivation still contributed significant variance to reading comprehension , ∆R2

= .04, F∆ = 11.71, p < .01.

Our fifth research question asked whether prior knowledge accounted for significant

variance in reading comprehension growth, with questioning and internal motivation

statistically controlled. Analyses to answer this question can be seen in Regression 2 in Table

6 and Regression 2 in Table 7. Results indicated that when prior knowledge was entered last

in the regression equation, this variable added significantly to growth in reading

comprehension with both measures, the GM comprehension test, ∆R2 = .008, F∆ = 4.08, p <

.05, and the MTC test, ∆R2 = .041, F∆ = 11.91, p < .01.

Our sixth and last research question asked whether questioning accounted for

significant variance in reading comprehension growth when background knowledge and

motivation were statistically controlled. Questioning did contribute significantly to each

measure of reading comprehension growth. Regression 3 in Table 6 indicates that questioning

adds significantly to the growth of reading comprehension as measured by the GM (Time 2),

(Time 2), ∆R2 = .016, F∆ = 8.04, p < .01. Also, Regression 3 in Table 7 shows questioning

contributing, although weakly, to growth in reading comprehension measured by the MTC

(Time 2), ∆R2 = .012, F∆ = 3.58, p = .06.

Discussion

In recent years, motivational researchers have called for research that helps the field

understand how motivational constructs relate to various cognitive processes, in such a way

that more integrated models of motivation and cognition emerge (e.g., Pintrich, 2003). The

present study contributes to extant work on the relations of motivational and cognitive

processes to reading comprehension by showing how motivational and cognitive variables

independently predict reading comprehension. Results support the notion that even with


strong statistical controls, internal motivation, as well as the cognitive variables of

background knowledge and student questioning, make significant and independent

contributions to variance in two separate measures of reading comprehension. In addition,

each of the predictor variables contributed significantly to growth in reading contribution

with the effects of previous comprehension controlled.

Thus, this study contributes to extant literature in two main ways. First, results from

this study allow examining the specific contributions of internal motivation to reading

comprehension, when the contributions of two important cognitive processes or strategies are

simultaneously taken into account. To our knowledge this study constitutes a first attempt in

this regard. Recent investigations have delved more deeply into whether specific dimensions

of reading motivation contribute to growth in reading comprehension (Authors, 2007).

Findings have indicated that indeed motivational constructs such as student choice,

involvement, and interest predicted reading comprehension growth after controlling for

students’ initial reading comprehension. However, no other cognitive variables have been

taken into account in these analyses. Thus, as noted by these authors in past investigations

(e.g., Authors, 1999) there is an absence of studies measuring reading strategies

independently of text comprehension itself, and measuring the simultaneous contribution of

these variables and of internal motivation to reading comprehension and its growth.

We propose that it is not the predominance of cognitive processes over internal

motivation or of internal motivation over cognitive processes that explain the contribution of

these variables to reading comprehension. Rather, our data support the view that background

knowledge, student questioning, and students’ internal motivations make independent

contributions to students’ reading comprehension. We view these independent contributions

as indicators of the importance of each of these variables in relation to reading

comprehension. However, and in accordance with many theories of motivation (see Pintrich,


2003; Author, 2006), we see internal motivation as the energizer of these linkages helping

students to engage their cognitive processes and strategy use, which leads to growth in

comprehension. We suggest that an internally motivated reader will be more devoted to

reading and thus comprehends better. In other words, if internal motivation for reading is

present and fostered in students, the cognitive processes of background knowledge activation

and student questioning become more fluent, enhancing students’ text comprehension.

Internally motivated readers have a desire to comprehend text. This desire to understand

energizes the use of reading strategies by causing the reader to be metacognitive, whether it is

by asking a question, forming a summary of what has been read, or activating background

knowledge to build a fuller text representation.

How are the two specific reading strategies examined in our study (i.e., activation of

background knowledge and student questioning) energized by a reader’s internal motivation?

With respect to readers’ activation of background knowledge, it is plausible that internally

motivated students are better able to remember what they are reading and better at building

stronger and more stable knowledge representations. Then, with further reading, internally

motivated readers may be better able to connect text to their background knowledge and

continue to build fuller and richer text representations.

With respect to readers’ questioning, this is a reading strategy that by its

characteristics denotes not only cognitive, but also motivational attributes of a reader. From a

motivational standpoint, a reader who asks a relatively large number of high-quality

questions conveys her curiosity, inquisitiveness, and interest in the topic and the text at hand.

