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THE RELATION BETWEEN GOAL ORIENTATION
AND STUDENTS’ MOTIVATIONAL BELIEFS AND SELF-REGULATED LEARNING
CHRISTOPHER A.WOLTERS,SHlRLEYL.YU, AND PAUL R. PINTRICH
THEUNIVERSITYOFMICHIGAN,ANNARBOR
ABSTRACT: The relations between three goal orientations and students’ moti- vational beliefs and self-regulated learning were examined in a correlational study of 434 seventh and eighth grade students. Data were collected over two time points (fall and spring) within one school year with self-report question- naires. Regression analyses revealed that adopting a learning goal orientation and a relative ability goal orientation resulted in a generally positive pattern of moti- vational beliefs including adaptive levels of task value, self-efficacy, and test anx- iety, as well as cognition including higher levels of cognitive strategy use, self- regulation, and academic performance. Results showed that adopting an extrinsic goal orientation led to more maladaptive motivational and cognitive outcomes. These findings were replicated across three different academic subject areas of English, math, and social studies. Results are discussed in terms of the implica- tions for goal theory.
The important role of goals and goal orientation beliefs has been a focus of recent research on students’ motivation and self-regulated learning in academic settings (Maehr & Pintrich 1991). Although there is some confusion in the literature on the differences between the constructs of goals and goal orientations, we assume that goals represent the very specific purposes that individuals are striving for in a
Direct all correspondence lo: Christopher A. Woiters, Department of Educational Psychology, 491 Farnish Hall, University of Houston, Houston, TX 77204-5874.
learning and Individual Differences, Volume 8, Number 3, 1996, pages 21 l-236. Copyright 0 1996 by JAI Press Inc. All rights of reproduction in any form reserved. ISSN: 1041-6080
212 LEARNING AND lNDIV/DUAL DIFFERENCES VOLUME 8. NUMBER 3.1996
specific setting (see Locke & Latham 1990). In an achievement context, a specific goal might be to get 10 problems correct on a math worksheet. In contrast, goal orientation theories are concerned with why the student wants to get the 10 prob- lems correct, how they approach the task, and the standards they use to evaluate their performance (Pintrich & Schunk 1996). Goal orientation represents an inte- grated pattern of beliefs that leads to “different ways of approaching, engaging in, and responding to achievement situations” (Ames 1992, p. 261). As such, goal ori- entation beliefs are somewhat more global than task-specific goals. In addition, goal orientation beliefs may reflect somewhat stable individual differences that individuals bring with them to many achievement tasks (Dweck & Leggett 1988; Pintrich & Schunk 1996). For example, a student may approach many different achievement tasks with a general mastery goal orientation, regardless of the spe- cific goals they set for themselves when doing those specific tasks. At the same time, however, the nature of the classroom and school environment can encour- age the adoption of different goal orientations (Ames 1992; Maehr & Midgley 1991). In this study we focus on goal orientation beliefs and conceptualize and measure them as personal characteristics of individual students.
The goal orientation research has suggested that there are two general goal ori- entations that students can adopt towards learning: a learning goal orientation where the student is focused on mastery and learning of the material and a per- formance or ability orientation where the student is focused on demonstrating his/ her ability and performance in relation to other students. These two goal ori- entations often go by different labels including task, mastery, or learning goal orientation and performance, ability, and ego orientation (see Ames 1992; Ander- man & Maehr 1994; Dweck & Leggett 1988; Nicholls 1984; Pintrich & Schunk 1996; Urdan & Maehr 1995) with some subtle theoretical differences attached to the different labels, but the basic construct is similar across these models. In gen- eral, these models assume that a learning goal orientation is positively related to a number of adaptive motivational and cognitive processes which should result in positive performance outcomes, while a performance or ability goal orientation can generate negative or maladaptive motivational and cognitive processes with concomitant negative performance outcomes (Pintrich & Schunk 1996).
In general, a learning goal orientation has been linked to positive motivational beliefs such as more adaptive attributional patterns and higher levels of self-effi- cacy and perceived competence. Students who adopt a mastery goal orientation are focused on learning and mastery of the material and this orientation leads them to make attributions to effort and to base their self-efficacy judgments on the idea that effort will lead to success and mastery (Ames 1992). Related to positive self-efficacy beliefs, feelings of anxiety should be lessened under a mastery goal orientation. Students who are focused on mastery and who have high efficacy beliefs should not feel as anxious about doing well and succeeding (Pintrich & Schunk 1996). Finally, a mastery goal orientation has been linked to an intrinsic interest in and value for learning (Ames 1992). Given these findings, we expected that a learning goal orientation would be positively related to self-efficacy and task value beliefs and negatively related to test anxiety in this study.
THERELATIONBETWEENGOAL ORIENTATION 213
Besides the relation between a learning goal orientation and these motivational beliefs, a mastery orientation also has been linked to the quality of students’ cog- nitive engagement in both experimental and correlational studies (Pintrich & Schrauben 1992). For example, in an experimental study Graham and Golan (1991) found that students who adopted a learning goal orientation were more likely to process the material to be memorized at a deeper level. In correlational studies, Pintrich and his colleagues have shown that students who adopt a mas- tery orientation focused on learning the material are more likely to report using cognitive strategies such as elaboration and organizational strategies which reflect deeper levels of cognitive processing. These studies also have shown that a general learning orientation is positively related to reports of metacognitive and self-regulatory strategies such as planning, monitoring, and regulating learning (Pintrich & De Groot 1990; Pintrich & Garcia 1991; Pintrich, Roeser, & De Groot 1994). In addition, we have found that these relations hold for both junior high students and college students. Other studies also have shown a similar pattern of results for mastery goal orientation with elementary, junior high, high school, and college students (e.g., Ames & Archer 1988; Bouffard, Boisvert, Vezeau, & Larouche 1995; Meece, Blumenfeld, & Hoyle 1988; Meece & Holt 1993; Nolen 1988). We expected to find similar results in this study regarding a positive rela- tion between a learning goal orientation and students’ self-reported use of cognitive and self-regulatory strategies.
The findings for performance or ability orientation are not as straightforward as those for a learning orientation. The normative model assumes that students who endorse a performance orientation will have less adaptive motivational beliefs (Ames 1992; Dweck & Leggett 1988). Students who adopt a performance orienta- tion are assumed to be focused on their performance relative to others, to be concerned about demonstrating their ability, and to be centered on their self- worth (Ames 1992). This general orientation should lead to less adaptive attribu- tional patterns such as attributing failure to lack of ability and lower perceptions of competence and self-efficacy. In addition, this goal orientation is often associ- ated with a concern with grades and other extrinsic rewards rather than an intrinsic value or interest in learning (Ames 1992; Dweck & Leggett 1988). Besides these motivational beliefs, the normative model would expect that a performance goal orientation would be associated with lower levels of cognitive engagement such as the use of more surface level processing strategies (e.g., rehearsal strate- gies) instead of deeper processing strategies (i.e., elaboration and organizational strategies) and less use of self-regulatory strategies (Nolen 1988; Pintrich & Schrauben 1992).
