The Effect of Achievement Goals: Does Level of Perceived Academic Competence Make a Difference

CONTEMPORARY EDUCATIONAL PSYCHOLOGY 22, 415–435 (1997)ARTICLE NO. EP970943

The Effect of Achievement Goals: Does Level of PerceivedAcademic Competence Make a Difference?

Avi Kaplan and Carol Midgley

University of Michigan

Researchers using a goal orientation framework have hypothesized that learning goalsare associated with adaptive patterns of behavior, regardless of the level of perceivedability. In contrast, perceived ability is hypothesized to moderate the relation betweenperformance goals and patterns of adaptive or maladaptive behavior. We examined thishypothesis in two samples of seventh grade middle school students, focusing on themath domain in one sample and on the English domain in the other. Using two differentstatistical methods, median split and multiple regression, we found only little supportfor the role of perceived competence as a moderator between performance goals andpatterns of behavior. Contrary to what has been suggested, we found some evidencethat perceived competence moderated the relation between learning goals and behavior.Implications of these findings for recent efforts to use goal theory to reform classroomsand schools are discussed. q 1997 Academic Press

Over the past decade, goal orientation theory has been used increasinglyby researchers to understand the role of psychological processes in elicitingdifferent cognitive and motivational patterns within the achievement domain(Ames, 1992; Dweck, 1986; Maehr, 1984; Nicholls, 1989). Two contrastingclasses of achievement goals have proven to be helpful in understandingadaptive and maladaptive patterns of behavior: ‘‘performance’’ goals and‘‘learning’’ goals. When oriented to performance goals, individuals are con-cerned about demonstrating their ability, and their focus is on being judgedable (or avoiding being judged as not able). When oriented to learning goals,individuals are concerned about developing their ability, and their focus ison gaining understanding, insight, and skill. As described by Elliott andDweck (1988),

. . . each of the achievement goals runs off a different ‘‘program’’ with differentcommands, decision rules, and inference rules, and hence, with different cognitive,affective, and behavioral consequences. Each goal, in a sense, creates and organizesits own world—each evoking different thoughts and emotions and calling forth differentbehaviors. (p. 11)

The analyses in this paper were conducted on data collected in a study supported by a grantfrom the Office of Educational Research and Improvement R117C80003 to Dr. Martin L. Maehr.

Address correspondence and reprint requests to Avi Kaplan, 1400 School of Education, Univer-sity of Michigan, 610 East University Ave. Ann Arbor, MI 48109-1259.

415

0361-476X/97 $25.00Copyright q 1997 by Academic PressAll rights of reproduction in any form reserved.

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416 KAPLAN AND MIDGLEY

A number of empirical studies have shown that a learning goal orientationis associated with more adaptive patterns of behavior, cognition, and affectthan is a performance goal orientation (e.g., Ames & Archer, 1988; Dweck &Leggett, 1988; Meece, Blumenfeld, & Hoyle, 1988; Nicholls, Patashnick, &Nolen, 1985). For example, the relationship between a learning goal orienta-tion and the use of effective cognitive learning strategies has been demon-strated in a number of studies in the field (e.g., Ames & Archer, 1988; Meeceet al., 1988; Nolen, 1988; Nolen & Haladyna, 1990). In a series of laboratorystudies, Dweck and her colleagues found that a learning goal orientation wasassociated with challenge seeking and high persistence (a mastery-orientedbehavior pattern), whereas a performance goal orientation was associatedwith avoiding challenge and low persistence (a helpless behavior pattern).However, Dweck in particular has noted that the influence of goals on subse-quent behavior depends on the level of an individual’s perceived ability (e.g.,Dweck, 1986; Dweck & Leggett, 1988). For example, in an experimentalstudy in which goals were manipulated, Elliott and Dweck (1988) found thatindividuals under the learning goals condition displayed an adaptive patternregardless of the level of their perceived skill at performing the task. However,the pattern exhibited by individuals under the performance goals conditiondepended on the level of their perceived skill. Individuals under the perfor-mance goals condition who assessed their skill as high exhibited adaptivepatterns; whereas those who assessed their skill as low exhibited maladaptivepatterns. Thus, perceived skill at performing the task moderated the relation-ship between performance goals and behavior.

There has been some debate about this hypothesized interaction betweenperceived skill and achievement goals. There are those who believe thatpursuing performance goals would result in negative outcomes regardless ofone’s level of perceived competence. Since goal orientation theory is nowbeing used as a theoretical framework to guide classroom and school reform(e.g., Ames, 1990; Maehr & Midgley, 1996), examining this question takeson new meaning.