Research in student questioning has described this curiosity as the active, initiative-driven

predisposition of learners who pose a substantial number of questions (e.g., Graesser,

McMahen, & Johnson, 1994; Collins, Brown, & Newman, 1990) but this research has not

necessarily linked these dimensions of questioning to specific motivational constructs. We


believe such linkages can be made. For example, student self-generated questions express

their interest in relation to the topic they are about to read; when given the opportunity to ask

their own questions in relation to text students are empowered to (a) set their own goals for

reading and (b) select and process certain types of information in preference to others, a

characteristic central to the notion of interest (Hidi, 1990). Student questions also encompass

possibilities for perceived-control and autonomy. By writing their own questions students

become aware that they are not merely responding to the teacher’s or test maker’s questions,

but rather they have an opportunity to decide what is of relevance in their reading and then

pursue this relevant information by seeking answers to their questions. Lastly, student-

generated questions can also embody opportunities for self-efficacy development, especially

when students are taught to differentiate among question types or levels and are provided

with opportunities to compare their current performances with past performances in

generating questions and note their progress in the use of the strategy.

The second major contribution of this study to the literature rests on its instructional

implications. Given that the results of this study showed that both cognitive reading strategies

and internal motivation contribute independently to students’ reading comprehension and its

growth, educators and practitioners need to take into account the significance that both of

these practices have for reading comprehension instruction. The benefits of cognitive

strategies for reading comprehension have been well established (e.g., Duke & Pearson,

2002; National Reading Panel, 2000; Pressley & Harris, 2006) however, educators need also

to consider how reading strategies can be taught and fostered in the light of supporting

students’ internal motivation for reading. In other words, students need opportunities to use

reading strategies in a classroom context where internal motivation is equally supported

through concrete practices. For example, summarizing a text or asking questions in relation to

a text that is disconnected from students’ backgrounds or for which students do not have a


broader context to relate the content to (e.g., completely disconnected from units that students

are learning in social studies or science) will not be as successful as providing students with

texts that relate to their backgrounds, or with texts for which students can make connections

to ideas learned in science or social studies (Authors, 2004; 2006). If teachers can incorporate

principles that support building different aspects of internal motivation for reading they most

probably have higher chances of having students use reading strategies successfully, and in

turn, become better comprehenders (Authors, 2004; 2006) Further, these dimensions of

motivation have been discussed extensively in terms of classroom practices that can be

supported and developed by teachers in classrooms across the age span (see Stipek, 1996;

Perry, Turner, & Meyer, 2006). The contributions of both cognitive and motivational factors

to reading comprehension and its growth, evident in the results of this study, serve to

emphasize that both are equally important in the development of students’ reading

comprehension and neither should be neglected in classroom instruction.

Limitations and Considerations for Future Research

The present study has some limitations that should be acknowledged. First, because

the study is correlational it does not provide information about the processes involved in the

observed relations of internal motivation, cognitive processes, and reading comprehension.

From this study we gleaned some information about the strength of these relations and also

that motivational and cognitive variables both predict comprehension, but data from this

study only allows us to hypothesize about possible explanations for the relationships among

these variables. Second, only two reading strategies were used in these analyses, thus future

research should examine other cognitive variables in these categories. Similarly, a composite

internal motivation variable was used in these analyses. In future work in it would be

interesting to examine the separate dimensions of internal motivation. Finally, we studied the

relations of reading motivation, cognitive processes, and reading comprehension in fourth-


grade students. Future studies should examine these relations developmentally, to see when

they begin to emerge and whether they get stronger as children get older.

Based on these limitations and emerging trends in the field of reading motivation we

consider three avenues for future research. First, we suggest that researchers should begin

studying how motivation, cognitive processes, and reading comprehension relate. This could

be done through interview studies to ascertain individuals’ understandings of how their

motivation relates to their cognitive effort, and reading strategies in particular. Such studies

could ask students directly about their perceptions of these relations. Gaining a better

understanding of the processes involved in such relations will help educators develop more

effective interventions to enhance both the motivation for reading and the use of cognitive

reading strategies.

Second, in this paper, we discussed ways in which motivation energizes or activates

cognitive processes. Both Author et al. (2004) and Pintrich (2003) suggested that cognitive

processes also might influence motivation. For instance, when given an activity or task in

school, students’ background knowledge with respect to that activity may activate

motivational processes and beliefs, such as their self-efficacy or interest. If they know a lot

about the activity, they may feel more efficacious about taking on a new activity in this area,

and also, may be more interested in it. Author and colleagues suggested that when the

students participate in reading activities which provide strong content goals and contain rich

topical content, students become more motivated to engage in and to gain knowledge from

these activities. Thus, future research should examine the reciprocal ways in which cognitive

and motivational processes interact.