However, there are conflicting theoretical considerations and empirical results that suggest that a performance goal orientation might not always show a negative relation with motivation, cognition, and performance. First, from a theoretical per- spective, the literature is unclear about whether learning and performance goal orientations are orthogonal or negatively related to one another. If they are orthog- onal, and some research has shown that they are independent dimensions (e.g., Ames & Archer 1988), then it would be possible for students to show varying pat- terns of goal orientation (e.g., high learning-high performance; high learning-low
214 LEARNING AND /ND/V/DUAL DIFFERENCES VOLUME 8. NUMBER 3.1996
performance, etc.). Taking a self-regulated learning perspective, Pintrich and Gar- cia (1991) suggested that there may be an advantage for students to be relatively high on both orientations. They proposed that in classroom settings where stu- dents are graded and obtaining good grades is an important goal embedded in the academic context, a focus on mastery as well as extrinsic factors such as grades might be the most adaptive pattern. In addition, Butler and Winne (1995) also have argued from a self-regulated learning perspective that both learning and perfor- mance goal orientations can be adopted by students and that these multiple goals can provide students with important guides for interpreting feedback and regulat- ing their learning. In their model, a performance goal orientation could be useful to students because it would provide an additional external reference by which to judge performance, not just an internal standard that is generated by a learning goal.
Besides the possibility of multiple and interacting goal orientations, there is emerging evidence that the normative model of two general goal orientations, a mastery and a performance orientation, may not be the best representation of all potential goal orientations. For example, Midgley and Maehr and their colleagues (see Anderman 1994; Maehr & Midgley in press; Midgley, Maehr, Hicks, Roeser, Urdan, Anderman, & Kaplan 1996; Urdan 1994) suggest that performance orienta- tion is too general and confounds different aspects of a performance orientation. They divide performance orientation into two different types, an extrinsic goal ori- entation and a relative ability orientation. A student adopting an extrinsic goal orientation would focus more on seeking extrinsic rewards such as grades and praise from teachers and parents as well as avoiding external sanctions such as punishment. A relative ability goal orientation would involve the student being more concerned with social comparison, competing with others to be the best, and not wanting to appear less able than others. Given this analysis, in this model there are three goal orientations, a learning orientation focused on mastery, an extrinsic orientation focused on grades and rewards, and a relative ability orientation focused on social comparison and competition with others. We utilize this basic three goal orientation model in this study.
In line with this model but working from a somewhat different perspective that combines social cognitive goal theory and traditional achievement motivation the- ory, Elliot and Harackiewicz (1996) have suggested that there may be two different types of performance orientation along with a mastery goal orientation. In their model, a mastery goal still represents a focus on task mastery as in the normative goal theory view, but an approach performance orientation is where students strive to achieve a goal based on normative competence and is similar to the tradi- tional need for achievement or need for success construct (Atkinson 1957). The other performance goal orientation is an avoidance performance orientation where the individual strives to avoid failure, the other general type of motive from Atkinson’s (1957) achievement motivation theory. In their model of three goal ori- entations, Elliot and Harackiewicz (1996) propose that it is only under the avoidance performance orientation that the negative effects of a performance goal will occur, not in the approach performance orientation.
THERELATIONBE7WEENGOALOR/EN7ATlOlV 215
In terms of the empirical results regarding these different theoretical predic- tions, the results are mixed. First, Ames and Archer (1988) found that there was no interaction between mastery and performance goal orientations on high school students’ cognitive strategy use and attributions, there was only a main effect dif- ference in favor of the high mastery group over the high performance group. Meece and Holt (1993) in a study of elementary school children did not find large differences between a high mastery-high performance group and a high mastery- low performance group formed by median splits. The two reliable differences found showed that the high mastery-low performance group did have a higher level of achievement on a standardized test and less superficial task engagement than the high mastery-high performance orientation. In a cluster analysis of the same students, in comparison to the median split procedure, they found that the group of students who were high in mastery and low in performance orientation had much higher levels of deeper cognitive engagement and achievement on both grades and standardized tests than the cluster group of students who were high in both mastery and performance (Meece & Holt 1993).
Pintrich and Garcia (1991) found similar differences between different groups of college students, with their results showing that the high mastery-low extrinsic (a focus on grades) group had the most adaptive motivational beliefs, highest lev- els of strategy use and best performance in terms of course grades, not the high mastery-high extrinsic group as they had predicted. They did find, however, that in the absence of a mastery orientation, a high extrinsic orientation (the low mas- tery-high extrinsic group) did have a better profile of motivation, strategy use, and achievement than the low mastery-low extrinsic group. More recently, Bouf- fard et al. (1995), in another correlational study of college students, did find a mastery by performance goal interaction such that the highest level of cognitive strategy use, self-regulation, and course grade was in the group of students who were classified (on the basis of median splits) as high in learning and high in per- formance orientation. This group also had the highest level of motivation, which in this study was a self-report of effort and persistence at a task. The next best pat- tern of motivation, cognition, and achievement was in the high mastery-low performance group, followed by the low mastery-high performance group, and the most maladaptive pattern was for those students low in both goal orienta- tions. The actual size of the differences between groups were not large, but they were statistically significant. This study seems to be one of the few correlational studies that finds a positive effect for having high levels of both a mastery and performance orientation in contrast to Pintrich and Garcia (1991) and Meece and Holt (1993).
However, in line with the Bouffard et al. (1995) results, in two experimental studies, Elliot and Harackiewicz (1996) did find support for their hypothesis that a performance orientation only has negative effects when the individual is focused on avoiding failure. They experimentally induced the three different goal orienta- tions, a mastery goal (focus on individual standards for performance), an approach performance orientation (focus on comparison with others in terms of demonstrating how able one is), and an avoidance performance orientation (focus on comparison with others in terms of avoiding the appearance of being unable).
216 LEARNlNGANLJINDIVlDUAL DIFFERENCES VOLUME 8, NUMBER 3.1996
The task was a puzzle solving activity and measures were taken of the college sub- jects’ interest in the puzzles, their choice of doing the puzzles in a free choice situation, and a self-report measure of task involvement. In both studies, the mas- tery group and the approach performance group did not show a different pattern of interest, choice, or involvement, while the avoidance performance group showed significantly lower levels of interest, choice, and involvement.
Taken together, the research on these different goal orientations suggest that it is important to distinguish theoretically and empirically between different types of performance or extrinsic goal orientations. Accordingly, in this study we will examine three different goal orientations, a learning goal orientation, a relative ability goal orientation, and an extrinsic goal orientation. The learning goal orien- tation is the basic mastery orientation where the student is focused on learning and understanding the material. Our definition and assessment of this construct is compatible with all the other research in this area (see Midgley et al. 1996). The relative ability performance orientation in this study is very similar to the approach performance orientation suggested by Elliot and Harackiewicz (1996) where the student is focused on doing well in order to demonstrate his/ her abil- ity and to achieve relative to other students. The final performance orientation we have labeled extrinsic orientation in this study, following Pintrich and Garcia (1991) and Midgley et al. (1996), where the focus is on obtaining high grades, rewards, or approval from others. This is not similar to Elliot and Harackiewicz’s (1996) avoidance performance orientation. Rather, it reflects the argument that in actual classroom contexts, in contrast to laboratory settings, obtaining good grades is an important goal for most students (Pintrich & Garcia 1991). At the same time, we also recognize that a singular focus on an extrinsic goal orientation could have a detrimental effect on motivation, cognition, and performance (Deci & Ryan 1985).
In addition, the correlational studies demonstrate that it is important to separate out the effects of these different goal orientations and, most importantly, to exam- ine the pattern of interactions between the different orientations. However, in many of the correlational studies, cluster analysis or median split procedures were used to examine the interactions. In the current study, we will investigate the interactions between different goal orientations, but we will use continuous vari- ables and cross-product terms to index the interactions in order to maintain the observed variance in the data and not categorize individuals into groups based on somewhat arbitrary median splits.