Recently, Miller, Behrens, Greene, and Newman (1993) tested predictionsfrom Dweck’s goal orientation theory using a sample of undergraduate stu-dents in introductory statistics. They assessed perceived ability, learning andperformance goal orientation, values, and self-regulatory strategies. In orderto test the interaction between goals and perceived ability, they used a mediansplit to form high perceived ability and low perceived ability groups. Goalorientation groups were formed by determining which of the scores (learningor performance) was dominant for each individual. If students’ average learn-ing goal orientation score was higher than their average performance goalorientation score, they were classified as learning goal oriented. If their aver-age performance orientation score was higher than their average learningorientation score, they were classified as performance goal oriented. Millerand his colleagues performed a series of 2 1 2 analyses of variance to test

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417COMPETENCE AND ACHIEVEMENT GOALS

the predictions concerning the interaction of goal orientation and perceivedlevel of ability on persistence, valuing, and self-regulation. Students withperformance goals and high perceived ability did not exhibit more adaptivebehaviors than those with low perceived ability. On the contrary, studentswith performance goals and high perceived ability reported the lowest levelsof self-monitoring (one of the subscales assessing self-regulation). In addition,students with dominant learning goals and high perceived ability reportedhigher levels of self-monitoring than did students with low perceived abilitywith either goal orientation. The authors concluded that ‘‘additional researchdesigned to tease out the interaction is clearly needed’’ (p. 11).

That is the goal of our study. We add to the existing information aboutthe interaction of self-evaluation and goal orientation in several ways. First,our study was conducted in the field with a sample of middle school students.Second, the study includes two subject domains, math and English. We usedtwo subject domains to see if the findings generalized across domains. Third,we used two methods to assess the predicted interaction. We began by follow-ing the procedures used by Miller and his colleagues (median split), categoriz-ing students according to their dominant goal orientation and their level ofperceived academic competence. However, there are reasons to be concernedabout using a median split to transform these continuous variables into dichot-omous variables. First, the researcher imposes a decision, not necessarilysupported by theory, about the point of split that would represent the expectedqualitative difference between the two levels of the variable. Furthermore,by creating a dichotomous variable, much information and statistical powerare lost (Aiken & West, 1991). Second, the treatment of goal orientations asa categorical variable comprising one dimension is in opposition to contempo-rary perceptions of goals as orthogonal to each other. Indeed, goal theoryassumes that different goals can be pursued simultaneously and to varyingdegrees. Therefore, as a second step, we used multiple regression with interac-tion terms to test the theoretical predictions.

In the present study, we used a measure of perceived academic competence.This is related to, but somewhat different from, the measure of perceivedability used by Miller and his colleagues (1993). Their items asked studentshow their ability compared to that of others in the class, how much understand-ing they had of the concepts taught in the class, and how well they did inclass and expected to do in the future. Perceived academic competence inour study is akin to the measure used by Elliott and Dweck (1988) whichrefers to ‘‘perceived level of current skill’’ (p. 6). They manipulated students’beliefs about their skill on a specific task through feedback on their perfor-mance. Our measure of perceived competence included a series of questionsasking students whether they believed they could do the work in class.

The main purpose of the current study was to test whether perceived compe-tence moderates the relationship between goal orientation and patterns ofadaptive and maladaptive behavior. Our measure of adaptive behavior as-

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sesses the reported use of effective cognitive learning strategies. Our measureof maladaptive behavior assesses the use of strategies associated with a help-less pattern of behavior. While some of these strategies might be appropriatein certain situations (e.g., skipping the difficult questions in a standardizedtest), they reflect avoidance of challenge and effort and are not conducive tolearning. In addition, we report the relationships among goal orientation,perceived academic competence, and these different approaches to learningas a further test of goal orientation theory. Goal orientation theory predictsthat perceived competence will moderate the relationship of performancegoals, but not of learning goals, and behavior (Dweck, 1986). Miller et al.’s(1993) results point to the possibility that perceived competence moderatesthe relation of learning goals and behavior. Such a finding, if replicated, mightpoint to a need for reconceptualizing this aspect of the theory. It further mighthave implications for reform efforts that are based on goal orientation.

METHOD

Participants

Participants in this study were 229 seventh grade students in two middle schools in the sameschool district in southeastern Michigan. These middle schools serve students from grades sixthrough eight and are located near a major metropolitan area in a community that is primarilywhite and working class. Permission to participate was obtained from parents when studentswere in the sixth grade. Eighty percent of the students received permission to participate. Dueto a period of economic upheaval (the closing of several large automotive plants), 14% of theparticipating students moved away from the district between the sixth and the seventh grades.The final sample included 103 students (57% female; 43% male) in one school who were askedabout the math domain, and 126 students (52% female; 48% male) in the other school who wereasked about the English domain. In both schools, most students (81% in English and 82% inmath) attended ‘‘general’’ math and English classes, although some students were in ‘‘advanced’’classes and some in ‘‘basic’’ classes. T-tests investigating differences in math and languagescores on the Comprehensive Test of Basic Skills (CTBS) indicated that there were no significantdifferences between participants in the two schools.

Procedures

Surveys were read out loud to students by trained research assistants during one class period(approximately 40 min) in the fall of 1992. Students were assured that the information theyprovided would be confidential, and surveys were removed from the school sites after they werecompleted.