Third, our data suggest that motivation contributed to reading comprehension

independently from students’ background knowledge and their questioning in reading. This

implies that the motivation effect was not attributable to these two powerful cognitive


processes. Thus, motivation may be an affective construct that directly influences reading

comprehension. However, it remains possible that the motivation effect is mediated by a

cognitive variable that was not measured in this study. For example, inferencing is a powerful

memory-based process that was not measured, nor was comprehension monitoring, a

metacognitive process. Either of these could mediate the effect of motivation on

comprehension. Thus, although motivation appears to contribute independently from two

cognitive processes (background knowledge and questioning strategy) there are additional

cognitive variables that should be tested as potential mediators of the effect of motivation on

reading comprehension.


Appendix A

Conceptual Knowledge Rubric

Prior Knowledge and Multiple-Text Comprehension Assessment

Facts and associations – simple.

Level 1Students present a few characteristics of a biome or an organism. Example: In grasslands are lions, tigers, zebras.

Facts and associations – extended.

Level 2

Students correctly classify several organisms, often in lists, with limited definitions.

Example: Animals live in a desert. They like to live there because it’s nice and warm.

Ducks like to drink water in the pond. They are different because one of them is wet

and the other dry. Snake and bears, birds, live in the deserts. They help each other

live by giving the animals water and some food that’s what the mothers do.

Concepts and evidence – simple.

Level 3

Students present well-formed definitions of biomes with many organisms correctly

classified, accompanied by one or two simple concepts with minimal supporting

evidence.

Example: Deserts are different than ponds because deserts have a little bit of water

and ponds have a lot of water. The animals that live in a pond are snakes, fish, bugs,

ducks, and plants. The plants that live in a pond are grass and seaweed. The animals

and plants that live in a desert are rattlesnakes, foxes, rabbits, owls, woodpeckers.

The plants that live in a desert are cactus, little grass, small trees. Some of the

animals eat plants. The plants eat the food in the soil and the little rain. The animals

help the plants live by when the animals step on the ground it makes it a little soft and

it is easy for the plants to grow. The plants help the animals by bringing some animals

close so other animals can catch them and eat them. The animals also help the plant

when some of the bugs that drink the plants nectar carry things from one plant to


help the plants live by when the animals step on the ground it makes it a little soft and

it is easy for the plants to grow. The plants help the animals by bringing some animals

close so other animals can catch them and eat them. The animals also help the plant

when some of the bugs that drink the plants nectar carry things from one plant to

another.

Concepts and evidence – extended.

Level 4

Students display several concepts of survival illustrated by specific organisms with

their physical characteristics and behavioral patterns.

Example: Some snakes, which live in the desert, squeeze their prey to death and then

eat them. This is called a deadly hug. Bright markings on some snakes are warnings

to stay away. In the desert two male jackrabbits fight for a female. Some deserts are

actually cold and rocky. Both deserts’ hot or cold, it barely ever rain and if it does it

comes down so fast and so hard it just runs off and does not sink into the ground.

Patterns of relationships – simple.

Level 5

Students convey knowledge of relationships among concepts of survival supported by

descriptions of multiple organisms and their habitats.

Example: A river is different from grassland because a river is body of water and

grassland is land. A river is fast flowing. Grasshoppers live in grasslands. A

grasshopper called a locust lays its egg in a thin case. One case could carry 100 eggs.

The largest herbivores in the grassland are an elephant. In the African savanna meat-

eats prey on grazing animals, such as zebra. Many animals live in grasslands. The

river is a home to many animals. In just a drop of river water millions of animals can

be living in it. Many fish live in the river. Many birds fly above the grasslands and

rivers. A river is called freshwater because it has no salt in it.


river is a home to many animals. In just a drop of river water millions of animals can

be living in it. Many fish live in the river. Many birds fly above the grasslands and

rivers. A river is called freshwater because it has no salt in it.

Patterns of relationships – extended.

Level 6

Students show complex relationships among concepts of survival emphasizing

interdependence among organisms.

Example: River and grassland are alike and different. Rivers have lots of aquatic

animals. Grasslands have mammals and birds. Rivers don’t have many plants but

grassland have trees and lots of grass. Rivers have lots of animal like fish trout and

stickle backs. They also have insects and mammals, like the giant water bug and river

otters. Grasslands usually have lions, zebras, giraffes, antelope, gazelles, and birds.

In rivers the food chain starts with a snail. Insects and small animals eat the snail.

Then fish eat the small animals and insects. Then bigger animals like the heron and

bears eat the fish. Snails also eat algae with grows form the sun. In the grass lands

the sun grown the grass. Animals like gazelle, antelope, and zebra eat the grass. Then

animals like lions eat them. This is called a food chain of what eats what. In a way the

animals are helping each other live. Animals have special things for uses. Otters have

closable noses and ears. Gills let fish breath under water. Some fish lay thousands of

egg because lot of animals like eating fish eggs. Some animals have camouflage.

Swallow tail butter fly larva look like bird droppings. That is what I know and about

grasslands rivers.