In summary, we will examine the relations between three different goal orienta- tions and students’ motivational beliefs, self-regulated learning, and academic performance. We will consider three measures of students’ motivational beliefs as outcomes of the different goal orientations: task value, self-efficacy, and test anxi- ety. We have included two measures of self-regulated learning, self-reported cognitive strategy use and regulatory strategy use. Finally, we include students’ course grades as an index of academic performance. We will address the following specific questions and hypotheses.
(1) What is the relation between adoption of a learning goal orientation and stu- dents’ self-efficacy, task value, anxiety, cognitive strategy use, self-regulation, and
THEt?ELATlONBETWEENGOALORlENTATlON 217
academic performance? Given all the research on the positive effects of mastery goal orientation, we expect that a mastery goal orientation will be positively related to all these outcomes, except for test anxiety, where a negative relation should be observed.
(2) What is the relation between adoption of a relative ability goal orientation and the same set of outcomes? Given the results and logic of a self-regulated leam- ing perspective (e.g., Bouffard et al. 1995; Butler & Winne 1995; Elliot & Harackiewicz 1996); Pintrich & Garcia 1991), we expected that a relative ability ori- entation would be positively related to all the outcomes, including anxiety, although this is counter to other findings in the goal theory tradition (Ames & Archer 1988; Meece & Holt 1993).
(3) What is the relation between adoption of an extrinsic orientation and the same set of outcomes? Given previous findings (e.g., Pintrich & Garcia 1991) and general theories about intrinsic and extrinsic motivation (e.g., Deci & Ryan 1985), it was expected that extrinsic orientation would be negatively related to the out- comes, except for anxiety which should show a positive relation to extrinsic goal orientation.
(4) What are the interactions between these different goal orientations and the outcomes? Given the diversity of the findings in the literature for interactions, the exact predictions of some of the interactions between goal orientations are not straightforward. However, following the logic of our main effects, we expected that students who were high in both mastery and relative ability goal orientations would show more positive patterns of motivation, self-regulation, and perfor- mance. Given the previous findings on mastery and extrinsic goal orientations (e.g., Pintrich & Garcia 1991), we expected that students who were high in mastery and low in extrinsic goal orientations would show the most adaptive patterns of outcomes. Finally, we did not have any specific predictions in terms of the interac- tion between relative ability and extrinsic goal orientations.
(5) Questions l-4 were the main focus of this study, however, given that there are potential grade and gender differences in some of these relations between goal orientation, motivation, and self-regulated learning (Pintrich & Schunk 1996), we also included grade and gender as controls in our analyses. Finally, a secondary aspect of our study involved replicating our findings across three dif- ferent academic subject areas. Most models of self-regulated learning assume that the relations between these constructs will be similar across subject areas. How- ever, given the recent arguments for discipline differences in classroom contexts and instruction (e.g., Grossman & Stodolsky 1995; Stodolsky & Grossman 1995), there could be domain or discipline differences in the relations between goal ori- entation, motivation, and self-regulation. It is important to examine the possibility of different relations obtained in different domains in order to estab- lish the generalizability of our models of self-regulated learning. Accordingly, we tested our model in three different academic subject areas of math, English, and social studies as an attempt to demonstrate the replicability and generalizability of our findings.
218 LEARNINGANDlNDlVlDlJAL DIFFERENCES VOLUME8.NUMEER3.1996
METHOD
PARTICIPANTS
Participants in the study were 434 seventh and eighth grade students from a jun- ior high school (7-9th grades) in a working class suburb of a midwestern city. The sample consisted of 225 females and 209 males ranging in age from 11 to 15 years, with an overall mean age of 12.6 years (SD = .66). The majority of subjects (95%) were Caucasian. All participants were enrolled in classes for mathematics, English, and social studies.
PROCEDURE
Data for this study were collected at the beginning of the school year (October/ Time 1) and again at the end of the school year (June/Time 2). All seventh and eighth grade students present in school the days questionnaires were adminis- tered participated in the study, although only those who had valid data for all of the outcome measures at both times of data collection were included in the present analyses. After a short set of directions were given and any questions answered, all items were read aloud by research assistants to whole classes of students.
MEASURES
Goal Orientation. Students completed an adapted version of the Motivated Strate- gies for Learning Questionnaire (MSLQ, see Pintrich & De Groot 1990; Pintrich, Smith, Garcia, & McKeachie 1993). The MSLQ is a self-report Likert-scaled ques- tionnaire (1 = strongly disagree to 7 = strongly agree) that assesses student moti- vation and cognition, including task value, self-efficacy, test anxiety, cognitive strategy use and regulatory strategy use. Students were presented with a descrip- tor and then rated themselves on that item once for each of four different subject areas including math, English, social studies and science.*
Students’ goal orientation was assessed using three different scales focusing on learning goal orientation, extrinsic goal orientation, and relative ability goal orien- tation. These scales were adapted from the Patterns of Adaptive Learning Survey (PALS, Midgley et al. 1996). The learning goal orientation scale consisted of six items such as, “In this class, understanding the work is more important to me than the grade I get.” Cronbach’s alphas for this scale across the three domains and Time 1 and Time 2 ranged from .69 to .76. The five items in the extrinsic goal scale consisted of items such as, “The main reason I do my work for this class is because we get grades.” Cronbach’s alphas for this scale ranged from .61 to .70. Finally, the five items in the relative ability goal scale consisted of items such as, “In this class, I like to show my teacher I’m smarter than the other kids.” Cronbach’s alphas for this scale ranged from .75 to .79.
Motivational Beliefs and Self-Regulated Learning. There were five motivational and learning strategy scales adapted from the MSLQ. The task value scale was com- posed of six items such as, “I like what I am learning in this class,” and Cron-
THEt?ELATlONBETWEENGOALORlEMATlON 219
bath’s alphas ranged from .75 to ~32. The self-efficacy scale included four items such as, “I’m certain I can understand the ideas taught in this course,” and indi- cated students’ beliefs about how capable they were of understanding and doing the work for each subject area. Cronbach’s alphas for this scale across the three domains and Time 1 and Time 2 ranged from .79 to .87. The four test anxiety items asked students about affective and physical symptoms of anxiety during tests, including, “I worry a great deal about tests in this class.” Cronbach’s alphas for this scale ranged from .74 to .81. The cognitive strategy use scale included nine items that asked students about different learning strategies in which they engaged in order to learn the material for each subject. The strategies included organizational, rehearsal, and elaboration strategies, e.g., “When I study for this class, I put important ideas into my own words.” Cronbach’s alphas for this scale ranged from .86 to .88. Finally, the seven regulatory strategy items asked students about strategies they might use to monitor and control their learning, e.g., “When I’m reading for this class I stop once in a while and go over what I have read.” Cronbach’s alphas for this scale ranged from .64 to .72.
Classroom Academic Performance. Students’ grades within each subject area from the first and second semesters (Time 1 and Time 2) were collected from school records. Marks were converted from letter grades to a thirteen point scale from “F” equal to 0 to “A+” equal to 12. For each subject area, grades were standard- ized within classrooms to control for differences in teachers’ grading practices.
RESULTS
DESCRIPTIVE RESULTS
For each of the three subject areas, Table 1 presents the means and standard deviations for the measures of students’ goal orientation, motivational beliefs, self- regulated learning and performance at Time 1 and Time 2. Results presented in Table 1 suggest that, within each time point, students’ goal orientations, motiva- tion, and self-regulation across different subject areas were quite similar to each other. In addition, the means and standard deviations were similar to those reported in other studies of junior high school students (i.e., Pintrich & De Groot 1990; Pintrich et al. 1994).