Measures

Scales included in the survey were from the Patterns of Adaptive Learning Survey (PALS)(Midgley, Maehr, & Urdan, 1993). The complete survey included 128 items which asked studentsabout their motivational orientations; academic self-beliefs; learning strategies; and perceptionsof their teachers’, parents’, and friends’ orientations to school. The present study used 27 itemscomprising five scales: learning goal orientation, performance goal orientation, perceived aca-demic competence, adaptive learning strategies, and maladaptive (helpless) learning strategies.Items were responded to on a 5-point Likert scale with 1 representing ‘‘not at all true of me’’and 5 representing ‘‘very true of me.’’ Items were phrased for math in one school and for Englishin the other school. All items, as well as indicators of the scales’ internal consistency, are reported

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in Table 1. Internal consistency was satisfactory for all scales (a § .74). In other studies thesemeasures have demonstrated both good internal consistency and construct validity (e.g., Midgley,Anderman, & Hicks, 1995; Midgley, Kaplan, Middleton, Urdan, Hicks, & Roeser, 1996; Mid-gley & Urdan, 1995).

Students’ scores on the CTBS for math and language were collected from school records andused as indicators of achievement.

RESULTS

Factor Analysis.

Factor analysis with an oblique rotation was conducted for the learninggoal orientation, performance goal orientation, and perceived competenceitems and for the adaptive and maladaptive learning strategies. Oblique rota-tion was chosen because the factors were expected to correlate with eachother. The results indicated that after rotation, the items used for each scaleloaded on distinct factors. Table 1 presents the items in each of the scales,the internal consistency of each scale, the means, the standard deviations, andthe factor loadings.

Correlations

Learning and performance goal orientations were moderately negativelycorrelated in both samples (r Å 0.20, p õ .05 in English and r Å 0.25, põ .01 in math). In Miller’s undergraduate sample, the correlation was 0.12.Perceived academic competence was positively correlated with learning goalorientation (r Å .48, p õ .01 in English and r Å .43, p õ .01 in math) anduncorrelated with performance goal orientation (r Å 0.04 in English and rÅ 0.15 in math). Adaptive learning strategies correlated negatively withmaladaptive learning strategies and more highly in English than in math (rÅ 0.46, p õ .01 in English and r Å 0.24, p õ .01 in math). Compatiblewith previous research, learning goal orientation correlated positively withadaptive learning strategies (r Å .60, p õ .01 in English and r Å .61, p õ.01 in math) and negatively with maladaptive learning strategies (r Å 0.59,p õ .01 in English and r Å 0.53, p õ .01 in math). Similar patterns wereobserved for perceived academic competence (for adaptive learning strategies:r Å .48, p õ .01 in English and r Å .27, p õ .01 in math; for maladaptivelearning strategies: r Å 0.66, p õ .01 in English and r Å 0.55, p õ .01 inmath). Performance goal orientation correlated positively with maladaptivelearning strategies (r Å .24, p õ .05 in English and r Å .46, p õ .01 inmath), however, was uncorrelated with adaptive learning strategies (r Å 0.08in English and r Å 0.13 in math).

Test of Theoretical Predictions

Replicating the procedures used by Miller and his colleagues (1993), wefirst split the continuous measure of perceived academic competence at themedian (English: 3.86; math: 3.80) to create a dichotomous variable represent-

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TABLE 1DESCRIPTIVE STATISTICS, INTERNAL CONSISTENCY, AND ITEM LOADINGS OF SCALES USED IN STUDY

English Math

Perceived academic competence

Mean 3.80a 3.65Standard deviation .80 .84Skewness 0.78 0.55Cronbach’s a .83 .85

Some of the work we do in English/math is too difficult for me. 0.76b 0.72I’m certain I can master the skills that are taught in English/math class

this year. .60 .75No matter how hard I try, there is some work in English/math class I’ll

never understand. 0.77 0.67I can do almost all the work in English/math if I don’t give up. .58 .72I’m certain I can do a good job on the assignments and tests in English/

math this year. .54 .81If I have enough time, I can do most of the work in English/math class. .61 .51I’m certain I can do even the most difficult work in English/math. .65 .72

Learning goal orientation

Mean 3.15 3.19Standard deviation .95 .91Skewness 0.19 0.12Cronbach’s a .81 .81

I like English/math work that I’ll learn from, even if I make a lot ofmistakes. .65 .71

I feel most successful in English/math when I learn something I didn’tknow before. .72 .74

I like English/math the best when it really makes me think. .72 .67Understanding the work in English/math is more important to me than

the grade I get. .74 .78The main reason I do my work in English/math is because I like to learn. .77 .65

Performance goal orientation

Mean 2.87 2.84Standard deviation 1.15 1.00Skewness .28 .36Cronbach’s a .86 .84

I would feel successful in English/math if I did better than the otherstudents in my class. .69 .66

I would feel really good if I were the only one who could answer theteacher’s questions in English/math. .80 .72

I’d like to show my teacher that I’m smarter than the other kids in myEnglish/math class. .77 .83

I wish I were smarter than most of the kids in my English/math class. .88 .80I’d like to show my parents that I’m smarter than the other kids in my

English/math class. .82 .84

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TABLE 1—Continued

English Math

Adaptive learning strategies

Mean 3.04 3.19Standard deviation .86 .77Skewness 0.31 .03Cronbach’s a .78 .74

When I do my English work/When working on a math problem, I try tothink about how it connects with something in everyday life. .62 .66