Appendix B

Questioning Rubric

Level 1: Factual Information

Questions are simple in form and request a simple answer, such as a single fact. Questions are

a request for a factual proposition. They are based on naïve concepts about the world rather

than disciplined understanding of the subject matter Questions refer to relatively trivial, non-

defining characteristics of organisms (plants and animals), ecological concepts or biomes.

Examples

How big are bats? Do sharks eat trash? How much do bears weigh?

Are there crabs in a river? How old do orangutans get? How big are grasslands? How many

rivers are there in the world?

Level 2: Simple Description

Questions are a request for a global statement about an ecological concept or an important

aspect of survival. Questions may also request general information that denotes a link

between the biome and organisms that live in it. The question may be simple, yet the answer

may contain multiple facts and generalizations. The answer may be a moderately complex

description or an explanation of an animal’s behavior or physical characteristics. An answer

may also be a set of distinctions necessary to account for all the forms of species.

Examples

How do sharks have babies? How do birds fly? How do bats protect themselves? What kinds

of sharks are in the ocean? What kind of waters do sharks live in? How far do polar bears

swim in the ocean?

Level 3: Complex Explanation

Questions are a request for an elaborated explanation about a specific aspect of an ecological

concept with accompanying evidence. The question probes the ecological concept by using


knowledge about survival or animal biological characteristics. Questions may also request

information that denote a link between the biome and organisms that live in it. Questions use

defining features of biomes to probe for the influence those attributes have on life in the

biome. The question is complex and the expected answer requires elaborated propositions,

general principles and supporting evidence about ecological concepts.

Examples

Why do sharks sink when they stop swimming? Why do sharks eat things that bleed? How do

polar bears keep warm in their den? Why do sharks have 3 rows of teeth? Why is the polar

bear’s summer coat a different color? Do fruit-eating bats have really good eyes? Do owls

that live in the desert hunt at night? Why do Elf Owls make their homes in cactuses?

Level 4: Pattern of Relationships

Questions display science knowledge coherently expressed to probe the interrelationship of

concepts, the interaction with the biome or interdependencies of organisms. Questions are a

request for principled understanding with evidence for complex interactions among multiple

concepts and possibly across biomes. Knowledge is used to form a focused inquiry into a

specific aspect of a biological concept and an organism’s interaction with its’ biome.

Answers may consist of a complex network of two or more concepts.

Examples

Do snakes use their fangs to kill their enemies as well as poison their prey? Do polar bears

hunt seals to eat or feed their babies? Why do salmon go to the sea to mate and lay eggs in

the river? How do animals and plants in the desert help each other? How are grassland

animals and river animals the same and different? Is the polar bear at the top of the food

chain?


Appendix C

Items from the measure of internal motivation

• Often reads independently

• Reads favorite topics and authors

• Is a confident reader

• Thinks deeply about the content of texts

• Enjoys discussing books with peer


Authors Note

The work reported herein was supported by the Interagency Educational Research

Initiative (IERI) (Award #0089225) as administered by the National Science

Foundation. The findings and opinions expressed here do not necessarily reflect the

position or policies of the Interagency Educational Research Initiative, the National

Science Foundation, or the University of Maryland. The authors of this manuscript

thank Eileen Kramer and Vanessa Rutherford for their assistance in preparing this

document.


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Table 1

Demographic Data of the Sample

Students Percent

Total N 205 100.0

Sex

Girls 108 52.7

Boys 97 47.3

Ethnicity

African American 35 17.1

Asian 8 3.9

Caucasian 138 67.3

Hispanic 15 7.3

Other / Missing 9 4.4


Table 2

Means and Standard Deviations of Variables Used in Multiple Regressions

Variable M SD

Gates-MacGinitie Time 1 486.75 47.37

Gates-MacGinitie Time 2 501.93 44.54

Mult Text Comp Time 1 2.85 1.14

Mult Text Comp Time 2 3.33 1.28

Prior Knowledge 2.47 .85

Motivation 19.39 4.79

Questioning 1.46 .54

Notes: n = 205

Mult Text Comp = Multiple-text reading comprehension


Table 3

Correlations among Measures of Reading Comprehension, Prior Knowledge, Motivation and

Questioning

1 2 3 4 5 6

1. Gates-MacGinitie Time 1 ---

2. Gates-MacGinitie Time 2 .75** ---

3. Mult Text Comp Time 1 .49** .39** ---

4. Mult Text Comp Time 2 .46** .50** .41** ---

5. Prior Knowledge .34** .39** .31** .41** ---

6. Motivation .51** .49** .38** .42** .35** ---

7. Questioning .36** .40** .21* .27** .27** .20*

Notes: n = 205

Mult Text Comp = Multiple-text reading comprehension

* p < .01, two-tailed

** p < .001, two-tailed

Motivation and Reading Comprehension 1 Running head: EFFECTS OF

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