Tables 2a, 2b, and 2c presents the zero-order correlations between the measures of students’ goal orientation, motivational beliefs, self-regulated learning and per- formance for the three subject areas at Time 1 and Time 2. In terms of the relations among the three goal orientations, the results were similar across time and the three academic domains, suggesting some consistency across domains. There was some multi-collinearity between the different goal orientations with correlations ranging from -.30 to .31 within one measurement time. Learning and relative abil- ity goal orientation were always positively correlated, correlations ranged from .22 to .31. Learning and extrinsic goal orientation were always negatively corre-
TA
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Rel
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4.78
1.
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4.73
4.60
1.
37
4.56
3.53
1.
19
3.49
.I5
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5.26
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3.73
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3.58
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5.09
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4.67
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5.39
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224 LEARNlNGANDlNDlVlDUAL DIFFERENCES VOLUME8.NLlMBER3.1996
lated with r‘s between -.19 and -.30. Relative ability and extrinsic goal orientation (r’s between .18 and .23) were always positively related to each other.
In general, the zero-order correlations showed the expected relations between goal orientations and the outcomes of motivation, self-regulated learning and per- formance (see Table 2). Learning goal orientation was positively related to adaptive motivational beliefs and self-regulated learning, although there was no relation between learning goal orientation and text anxiety and only small rela- tions to performance. Relative ability goal orientation, as predicted, was positively related to students’ motivational beliefs (except for anxiety where there was no relation) and positively with students self-regulated learning and performance. Also as predicted, extrinsic goal orientation was negatively related to students’ efficacy and value, their self-regulated learning, and performance. Finally, as expected extrinsic goal orientation was positively related to students’ level of test anxiety. Again, these findings were fairly similar across the three academic domains.
PREDICTING MOTIVATIONAL BELIEFS AND SELF-REGULATED LEARNING AT TIME 1
In order to investigate our primary research questions, we next computed a set of regression equations with students’ Time 1 motivational beliefs, self-regulated learning and performance as outcome measures. The predictors for each of these equations were the three Time 1 goal orientation variables, learning goal, relative ability, and extrinsic goal. In addition, two dichotomous variables representing students’ gender (males coded as 0), and grade level (7th graders coded as 0) also were included to control for any gender and grade level differences. Finally, in order to address research question four and examine the interactions between the goal orientation variables, we included three additional variables representing the cross product term among each of the goal orientation measures. Hence, terms representing the interaction of learning goal orientation with relative ability goal orientation, and with extrinsic goal orientation, and one representing the interac- tion between relative ability and extrinsic goal orientations were included for a total of eight predictors for each equation. In order to reduce the multicollinearity among these terms and the main effect goal orientation measures, the interaction terms were created and subsequent analyses completed after centering the three goal orientation measures (Jaccard, Turrisi, & Wan 1990). Six regressions were computed for each of the three subject areas, giving a total of 18 equations.2
Generally, results across subject areas were quite similar, providing replication for our findings. Therefore, we will present results for each subject area together, noting differences among the domains only when applicable. With respect to the outcome variables, we will discuss the equations predicting students’ motiva- tional beliefs, then the self-regulated learning outcomes, and finally the regressions predicting students’ classroom performance.
Task Value. The eight predictors together explained a significant amount of the variance in students’ value for classroom tasks in math, F (8,425) = 45.72, ~7 < .OOl, English, F (8,425) = 45.76, p < .OOl and social studies, F (8,425) = 53.98, p < .OOl.
THEREL4TlONBETWEENGOALORlENTATlON 225
Within each subject area, almost 50% of the variance in task value could be explained (see Table 3). The single best predictor of task value was learning goal orientation which explained almost 36% of the variance in task value alone. How- ever, both relative ability and extrinsic goal orientations were also significant pre- dictors of task value. As predicted, students reporting a greater focus on relative ability goal tended to report higher levels of task value. On the other hand, stu- dents with a greater extrinsic goal focus tended to report lower task values across each subject area (see Table 3).
In English, p = -.09, p < .05, and social studies, p = -.14, p < .OOl, there was a sig- nificant interaction between the learning and relative ability goal orientations (see Table 3). Regression equations were graphed in order to interpret the nature of these interactions. In English and social studies, the significant interaction indi- cated, contrary to our hypothesis, that the positive impact of a learning goal orientation was greater at lower levels of relative ability, while this impact was diminished at higher levels of relative ability goal orientation. A similar relation was also found in math, but the differential impact of a learning goal at different levels of relative ability did not reach significance (p = -.05).
Self-E11 icacy. The eight predictors together explained a significant portion of the variance in students’ self-efficacy in math, F (8,425) = 15.85, p < .OOl, English, F (8,425) = 26.02, p < .OOl, and social studies, F (8,425) = 28.17, p < .OOl. Approxi- mately 30% of the variance in self-efficacy was explained in English and social studies, while about 25% of the variance in self-efficacy was explained in math (see Table 3). A learning goal orientation was the single best predictor of self-effi- cacy with standardized coefficients ranging from .34 in math to .41 in English. Thus, as expected, students who reported a greater endorsement of a learning goal also tended to report higher levels of self-efficacy. Relative ability goal orien- tation was also an important predictor of self-efficacy across each subject, with a greater relative ability orientation predicting higher levels of self-efficacy as hypothesized. Extrinsic goal orientation was also significantly related to self-effi- cacy. However, students reporting a greater extrinsic goal focus tended to have lower levels of self-efficacy as we predicted. In social studies, but not in math or English, there was also a significant effect of gender on self-efficacy, with boys reporting higher levels of self-efficacy than girls.
As with task value, the interaction between learning and relative ability goal ori- entations was significant for English, p = -.lO, p < .Ol, and social studies, p = -.15, p < .OOl, but not for math (see Table 3). The nature of these interactions showed the same relation as the result for task value. That is, within English and social studies, the impact of a learning goal was moderated by relative ability goal orientation. When relative ability was high, increases in learning goal orientation did not have as positive an impact on self-efficacy as when relative ability goal orientation was low. Again, this was contrary to our hypothesis that there would be a positive multiplicative effect of having both relative ability and learning goal orientations.
Test Anxiety. Approximately 10% of the variance in test anxiety was explained jointly by the eight predictors. Although this amount was less than the variance
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THERELATlONBElWEENGOALORiE~ATlON 227
explained in task value and self-efficacy it was still significant across math, F (8, 425) = 6.01, p < .OOl, English, F (8,425) = 7.49, p < .OOl, and social studies, F (8,425) = 7.90, p < .OOl. Unlike task value and self-efficacy, neither learning goal nor rela- tive ability orientation predicted a significant amount of the variance in test anxi- ety. However, extrinsic goal orientation was an important predictor of test anxiety, with students who reported a greater extrinsic goal focus likely to report higher levels of test anxiety as we had predicted (see Table 3). Further, in math and social studies, girls tended to report higher levels of test anxiety than boys. There was no difference between the anxiety level reported by girls and boys with respect to English.
In social studies only, the interaction between learning and extrinsic goal orien- tations was significant, j3 = .13, p < .Ol. This interaction, which was counter to our prediction, indicated that the positive relation between extrinsic goal orientation and test anxiety was stronger at high levels of learning goal than when a learning goal was weakly endorsed.