After I write something the first time in English, I keep working on it tomake it better.c .61

If I can’t solve a problem one way in math, I try to figure out a differentway.d .62

In English/math, I spend some time thinking about how to do my workbefore I start it. .71 .63

I ask myself questions when I read my English/when I work on mymath, to make sure I understand. .76 .69

I try to connect new work in English/math to what I’ve learned before. .81 .82

Maladaptive learning strategies

Mean 2.54 2.38Standard deviation 1.00 .94Skewness .75 .69Cronbach’s a .77 .80

I only figure out why I got something wrong in English/math when theteacher makes me do it. .62 .55

When I don’t understand my work in English/math, I give up and dosomething else. .68 .84

When the work in English/math is difficult, I just do what is easy. .90 .57When I do my English/math, I sometimes skip the hard parts. .78 .64

a All scales were responded to on a five point Likert-type scale; participants’ scores are theaverage of scores on individual items.

b Numbers corresponding to single items are factor loadings.c This item appeared only in the English scale.d This item appeared only in the math scale.

ing high and low academic competence. Second, dominant goal orientationgroups were formed according to the goal orientation on which students hadthe higher score. Six cases were excluded from each sample as their scoreson learning and performance goal orientation measures were the same. Thenumber of students participating in this analysis was, therefore, 97 in theEnglish sample and 120 in the math sample. Table 2 presents descriptivestatistics for each of the dependent variables (adaptive and maladaptive learn-

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TABLE 2DESCRIPTIVE STATISTICS FOR ADAPTIVE AND MALADAPTIVE LEARNING STRATEGIES FOR DOMI-

NANT GOAL ORIENTATION AND PERCEIVED ACADEMIC COMPETENCE GROUPS CREATED BY MEDIAN

SPLIT

Dominant goal orientation

Learning Performance

English Math English MathPerceived

Variables: competence Mean SD Mean SD Mean SD Mean SD

Adaptivelearning High 3.59 .72 3.64 .78 2.88 .89 2.91 .82strategies Low 2.96 .64 3.07 .61 2.44 .84 2.88 .57

Maladaptivelearning High 1.83 .61 1.76 .70 2.70 .73 2.50 .76strategies Low 2.48 .82 2.29 .75 3.48 1.07 3.18 .93

NÅ High 31 42 19 20Low 25 26 22 32

ing strategies) for the four groups in the sample (learning orientation/highcompetence, learning orientation/low competence, performance orientation/high competence, performance orientation/low competence).

Multivariate Analysis of Goal Orientation and Perceived AcademicCompetence

Main and interaction effects of dominant goal orientation group and per-ceived academic competence group on the dependent variables were testedwith MANOVA. Students’ scores on the CTBS were entered as a covariate.The Box-M test of homogeneity indicated that variance among the differentgroups was not significantly different (English: F(18,23854) Å 1.51, p ú .08;math: F(18,26731) Å 1.58, p ú .6). Main effects were found for goal orienta-tion (English: F(2,87) Å 19.73, p õ .001; math: F(2,110) Å 18.32, p õ .001)and perceived academic competence (English: F(2,87) Å 13.10, p õ .001;math: F(2,110) Å 5.54, p õ .01). The multivariate interaction was not signifi-cant. These results are similar to findings reported by Miller and his colleagues(1993) with persistence, valuing, and self-regulation as the dependent vari-ables. We next conducted a series of analyses of covariance (ANCOVA) testsfor the specific testing of the theoretical hypotheses.

ANCOVAs of Goal Orientation and Academic Competence Groups

Four ANCOVA tests were conducted within each sample: between thepredominant learning goal groups for each of the learning strategies, and

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between the predominant performance goal groups for each of the learningstrategies. The Bonferroni adjustment was used to account for the multipletests (p õ .0125). ANCOVA was used rather than t tests in order to includeachievement as a covariate. In all the following tests, students’ CTBS scoreswere entered as a covariate.

Adaptive learning strategies. An ANCOVA testing for differences betweenthe predominant learning goal groups in the English sample indicated thatlearning-oriented students with high perceived competence were more likelyto report use of adaptive strategies than were learning-oriented students withlow perceived competence (F Å 19.70, p õ .001). Similar results were foundin the math sample although the significance level was marginal (F Å 4.67,p õ .013). No difference was found in reported use of adaptive learningstrategies among the predominantly performance-oriented students with dif-ferent levels of perceived competence in the English sample (F Å 2.68, p õ.11) and in the math sample (F Å 1.70, p õ .200).

Maladaptive learning strategies. An ANCOVA testing for differences be-tween predominant learning goal groups in the English sample indicated thatlearning-oriented students with high perceived competence were less likelyto report using maladaptive learning strategies than were learning-orientedstudents with low perceived competence (FÅ 9.41, põ .005). This significantdifference, however, was not found in the math sample (F Å 3.30, p õ .08).The ANCOVA testing for differences between the predominant performancegoal groups in the English sample indicated that performance-oriented stu-dents with high perceived competence were less likely to report maladaptivestrategies than were performance-oriented students with low perceived com-petence (F Å 6.96, p õ .012). This significant difference was not found inthe math sample (F Å .96, p õ .33).

