Motivation and Reading Comprehension 1 Running head: EFFECTS ...

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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 Taboada College of Education & Human Development George Mason University Robinson Hall A, Room 320 4400 University Drive, MS 4B3 Fairfax VA 22030 Phone: 703-993-9182 Email: [email protected]

Motivation and Reading Comprehension 2 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.

Motivation and Reading Comprehension 3 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

Motivation and Reading Comprehension 4 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;

Motivation and Reading Comprehension 5 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

Motivation and Reading Comprehension 6 (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

Motivation and Reading Comprehension 7 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

Motivation and Reading Comprehension 8 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

Motivation and Reading Comprehension 9 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).

Motivation and Reading Comprehension 10 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

Motivation and Reading Comprehension 11 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?

Motivation and Reading Comprehension 12 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

Motivation and Reading Comprehension 13 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

Motivation and Reading Comprehension 14 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.

Motivation and Reading Comprehension 15 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 sixlevel 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

Motivation and Reading Comprehension 16 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 20minute 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

Motivation and Reading Comprehension 17 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

Motivation and Reading Comprehension 18 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.

Motivation and Reading Comprehension 19 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

Motivation and Reading Comprehension 20 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

Motivation and Reading Comprehension 21 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,

Motivation and Reading Comprehension 22 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

Motivation and Reading Comprehension 23 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, studentgenerated 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

Motivation and Reading Comprehension 24 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-

Motivation and Reading Comprehension 25 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

Motivation and Reading Comprehension 26 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.

Motivation and Reading Comprehension 27 Appendix A Conceptual Knowledge Rubric Prior Knowledge and Multiple-Text Comprehension Assessment Facts and associations – simple. Level 1 Students 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

Motivation and Reading Comprehension 28 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 meateats 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.

Motivation and Reading Comprehension 29 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.

Motivation and Reading Comprehension 30 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, nondefining 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

Motivation and Reading Comprehension 31 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?

Motivation and Reading Comprehension 32 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

Motivation and Reading Comprehension 33 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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Motivation and Reading Comprehension 41 Table 1 Demographic Data of the Sample Students Total N

Percent

205

100.0

Girls

108

52.7

Boys

97

47.3

35

17.1

8

3.9

Caucasian

138

67.3

Hispanic

15

7.3

9

4.4

Sex

Ethnicity African American Asian

Other / Missing

Motivation and Reading Comprehension 42 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

Motivation and Reading Comprehension 43 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