Cognitive Strategy Use. Within math, F (8,425) = 28.01, p < .OOl, English, F (8,425) = 30.96, p < .OOl, and social studies, F (8,425) = 29.52, p < .OOl, about 35% of the vari- ance in students’ cognitive strategy use was accounted for by the eight predictors as a group. The strongest predictor of students’ cognitive strategy use was leam- ing goal orientation, with approximately 25% of the variance in cognitive strategy use in math, English and social studies explained by this variable alone. As expected, students who adopted a learning goal were more likely to report more use of cognitive strategies. Relative ability goal orientation also explained a signif- icant portion of the variance in cognitive strategy use with higher levels of rela- tive ability positively related to the use of cognitive strategies as expected (see Table 3). In addition, gender was a significant predictor of cognitive strategy use in each subject area, and grade level was a significant predictor in math and social studies, but not in English (see Table 3). Overall, girls tended to report higher lev- els of cognitive strategy use than boys, and eighth graders reported higher levels of strategy use than seventh graders.
The only significant interaction term was between relative ability and extrinsic goal orientations for social studies, p = .08, p < .05. We did not make any specific predictions for this two-way interaction, but the graphing of the interaction term showed that the positive relation between relative ability and cognitive strategy use was stronger when extrinsic goal orientation was high.
Regulatory Strategy Use. Similar to cognitive strategy use, about 35% of the total variance in self-regulation was explained by all eight predictors. This was equally true across math, F (8, 425) = 25.50, p < .OOl, English, F (8, 425) = 26.58, p < .OOl, and social studies, F (8, 425) = 23.22, p < .OOl. Again, the strongest predictor of self-regulation was learning goal orientation, with a greater learning goal focus associated with higher levels of self-regulation as predicted. The next strongest predictor in each subject area was extrinsic goal orientation, and as hypothesized, students with a greater focus on extrinsic goal tended to report lower levels of self-regulation (see Table 3). Relative ability goal orientation was also a significant
228 LEARNINGANDINDIVIDUAL DIFFERENCES VOLUME 8, NUMBER 3.1996
predictor of self-regulation, with a greater relative ability focus associated with higher levels of self-regulation.
Finally, in English, p = -.lO, p < .05, but not in math or social studies, the interac- tion between learning and extrinsic goal orientations was significant. This effect was in line with our predictions and revealed that the impact of having a learning focus on self-regulation was more strongly positive when a student did not also strongly endorse an extrinsic goal orientation.
Classroom Performance. The final three regressions for the Time 1 outcomes pre- dicted students’ classroom performance or grades. For these equations, the total amount of variance explained was similar to test anxiety, but was much lower than the other equations although it was still significant for math, F (8,425) = 2.44, p < .05, English, F (8, 425) = 5.81, p < .OOl, and social studies, F (8, 425) = 5.06, p < ,001 (see Table 3). In each subject area, both relative ability and extrinsic goal orientation predicted a significant amount of the variance in classroom perfor- mance (see Table 3). While the standardized coefficients for relative ability were positive, indicating that increases in relative ability were associated with increases in performance, the coefficient for extrinsic goal orientation was nega- tive, indicating that increases in an extrinsic goal focus were associated with low- ered classroom performance in line with our predictions.
Finally, there was one significant interaction across the three domains. The inter- action between relative ability and extrinsic goal orientations was significant in English, p = .ll, p < .05, showing that the negative impact of having an extrinsic goal focus on performance was greater when relative ability was also high.
PREDICTING MOTIVATIONAL BELIEFS AND SELF-REGULATED LEARNING AT TIME 2
We ran similar analyses for outcomes collected at Time 2. In addition, for these analyses, students’ motivational beliefs, self-regulation and performance mea- sures from Time 1 were used as predictors in the appropriate regression equations. Thus, these analyses allow us to examine the influence of the goal ori- entation variables after accounting for students’ previous motivational beliefs, self-regulation or classroom performance. Again, results across subject areas were similar and will be presented together, with important differences among the sub- ject areas noted.
Task Value. Table 4 presents the standardized coefficients for the three equations predicting students’ task value at Time 2. In each of the three domains, approxi- mately 50% of the variance in task value was explained by the nine predictors, F (9, 424) = 52.95, p < .OOl, F (9,424) = 41.20, p < ,001, and F (9, 424) = 46.81, p < .OOl, for math, English and social studies, respectively. In each equation, learning goal orientation was the single best predictor of task value, explaining close to 25% percent of the total variance in task value even after accounting for the other pre- dictors (see Table 4). Further, learning goal orientation explained at least twice as much variance as Time 1 task value, although this variable was still a significant predictor within each subject area. Accordingly, as we expected, endorsing a
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230 LEARNING AND /ND/V/DUAL DIFFERENCES VOLUMEB,NUMBER3.1996
learning goal was more important for high levels of task value, over and above initial levels of task value. Relative ability and extrinsic goal orientations were also significantly related to task value in each subject area, although relative abil- ity was positively associated with task value and extrinsic goal negatively related to task value as predicted. None of the interaction terms in these equations reached significance.
Self-Efficacy. A significant portion of students’ self-efficacy for academic tasks was explained in math, F (9, 424) = 29.81, p < ,001, English, F (9, 424) = 28.52, y i .OOl, and social studies, F (9, 424) = 29.47, p >OOl. Across each subject, self- efficacy at Time 1 and learning goal orientation were both strong positive predic- tors of self-efficacy at Time 2 (see Table 4). In English and social studies, extrinsic goal and relative ability were also strong predictors of self-efficacy. In these two subjects, a stronger endorsement of an extrinsic goal orientation was associated with lower self-efficacy, while a greater relative ability focus was associated with higher levels of self-efficacy as hypothesized. In math, neither extrinsic goal nor relative ability orientation was a significant predictor of self-efficacy. However, in math, there was a significant difference in the self-efficacy reported by seventh and eighth graders, with the latter generally reporting higher levels of self-effi- cacy (see Table 4). Girls reported lower self-efficacy than boys in both math and social studies, but not in English.
Finally, the interaction between learning goal and extrinsic goal orientations was a significant predictor of self-efficacy in English only, p = -.13, p < .Ol. This interaction indicated that, while increases in learning goal orientation were gener- ally associated with higher levels of self-efficacy, this relationship was not as strong when extrinsic goal orientation was high. That is, as we predicted, at high levels of extrinsic goal focus, increases in learning goal orientation did not lead to as great an increase in self-efficacy as when extrinsic goal was low.
Test Anxiety. In math, F (9,424) = 25.15, p < ,001, English, F (9,424) = 27.34, ~7 < ,001, and social studies, F (9, 424) = 23. 81, p < ,001, a significant portion of the variance in test anxiety was explained by the predictors as a group. Across each subject area, the strongest predictor of students’ test anxiety at Time 2 was stu- dents’ report of their test anxiety at Time 1 (see Table 4), suggesting that test anxi- ety is more trait-like and less related to goal orientation. However, extrinsic goal orientation was also a strong positive predictor of students’ test anxiety, even after accounting for previous test anxiety (see Table 4), with students who were more concerned about grades, being more anxious as we predicted. Moreover, gender was a significant predictor of students’ level of test anxiety, with girls reporting more test anxiety than boys in all subjects. Finally, in English and social studies but not in math, eighth grade students reported higher level of test anxi- ety than seventh graders. None of the interaction terms were significant in pre- dicting test anxiety at Time 2.
Cognitive Strategy Use. Over 40% of the variance in cognitive strategy use at Time 2 was explained in math, F (9,424) = 39.10, p < .OOl, English, F (9,424) = 40.70, p <
THERELATIONBETWEENGOAL ORIEMATION 231
.OOl, and social studies, F (9, 424) = 36.20, p < .OOl. Although Time 1 strategy use was a strong predictor of Time 2 strategy use, it was not as strong a predictor as learning goal orientation within any of the three subjects areas (see Table 4). On average, as we hypothesized, students who reported a high learning goal orienta- tion reported a higher level of cognitive strategy use. Relative ability was also related to strategy use, with a greater relative ability focus associated with increased use of cognitive strategies as predicted. Further, paralleling the result at Time 3, students in the eighth grade reported a higher level of cognitive strategy use than seventh graders across all subjects areas. In math only, girls reported higher levels of strategy use than boys.