Summary of Findings Regarding Interactions between Goal Orientationand Perceived Competence—Median Split Technique

Similar to Miller and his colleagues (1993), an interaction between predomi-nant goal orientation and level of perceived competence did not emerge fromthe multivariate analysis. In the ANCOVAs, the predicted moderating role ofperceived competence on performance goal orientation was not found for adaptivepatterns of learning. The two performance goal groups (high and low perceivedcompetence) did not differ from each other in the reported use of adaptivelearning strategies in the English or the math samples. Unlike Miller and hiscolleagues, however, we found support for the predicted moderating effect ofperceived competence on predominant performance orientation for maladaptivelearning strategies, but only in the English sample. Finally, contrary to predictions,and again similar to findings by Miller and his colleagues, a moderating effectof perceived competence was found for learning goals. That is, in both samples,the learning goal/high competence group was significantly higher than the learn-ing goal/low competence group on reported use of adaptive learning strategies.

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Furthermore, in the English sample, the learning goal/low competence groupwas significantly higher than the learning goal/high competence group in thereported use of maladaptive learning strategies.

Miller and his colleagues suggested that the method they used for theformation of the groups (similar to our strategy above) might be the reasonfor the failure to find support for the theoretical predictions. They discussedbriefly the possibility of using a more direct approach for determining thedominant goal orientation, such as a forced-choice method. They rightlynoted, however, that such a method would sacrifice the variation with whichsubjects experience the different goal orientations.

Multiple Regression

We decided to use a different method for the investigation of the interactionof goal orientation and perceived competence. Following Aiken and West(1991), we used multiple regression analyses with interaction terms for thegoal orientations and perceived competence. Aiken and West note that multi-ple regression is often more appropriate than ANOVA for naturalistic studiesthat involve measured variables. Furthermore, multiple regression analysisallows a test for the expected interaction effects without the loss of informationthat results from transforming a continuous variable into a dichotomous one.

In all the following analyses, the learning goals, performance goals, andperceived competence variables were centered prior to the construction ofthe interaction terms. The regression analyses were run with the centeredvariables while the dependent variable was left in its original metric. Centeringthe variables accounts for multicollinearity that might result from a highcorrelation between the first order terms and the interaction terms if theformer are not centered. The centering procedure does not affect the regressioncoefficient of the interaction term. Furthermore, the process allows for a moremeaningful interpretation of the regression coefficients as each represents therelationship between the first order predictor and the criterion variable at themean level rather than a less meaningful level (i.e., zero) of the other predictor(Aiken & West, 1991).

Test of Theoretical Predictions

To support the theoretical predictions, we expected to find two indicators incombination. First, we expected to find significant interaction terms betweenlearning goals and perceived competence that would indicate that the increasein endorsement of learning goals has a different correspondence with learningstrategies among students with different levels of perceived competence. Spe-cifically, at low levels of learning goals different levels of perceived compe-tence are expected to result in different levels of strategy use. As learninggoals increase, the reported use of strategies at different levels of perceivedcompetence is expected to converge—to a high level in the case of adaptivestrategies and to a low level in the case of maladaptive strategies. Second,

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we expected to find significant interaction terms between performance goalsand perceived competence that would indicate that the increase in endorse-ment of performance goals has a different correspondence with learning strate-gies among students with different levels of perceived competence. However,rather than convergence at higher levels of performance goals, we wouldexpect to find divergence. Specifically, in the case of adaptive learning strate-gies we would expect to find an increase (or no change) in the reported useamong students with high perceived competence and a decrease in the reporteduse among students with low perceived competence. In the case of maladap-tive learning strategies this pattern is expected to be reversed.

Two hierarchical multiple regression analyses were run within each sample,one with the adaptive and the other with the maladaptive strategies as thedependent variable. The Bonferroni adjustment was used to account for themultiple tests (p õ .025). At the first step, CTBS scores were entered intothe equation. The second step involved introducing learning goals, perfor-mance goals, and perceived competence. At the third step, two interactionterms were introduced: learning goals and perceived competence, and perfor-mance goals and perceived competence. Results of the regression analyses foradaptive learning strategies are presented in Table 3. Results of the regressionanalyses for maladaptive learning strategies are presented in Table 4.

Adaptive learning strategies. The results of the regression analysis in theEnglish sample indicated that neither the interaction between performancegoals and perceived competence nor the interaction between learning goalsand perceived competence were significant predictors of adaptive learningstrategies. Achievement and performance goals were not significant pre-dictors. Learning goals and perceived competence were both positive pre-dictors of report of adaptive learning strategies. These results point to thefacilitating roles of learning goals and perceived competence for adaptivelearning strategies. The facilitating role of perceived competence was foundamong all students regardless of their level or type of goal orientation.

In the math sample, however, the interaction between learning goals andperceived competence was marginally significant (põ .03). Similar to the Englishsample, achievement and performance goals were not significant predictors ofreport of adaptive learning strategies, and learning goals was a significant pre-dictor. Unlike the English sample, perceived competence was not a significantpredictor of report of adaptive learning strategies. Following Aiken and West(1991), a plot of the interaction is presented in Fig. 1. The plot indicates thatthe relation between learning goals and use of adaptive strategies in math isdifferent at different levels of perceived competence. Specifically, while an in-crease in learning goals is related to an increase in adaptive strategy use forstudents at all levels of perceived competence, this relation is stronger amongstudents with high perceived competence. This is in opposition to the theoreticalprediction. Similar to the English sample, the interaction between performancegoals and perceived competence was not significant, indicating no differences in

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TABLE 3RESULTS OF MULTIPLE REGRESSION ANALYSIS FOR ADAPTIVE LEARNING STRATEGIES

English Domain

Predictor Beta step 1 Beta step 2

CTBS language 0.02 0.01Learning goals .41***Performance goals .00Perceived competence .27**

R2 .00 .41

*p õ .025, **p õ .01, ***p õ .001

Math Domain

Predictor Beta step 1 Beta step 2 Beta step 3

CTBS math .00 0.02 0.01Learning goals .51*** .47***Performance goals .01 .03Perceived competence .03 .09Learning goals xPerceived competence .16Ç

Performance goals xPerceived competence 0.11

R2 .00 .38 .43

Çp õ .03, *p õ .025, **p õ .01, ***p õ .001

the relation between performance goals and reports of adaptive learning strategiesamong students with different levels of perceived competence.

Maladaptive learning strategies. Similar to the results for adaptive learningstrategies, the results of the regression analysis in the English sample indicatedthat neither the interaction between performance goals and perceived compe-tence nor the interaction between learning goals and perceived competencewere significant. Achievement was not a significant predictor for maladaptivelearning strategies; however, both goals and perceived competence were.Learning goals and perceived competence were significant negative predictorswhile a performance goal orientation was a significant positive predictor formaladaptive learning strategies. These results again point to similar relationsbetween goals and maladaptive strategies among all students regardless oftheir level of perceived competence.

In the math sample, similar to the results for the adaptive learning strategies,the results of the regression analysis indicated that the interaction between learning

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TABLE 4RESULTS OF MULTIPLE REGRESSION ANALYSIS FOR MALADAPTIVE LEARNING STRATEGIES

English Domain

Predictor Beta step 1 Beta step 2

CTBS language 0.03 0.02Learning goals 0.34***Performance goals .15*Perceived competence 0.62***

R2 .01 .55

*p õ .025, **p õ .01, ***p õ .001

Math Domain

Predictor Beta step 1 Beta step 2 Beta step 3

CTBS math 0.15*** 0.07** 0.07**Learning goals 0.33*** 0.37***Performance goals .29*** .31***Perceived competence 0.29*** 0.24**Learning goals xPerceived competence .16*Performance goals xPerceived competence 0.08

R2 .18 .55 .58

Çp õ .03, *p õ .025, **p õ .01, ***p õ .001

goals and perceived competence was significant in predicting maladaptive learn-ing strategies. In this analysis, achievement, learning goals, performance goals,and perceived competence were all significant predictors of maladaptive learningstrategies. Achievement, learning goals, and perceived competence were signifi-cant negative predictors, while a performance goal orientation was a significantpositive predictor for maladaptive learning strategies. A plot of the interactionis presented in Fig. 2. The plot indicates that an increase in learning goals isassociated with a greater decrease in maladaptive strategy use for students withlow perceived competence than for students with higher perceived competence.This interaction manifests the expected convergence in low reported use ofmaladaptive strategies at high levels of learning goals and thus supports thetheoretical prediction. The interaction between performance goals and perceivedcompetence was not significant. This finding indicates that an increase in perfor-mance goals is positively related to reports of maladaptive learning strategiesregardless of students’ perceived competence. This fails to support the theoreticalprediction.

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FIG. 1. Level of learning goals and perceived competence in report of adaptive learningstrategies in math.

DISCUSSION

The present study was concerned with the hypothesized moderating roleof perceived competence on the relations between goal orientations and theuse of adaptive and maladaptive strategies. Our results add to the knowledgeconcerning this theoretical question in several ways. First, we found littlesupport for the theoretical predictions concerning the moderating role of

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FIG. 2. Level of learning goals and perceived competence in report of maladaptive learningstrategies in math.

perceived competence for the relation between performance goals and learningstrategies. Specifically, the predicted moderating role of perceived compe-tence was found only in one case—maladaptive learning strategies in themath sample—and was supported by the ANCOVA analysis but not bythe regression analysis. Second, we found evidence that perceived academiccompetence moderated the relation between learning goals and both adaptiveand maladaptive patterns of learning. Only in one case—for maladaptive

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learning strategies in the math sample—did the findings support the theoreti-cal prediction that this moderating role does not exist. This was unexpected.Dweck and others (Dweck, 1986; Elliott & Dweck, 1988) have theorized thata learning goal orientation is associated with adaptive patterns of learningregardless of the level of perceived skill. Third, we examined these relationsin two samples of seventh grade students, asking one about the math domainand the other about the English domain, and found some domain differences.Fourth, we used two different techniques, median split (used by Miller) andmultiple regression, to test the moderating role of goals and found someconverging and some diverging results using the two different techniques.