Finally, several of the interaction terms associated with cognitive strategy use were significant. In math, l3 = -.09, p < .05, and social studies, /3 = -.08, p < .05, the interaction between learning and extrinsic goal orientations was significant. As predicted, these interactions indicated that in math and social studies, a learning goal orientation had a greater positive influence on cognitive strategy use when extrinsic goal orientation was not highly endorsed. The interaction between learn- ing and relative ability goal orientation was also significant for English, /3 = -.OB, p < .05, and social studies, p = -.08, p < .05, providing evidence that in these subjects, the influence of a learning goal focus on cognitive strategy use was tempered by relative ability. That is, strongly endorsing a relative ability goal weakened the positive impact of learning goal orientation on cognitive strategy use which was contrary to our predictions.
Regulatory Strategy Use. Within math, F (9,424) = 43.54, p < .OOl, English, F (9,424) = 44.06, p < .OOl, and social studies, F (9, 424) = 51.25, p < .OOl, about 50% of the variance in regulatory strategy use was accounted for by the nine predictors as a group. Students’ previous level of regulatory strategy use and their learning goal orientation were both strong positive predictors of Time 2 regulatory strategy use. Although somewhat weaker than learning goal orientation and Time 1 regula- tion, relative ability and extrinsic goal orientation were also significant predictors of regulatory strategy use. Again, as we predicted, extrinsic goal orientation was negatively related to regulation, while a greater relative ability focus tended to be associated with higher levels of self-regulation. Also, eight graders were more likely, on average, to use these strategies than seventh graders across all subjects.
Across all three subject areas, the interaction between learning and extrinsic goal orientations was significant. Standardized coefficients for these interactions were -.08, -.09, and -.07, p’s < .05, for math, English and social studies, respectively. Thus, in all subjects and as we hypothesized, the positive relation between leam- ing goal orientation and self-regulation was stronger when extrinsic goal orientation was low, than when it was high.
Classroom Performance. Although the nine predictors together explained a signif- icant portion of the variance in math, F (9,424) = 90.93, p < .OOl, English, F (9,424) = 25.32, p < .OOl, and social studies, F (9, 424) = 83.53, p < .OOl, the results for Time 2 classroom performance changed more than the results for any of the other out- comes. In each of the three subject areas, Time 1 performance was without ques-
232 LEARNlNGANDlNDlVlDUALDIFFERENCES VOLUME 8, NUMBER 3.1996
tion the most important predictor of Time 2 performance (see Table 4). In math and social studies, previous performance accounted for well over half of the vari- ance in current performance even when accounting for the other variables in the equation. In English, the effect of previous performance was also very strong (p = .58), although weaker than in math and social studies. Hence, students’ academic performance was very stable from Time 1 to Time 2. In math and social studies but not in English, learning goal orientation was also an important positive pre- dictor of performance.
Finally, in English, p = -.12, p < .05, and social studies, p = -.07, p < .05, the inter- action between learning goal and extrinsic goal orientations was also a significant predictor of performance (see Table 4). As with the previous interactions between these two goal orientations and in line with our predictions, this interaction showed that the positive impact of a learning goal focus on performance was greater when extrinsic goal orientation was low.
DISCUSSION
The purpose of the current study was to examine the role of three different goal orientations in predicting motivational, cognitive and performance outcomes. The results of the study extend our understanding of goal theory by demonstrating the importance of separating out the effects of a relative ability goal orientation from an extrinsic goal orientation and a learning goal orientation. In addition, given that the data were collected from junior high school students in their actual classroom settings and students reported on their beliefs about their academic work in different disciplines, the data, although correlational, are higher in eco- logical validity than experimental data. We discuss the findings in terms of our five general questions from the introduction.
First, the results indicate that the goal orientation students endorse in the class- room have important implications for their motivational beliefs, self-regulation and classroom performance. Further, these findings were present at Time 1, and at Time 2 when students’ previous level of the outcome variable was taken into account. The strongest and most consistent relations found among the goal orien- tation variables and the outcome measures involved the impact of a learning goal orientation. As with previous studies (Ames 1992; Meece et al. 1988; Meece & Holt 1993; Pintrich & Garcia 1991), students who adopted a learning goal orientation tended to have more adaptive motivational and strategy use outcomes. Specifi- cally, learning goals were positive predictors of task value, self-efficacy, and both cognitive and self-regulatory strategy use in all subjects at Time 1. Students’ endorsement of a learning goal orientation continued to be the strongest predictor of these outcomes at Time 2, even when accounting for students’ Time 1 score on the outcome measure.
THERELATIONBETWEENGOAL ORIENTATION 233
Interestingly, learning goal orientation was not a strong predictor of students’ classroom grade in any subject at Time 1, but did predict performance in math and social studies at Time 2, even with prior Time 1 achievement taken into account. The fact that we controlled for initial levels of these outcomes and still found a sig- nificant relation between them and later learning goals suggests that these motivational, cognitive, and performance outcomes can be influenced by goal ori- entation, the outcomes are not just reflective of stable individual differences. In contrast, a learning goal orientation failed to predict students’ level of test anxiety at either time point in any subject area, suggesting that test anxiety is not related to a learning goal orientation and may be a more stable individual difference. In summary, and in line with previous research in the normative goal theory tradi- tion, a learning goal orientation, where the students focus on mastery of the material to be learned, promotes adaptive motivational beliefs such as higher effi- cacy and task value as well as adaptive levels of deeper cognitive engagement and higher levels of self-regulation.
Our second question concerned the role of a relative ability goal orientation, In this study our relative ability goal orientation reflected how strongly students adopted goals related to doing better than others. The items for this scale were similar to an approach performance orientation in the work by Elliot and Harack- iewicz (1996) where students are actively trying to do better than others and are not afraid to fail or are trying to avoid looking bad. In contrast to the normative model of goal theory where a performance goal orientation should have negative effects (Ames 1992; Dweck & Leggett 1988), our results indicated that adopting a relative ability goal orientation resulted in positive academic outcomes in motiva- tion, cognition, and performance. Relative ability goal orientation positively predicted students’ task value, self-efficacy, and cognitive and self-regulatory strategy use. These relations, furthermore, were significant at Time 1, and at Time 2 even when accounting for students’ Time 1 outcomes. In addition, at Time 1, stu- dents’ level of relative ability goal orientation positively predicted classroom performance. Thus, assuming a relative ability goal orientation in the classroom had a positive relation with motivational and cognitive processes as well as actual performance, independent of other goal orientations. This finding helps to clarify the confusion in the literature regarding the positive or negative role of relative ability goal orientation (cf., Ames 1992; Dweck & Leggett 1998; Elliot & Harack- iewicz 1996; Meece & Holt 1993; Pintrich & Garcia 1991). Our correlational findings, along with Elliot and Harackiewicz’s (1996) experimental results, sug- gests that adopting a relative ability goal orientation does not automatically result in maladaptive motivational or cognitive outcomes and can actually be adaptive in the classroom context.