Perceived Competence and Learning Strategies in Performance GoalOrientation

Using the median split technique and multiple regression we found littlesupport for the role of perceived competence as a moderator between perfor-mance goal orientation and the use of learning strategies. When using themedian split to create predominantly performance-oriented groups, all themeans of reported use of learning strategies were arrayed in the theoreticallypredicted direction; however, the differences in report of adaptive learningstrategies and in report of maladaptive learning strategies in the math domainwere not large enough to reject the null hypothesis. The results of the multipleregression indicated that performance goals were unrelated to adaptive learn-ing strategies and were positively related to maladaptive learning strategiesregardless of level of perceived competence.

Performance-oriented students are concerned with others’ judgment of theirlevel of skill. Perception of high skill or competence is assumed to providea buffer against negative emotions and behaviors that might arise as a resultof such a concern. The present findings suggest that this buffer is not strongenough to protect performance-oriented students from the influences of thisconcern on use of learning strategies. The differences between these resultsand those expected from the theoretical predictions and from experimentalresults such as those of Dweck (e.g., Dweck & Leggett, 1988) might stemfrom differences in measurement and design and will be discussed below.

Perceived Competence and Learning Strategies in Learning GoalOrientation

Using both the median split technique and multiple regression, we foundevidence that level of perceived competence moderated the relation betweena learning goal orientation and the use of both adaptive and maladaptivelearning strategies. Again, when using the median split to create predomi-nantly learning-oriented groups, all the means of reported use of learningstrategies indicated reports of more adaptive and less maladaptive strategiesby the high perceived competence group. In one case—report of maladaptivelearning strategies in the math domain—the difference was not large enough

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to achieve significance after accounting for achievement. Similarly, the resultsof the multiple regression in the English domain, where no interaction wassignificant, and for adaptive learning strategies in the math domain, wherethe significant interaction pointed to an increased effect of learning goalsamong students with high perceived competence, pointed to a moderatingrole of perceived competence for learning-oriented students.

Miller and his colleagues (1993) also found that students with dominant learn-ing goals and high perceived ability reported higher levels of self-monitoringthan did students with low perceived ability. Dweck (1986) hypothesized thatstudents who were oriented to learning goals would engage in adaptive patternsof behavior regardless of their level of perceived skill at the task. She and othershave suggested that students who hold learning goals are not concerned aboutother’s judgment of their level of ability, but rather are focused on the task.Therefore, perception of low ability is not expected to present a risk to self- orothers’ evaluations and is not expected to result in maladaptive self-protectivebehavior. Students with learning goals are expected to manifest a ‘‘mastery’’pattern of academic behavior, regardless of their level of perceived ability. Ourresults that indicated the moderating role of perceived competence for studentswith a predominant learning goal orientation are provocative and somewhatdisheartening. Those who are interested in using goal theory as a framework toguide school reform (e.g., Ames, 1990; Maehr & Midgley, 1996) have suggestedthat the emphasis should be on moving toward a learning focus in schools. Theyhave gone on to say that when learning goals are salient, students’ level ofperceived competence is no longer a primary concern. These results indicate thatstudents’ perceptions of their ability to do their academic work remain an issueeven when they are learning focused.

Two related points might provide an explanation for this observed phenom-enon. First, the students in this sample were in traditional middle schools,and policies and practices that have been theorized to promote performancegoals (ability grouping, recognition based on relative ability, normative evalu-ation) were quite evident (Midgley, 1993; Urdan, Wood, & Midgley, 1995).In an environment in which learning goals were emphasized more, levelof perceived competence might become less influential for students with apredominant learning goal orientation. Second, while students in our studyand in the study conducted by Miller and his colleagues were labeled ‘‘pre-dominately learning oriented,’’ it is important to note that these students alsomay have been oriented to performance goals, but to a lesser extent. Indeed,the multiple regression technique does not distinguish among students withhigh learning goals and high performance goals and students with high learn-ing goals and low performance goals. It is quite possible that the kind oflearning strategies assessed in this study were elicited under conditions inwhich performance goals were salient, even to predominantly learning-ori-ented students. This may explain why the strategies used by students withpredominant learning goals were still influenced by their level of perceived

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skill, hence the relation of perceived competence to their academic behavioremerges. We would like to believe that in schools that emphasize understand-ing, improvement, and mastery, students with learning goals would engagein productive behaviors regardless of their level of perceived competence.

Differences in the Approaches Used in the Present Study and by Dweck

Perhaps it is not surprising that the results from this study do not providesupport for Dweck’s hypothesis given the very different approaches used.First, Dweck’s studies generally involve students engaged in specific tasks(e.g., Elliott & Dweck, 1988; Diener & Dweck, 1978; Smiley & Dweck,1994). In contrast, the present study and the study conducted by Miller and hiscolleagues (1993) were based on multiple episodes and activities in students’academic lives. Second, the tasks introduced in Dweck’s studies are com-monly new and unfamiliar. In the present study, the students were asked toreflect on familiar academic tasks with which they have had a longer relation-ship. Third, in Dweck’s studies, the outcome is commonly a particular ob-served behavior (e.g., a choice, an utterance). In contrast, in the present studythe outcome was self-reported behaviors that presumably were exhibited overan extended period of time. Perceived competence, under such conditions, ismore likely to be constructed from multiple and repeated occurrences.