In terms of our third question, and in line with our predictions, extrinsic goal orientation was related to more negative and maladaptive patterns of motivation, cognition, and performance. Extrinsic goal orientation strongly predicted stu- dents’ level of test anxiety at both Time 1 and Time 2 with the adoption of extrinsic goals focused on obtaining grades being related to higher levels of anxiety. At the same time, students’ endorsement of an extrinsic goal orientation was negatively related to task value, self-efficacy and self-regulated learning. Students who
234 LEAARNINGANDlNDlVlDUAL DIFFERENCES VOLUME 8, NUMBER 3.1996
focused on extrinsic goals and purposes tended to report lower levels of utility and interest in the subject, lower levels of self-efficacy to do the task, and less self- regulated learning. Furthermore, at Time 1 extrinsic goal orientation also was neg- atively related to performance in the classroom, with students who were more focused on extrinsic goals and purposes more likely to receive poorer grades than students who did not endorse an extrinsic goal orientation. These findings are in line with intrinsic motivation theory (Deci & Ryan 1985) which predicts that a sin- gular focus on extrinsic goals is detrimental to student motivation, self-regulation, and academic performance.
Taken together, these findings help to clarify current goal theory formulations by suggesting the need to distinguish between a relative ability goal orientation and an extrinsic goal orientation focused on grades. This is in line with the recent work of Midgley and Maehr and their colleagues (e.g., Midgley et al. 1996). Based on our correlational classroom results and Elliot and Harackiewicz’s (1996) exper- imental results, it appears that a relative ability goal orientation, where students are focused on doing better than others, does not necessarily have to lead to mal- adaptive motivation, cognition, or performance. In particular, in a traditional classroom context where the classroom tasks (e.g., worksheets) are often not that challenging or interesting (Doyle 1983; Pintrich et al. 1994) and may be “over- learned”, a relative ability goal orientation may help students regulate their moti- vation, cognition, and behavior by giving them an external criteria (doing better than others) by which to regulate themselves (Butler & Winne 1995). In addition, adopting a relative ability goal orientation focused on competing with others could function as a motivational strategy of self-regulation (Garcia & Pintrich 1994) which students use to motivate themselves in the face of easy, boring, or uninteresting tasks (cf., Elliot & Harackiewicz 1996; Sansone, Weir, Harpster, & Morgan 1992). The positive results from the literature on the use of competition between cooperative groups in a classroom (e.g., Slavin 1995) would also be in line with this interpretation of motivation through competition with others. At the same time, our results must be interpreted with caution because we did not con- trol for differing levels of student achievement. The positive effects of relative ability goal orientation found in this study do not consider the possibility that for very low achieving students, who cannot compete with others on even the simpler academic tasks, may not benefit from adopting a relative ability goal orientation. Future research will have to investigate the possibility of this type of interaction.
Our fourth question concerned the interactions among the different goal orien- tations. Overall, there were not that many significant interactions across the two times and three subject areas. There were 108 interaction terms included in our regressions and 18 of these terms were statistically significant, although the actual size of the standardized coefficients were small. This represents 17% of the inter- actions terms, somewhat above a chance level of 5%, but not a large percentage. Given the rather small percentage of significant interactions and the relatively small size of the standardized coefficients, we do not want to overemphasize the importance of these interactions. At the same time, there are some consistent pat- terns in the interactions that are worth commenting on in terms of their implications for goal theory. For example, of the 18 significant interactions, 10
THEt?ELATlONBETWEENGOALORlE~AT~ON 235
involved an interaction ,between a learning goal and an extrinsic goal. In all 10 cases, the results were as predicted and in line with previous findings (Pintrich & Garcia 1991), that high levels of an extrinsic goal orientation dampened the posi- tive effect of adopting a learning goal orientation for efficacy, cognitive strategy use, self-regulation, and performance.
The learning goal by relative ability goal orientation interaction was significant six times and the results did not support our hypothesis that having high levels of both would be adaptive. Instead, it also appears that it is most adaptive, at least in terms of task value, efficacy, and cognitive strategy use, to endorse a learning goal orientation and not adopt a relative ability goal orientation. This finding is also counter to the findings of Bouffard et al. (1995) that found a positive multiplicative relation between high levels of mastery and performance goals. Taken together, these 16 interactions (out of 18 significant ones), along with the positive main effects of a learning goal orientation, suggest that the normative model of goal the- ory (Ames 1992) and intrinsic motivation theory (Deci & Ryan 1985) is correct in that having a high level of endorsement of a mastery, learning, and intrinsic orien- tation to school work is most adaptive for motivation, cognition, self-regulation, and performance in the classroom context.
Moreover, the lack of interactions in terms of strength and number suggests that while students may adopt multiple goals and show varying levels of endorsement of different goals, the main effects of different goal orientations are much stronger and more consistent. This suggests that there is a need for future research on how students actually coordinate the three different goal orientations and actively use them as they approach, engage in, and evaluate their performance on different academic tasks. This will require other types of more dynamic assessment instru- ments that tap into students’ actual and on-line goal coordination (Butler & Winne 1995), rather than the more static and independent measures of the three goal ori- entations used in the current study.
Finally, our fifth question concerned the secondary issues of grade, gender, and subject area differences in the relations among goal orientation, motivation, self- regulated learning, and performance. In terms of mean level differences by age and gender, there were 72 tests for main effects of gender and age, with 27 being significant (38%), but most of the effects were small. The two most consistent find- ings were that females reported higher levels of cognitive strategy use and test anxiety across the three subject areas. Females also reported lower levels of effi- cacy for social studies and math. In terms of grade differences, eighth graders were more likely to report higher levels of cognitive strategy use and self-regulation than seventh graders in all three subject areas. Although small, these findings sug- gest that gender and grade need to be considered in future studies. Moreover, we did not test for gender by goal orientation interactions in this study and future research will have to address whether the patterns of relations we found are simi- lar for both males and females. In terms of grade, the developmental and contextual differences between seventh and eighth grade are probably not great, but other research on goal orientations has shown important differences between elementary and secondary students, teachers, and school contexts (e.g., Midgley, Anderman, & Hicks 1995; Maehr & Midgley in press). Our conclusions are limited to middle school students in a fairly traditional junior high, there may be different
236 LEARNlNGANDINDIVlDUAL DIFFERENCES VOLUME 8. NUMBER 3,1996
patterns found in elementary schools or in middle schools where the school con- text and climate are dramatically different (see Maehr & Midgley in press).
In terms of the subject area differences, the main conclusion stemming from the current study is that the students’ goal orientations seemed to operate similarly across each of the three subject areas examined. In fact, there were no differences across subject areas in terms of which goals predicted which outcomes. If a partic- ular goal orientation was predictive of an outcome in math, it was also predictive of that outcome in English and social studies. Recent work has indicated that dif- ferent academic subjects represent different contexts which have implications for instruction (Grossman & Stodolsky, 1995; Stodolsky & Grossman, 1995). In our own work we have found differences in the mean levels of task value, self-efficacy, strategy use and self-regulation that students report across different school subject areas (Wolters & Pintrich, 1995). However, in this study the relations among goal orientation and students’ motivation, self-regulated learning, and performance were replicated across subject areas. Accordingly, although the actual levels of student motivation and cognition may vary by subject area, the general relations among the constructs seem similar and suggest that our models of self-regulated learning can apply and be generalized to different academic subject areas.
In conclusion, students’ goal orientations are related in predictable and consis- tent ways to motivational and cognitive processes and actual achievement in classroom settings. A general mastery or learning goal orientation is most adap- tive for motivation and learning, but a relative ability goal orientation can also be positive for middle school students. In contrast, an extrinsic goal orientation where students are only focused on grades does not seem to be adaptive for moti- vation and self-regulated learning. These results, when combined with the extant research on goal orientation and self-regulated learning, suggest that a coherent social cognitive model of goal orientation, motivation, and self-regulated learning is emerging that will be very fruitful and productive for future research on class- room learning.