In the laboratory, a goal orientation is elicited for a specific and often noveltask, and in an environment that students have not experienced previously. Inthe field, students might have a general goal orientation toward math, for example,still realizing, however, that achieving their academic goal (i.e., mastering thematerial) might involve much effort and cost. When students are learning ori-ented, they would still be interested in mastering skills and be only mildlyconcerned with normative performance. However, some classroom characteris-tics, such as a rigid time schedule for assignments and lack of opportunity toprogress at an individual pace, might impel students with low perceived compe-tence to act in ways that are contrary to what is found under laboratory conditions.Thus, while students with learning goals are not likely to perceive the situationas a threat to the self, when combined with low perceived competence, theymight still engage in maladaptive learning strategies.

In the case of performance goals, laboratory studies indicate that in someparticular performance-oriented situations—for example, when there is a riskfor public mistakes—students with high perceived competence will manifestbehavior similar to students with low perceived competence (Dweck & Leg-gett, 1988; Elliott and Dweck, 1988). When reflecting on their behavior in asurvey, performance-oriented students with high perceived competence mightinclude these occasions in their reports.

Similarities and Differences in Results Based on Subject Domain and theTechnique Used

That somewhat similar results were found in two different subject domainsand using two different analysis techniques adds credibility to the observed

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results. In particular, the results of this study suggest that endorsement ofperformance goals in combination with high perceived competence does notfacilitate adaptive learning strategies and only occasionally might provide abuffer against maladaptive learning strategies. Furthermore, the results pro-vide support for the enduring role of perceived competence among learning-oriented students for adaptive learning strategies.

A limitation of our study was the fact that the subject domains were testedin two different samples rather than with the same respondents. Future re-search should investigate the generalizability of results across domains withthe same sample. The generalizability of our study is limited also to youngadolescents; in this case seventh graders in traditional middle schools. Millerand his colleagues (1993) investigated undergraduate students. Older adoles-cents in a college setting may have a different conception of perceived compe-tence than do young adolescents. As research in goal orientation continues,greater attention needs to be paid to the ages and stages of participants in thevarious studies. Additionally, future studies, particularly those examiningthese relationships in various subject domains, should consider the role ofgender. There is some evidence that gender differences in perceived compe-tence differ in English and math (e.g., Eccles, Adler, Futterman, Goff, Kac-zala, Meece, & Midgley, 1983). There is also some evidence that males aremore oriented to performance goals than are females and females are moreoriented to learning goals than are males (e.g., Roeser, Midgley, & Urdan,1996). Therefore, gender may play a role in the relationships between goals,perceived competence, and behavior.

It should also be pointed out that although most of the students in thisstudy were in ‘‘general’’ math and English, some were in advanced and somewere in basic classes. Perceived competence would likely differ for studentsin the various tracks. Additionally, the goal emphases in these tracks maydiffer and influence students’ personal goal orientations. It will be importantin future studies conducted in secondary schools to consider whether trackplacement affects the role perceived competence plays in moderating therelationship between goals and outcomes.

Our study raises several points concerning theory, methodology, and practice.First, it has been suggested recently that performance goals should be conceptual-ized in terms of both approach and avoidance motivational orientations (e.g.,Elliot & Harackiewicz, 1996). In Dweck’s studies, as in ours, performance goalshave been conceptualized in approach terms, that is, as an orientation to demon-strating ability. However, performance goals can also be conceptualized in avoid-ance terms, that is, as an orientation to avoiding the demonstration of lack ofability. Interestingly, in our study, perceived competence moderated approachand avoidance learning strategies for learning-oriented students but not for perfor-mance-oriented students. This point suggests further theorizing about the determi-nants of approach and avoidance tendencies within both the performance andlearning goal conceptualizations, which leads to a second point suggested by our

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study. The findings from the present study reinforce the notion that studentspursue multiple goals simultaneously. Indeed, the findings concerning the roleplayed by perceived competence for ‘‘predominately’’ learning oriented studentssuggest that these students’ reports might be influenced by reflection on behaviorthey engaged in while experiencing learning environments in which performancegoals were salient—a situation that might have elicited a performance-orientedbehavior. Some research has looked at various ‘‘patterns’’ of goal pursuit(Meece & Holt, 1993; Kaplan & Bos, 1995; Urdan & Midgley, 1995), but thereis still little discussion about these combinations of goal pursuits and what roleperceived ability might play or what they mean for students’ patterns of behavior.

Finally, the differences that emerged between the results of the presentstudy and Dweck’s studies invite a more thorough examination of the differentresults that emerge when conducting research under experimental versus sur-vey methods. In particular, the difference between manipulation of goal orien-tation for a situated task versus reflective self-report of goal orientation basedon an extended period of time and its implications for goal theory and educa-tional practice should be examined.

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The Effect of Achievement Goals: Does Level of Perceived Academic Competence Make a Difference

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