ACKNOWLEDGMENTS: The data reported on in this article are part of the Compe- tence and Commitment Project at The Combined Program in Education and Psychology at The University of Michigan. The authors thank the following colleagues on this project: Eric Anderman, Anastasia Danos Elder, Teresa Garcia, Lynley Hicks, Barbara Hofer, Helen Patrick, Allison Ryan, and Tim Urdan. The authors also thank editors, Teresa Garcia and Frank Dempster, for their helpful comments on an earlier draft of this article.
NOTES
1. Although students responded to this questionnaire with respect to science, approxi- mately one-half of the eighth grade students currently were not enrolled in a science class due to budgetary considerations in the school district. Because questionnaires asked stu-
THERELATIONBETWEENGOAL ORIE~ATION 237
dents about their current beliefs, attitudes and behaviors, any analyses including science would have meant large amounts of missing data. To avoid this problem, only the subject areas of mathematics, English and social studies were included in our current analyses.
2. It also should be noted that we investigated the possibility of curvilinear relations in the data by including quadratic terms (created by squaring the main effect goal orientation measures) in a series of earlier equations. There were few reliable quadratic relations obtained in these analyses, so we focused on the regressions that included the two-way interaction terms.
REFERENCES
Ames, C. (1992). “Classrooms: Goals, structures, and student motivation.” Journal of Educu- tional Psychology, 84,261-271.
Ames, C., & J. Archer. (1988). “Achievement goals in the classroom: Students’ learning strategies and motivation processes.“ Journal of Educational Psychology, 80,260-267.
Anderman, E., & M. Maehr. (1994). “Motivation and schooling in the middle grades.” Review of Educational Research, 64,287-309.
Anderman, E. (1994). Achievement goals and the transition to middle grades school. Unpub- lished doctoral dissertation, University of Michigan, Ann Arbor.
Atkinson, J. (1957). “Motivational determinants of risk-taking behavior.” Psychological Review, 64‘359-372.
Bouffard, T., J. Boisvert, C. Vezeau, & C. Larouche. (1995). “The impact of goal orientation on self-regulation and performance among college students.” British Journal of Educa- tional Psychology, 65,317-329.
Butler, D., & I’. Winne. (1995). “Feedback and self-regulated learning: A theoretical synthe- sis.” Review of Educational Research, 65,245-281.
Deci, E. L., & R. M. Ryan. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.
Doyle, W. (1983).” Academic work.” Review of Educational Research, 53, 159-199. Dweck, C., & E. Leggett. (1988). “A social cognitive approach to motivation and personal-
ity.” Psychological Review, 95,256-273. Elliot, A., & J. Harackiewicz. (1996). “Approach and avoidance achievement goals and
intrinsic motivation: A mediational analysis.” Journal of Personality and Social Psychol- ogy 70,461-475.
Graham, S., & S. Golan. (1991). “Motivational influences on cognition: Task involvement, ego involvement, and depth of information processing.“ Journal of Educational Psychol- ogy, 83,187-194.
Grossman, I’., & S. Stodolsky. (1995). “Content as context: The role of school subjects in sec- ondary teaching.” Educational Researcher, 24 (S), 5-11.
Jaccard, J., R. Turrisi, & C. Wan. (1990). Interaction effects in multiple regression. Newbury Park, CA: Sage Publications.
Locke, E.A., & G. I’. Latham. (1990). A theory of goal setting and tusk performance. Englewood Cliffs, NJ: Prentice Hall.
Maehr, M. L., & C. Midgley. (1991). “Enhancing student motivation: A schoolwide approach.“ Educational Psychologist, 26,399-427.
-. (in press). Transforming school cultures. Boulder, CO: Westview Press.
238 LEARNINGANDINDIVIDUAL DIFFERENCES VOLlJtiE6.NUMBER3.1996
Maehr, M. L., & P. R. Pintrich. (Eds.). (1991). Advances in motivation and achievement: Vol. 7. Goals and self-regulatory processes. Greenwich, CT: JAI Press.
Meece, J., P. Blumenfeld, & R. Hoyle. (1988). “Students’ goal orientation and cognitive engagement in classroom activities.” Journal of Educational Psychology, 80, 514-523.
Meece, J., & K. Holt. (1993). A pattern analysis of students’ achievement goals. Journal of Educational Psychology, 85,582-590.
Midgley, C., E. Anderman, & L. Hicks. (1995). “Differences between elementary and mid- dle school teachers and students: A goal theory approach.” Journal of Early Adolescence, 25, 90-113.
Midgley, C., M. L. Maehr, L. Hicks, R. Roeser, T. Urdan, E. Anderman, & A. Kaplan. (1996). The patterns ofadaptive learning survey (PALS). Ann Arbor, MI: The University of Mich- igan.
Nicholls, J. (1984). “Achievement motivation: Conceptions of ability, subjective experience, task choice, and performance.“ Psychological Review, 91,328-346.
Nolen, S. (1988). “Reasons for studying: Motivational orientations and study strategies.” Cognition and Instruction, 5, 269-287.
Pintrich, P. R., & E. De Groot. (1990). “Motivational and self-regulated learning compo- nents of classroom academic performance.” Journal of Educational Psychology, 82,33-40.
Pintrich, P. R., & T. Garcia. (1991). “Student goal orientation and self-regulation in the col- lege classroom.” Pp. 371-402 in Advances in motivation and achievement: Goals and self regulatory processes (Vol. 7), edited by M. L. Maehr & P. R. Pintrich. Greenwich, CT: JAI Press.
Pintrich, P. R., R. Roeser, & E. De Groot. (1994). “Classroom and individual differences in early adolescents’ motivation and self-regulated learning.” ~ot~rnal of Early Adolescence, 24, 139-161.
Pintrich, P. R., & B. Schrauben. (1992). “Students’ motivational beliefs and their cognitive engagement in classroom tasks.” Pp. 149-183 in Student perceptions in the classroom: Causes and consequences, edited by D. H. Schunk & J. Meece. Hillsdale, NJ: Lawrence Erlbaum Associates.
Pintrich, P. R., & D. H. Schunk. (1996). Motivation in education: Theory, research, and applica- tions. Englewood Cliffs, NJ: Merrill Prentice-Hall.
Pintrich, P. R., D. A. F. Smith, T. Garcia, & W. J. McKeachie. (1993). “Reliability and predic- tive validity of the Motivated Strategies for Learning Questionnaire (MSLQ).” Educa- tional and Psychological Measurement, 53, 801-813.
Sansone, C., C. Weir, L. Harpster, & C. Morgan. (1992). “Once a boring task, always a bor- ing task? The role of interest as a self-regulatory mechanism.” Journal ofPersonality and Social Psychology, 63, 379-390.
Slavin, R. (1995). Cooperative learning. Boston: Allyn & Bacon. Stodolsky, S., & P. Grossman. (1995). “The impact of subject matter on curricular activity:
An analysis of five academic subjects.” American Educational Research Journal, 32, 227- 249.
Urdan, T. (1994). Extending goal theory: Examining social gouls and multiple goal profiles. Unpublished doctoral dissertation. University of Michigan, Ann Arbor.
Urdan, T., & M. Maehr. (1995). “Beyond a two-goal theory of motivation and achievement: A case for social goals.“ Review of Educational Research, 65, 213-243.
Wolters, C., & P. R. Pintrich. (1995, August). Gender and disciplinary differences in students’ motivation and self-regulated learning. Paper presented at the European Association for Research on Learning and Instruction (EARLI). Nijmegen, The Netherlands.
