Motivation and Classroom Learning Research Paper

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Classroom learning is often discussed solely in terms of cognition and the various cognitive and metacognitive processes that are involved when students learn in academic settings. In fact, in a key chapter on learning, remembering, and understanding in the Handbook of Child Psychology, Brown, Bransford, Ferrara, and Campione (1983) noted

Bleak though it may sound, academic cognition is relatively effortful, isolated, and cold. . . .Academic cognition is cold, in that the principal concern is with the knowledge and strategies necessary for efficiency, with little emphasis placed on the emotional factors that might promote or impede that efficiency. (p. 78)

This quote in the most important and influential handbook on child development reflects the state of the field in the early 1980s. Most of the models and research on academic cognition did not address issues of motivation or emotion and how these factors might facilitate or constrain cognition and learning. Basically, motivation was irrelevant to these cold models of cognition as they concentrated on the role of prior knowledge and strategies in cognition and learning.

At the same time, most motivational research in general— and within educational psychology specifically—did not investigate the linkages between motivational beliefs and academic cognition. Motivational research was focused on examining performance, which often was operationalized in terms of experimental tasks such as performance on anagram tasks or other lab tasks that were knowledge-lean and did not really reflect school learning tasks. In addition, motivational research was concerned with the classroom factors that predicted student motivation and achievement, but achievement was usually operationalized as course grades, performance on classroom tests, or performance on standardized achievement tests.The research did not really examine learning on domainspecific academic tasks (e.g., math, science tasks), which is what the cognitive researchers were focused on in their research. Motivational models and constructs were cognitive— especially in social cognitive models of motivation—but the links between the motivational constructs and the cognitive tasks and models were not made explicit in the research or in the theoretical models of motivation.

Fortunately, this state of affairs has changed dramatically over the last 20 years of research. Cognitive researchers now recognize the importance of motivational constructs in shaping cognition and learning in academic settings (e.g., Bransford, Brown, & Cocking, 1999), and motivational researchers have become interested in how motivational beliefs relate to student cognition and classroom learning (e.g., Pintrich, 2000a, 2000c). This integrative work on academic cognition and motivation has provided a much more accurate and ecologically valid description of classroom learning. Given these advances in our scientific knowledge, our understanding of classroom learning is not only more robust and generalizable, but it is also more readily applicable to problems of instructional improvement.

The purpose of this research paper is to summarize this work and discuss how various motivational constructs are related to student cognition and learning in classrooms. Given space considerations, this research paper does not represent a comprehensive review of the extant research in this area; rather, it attempts to highlight the key features of the work and active areas of research interest and future directions for the field.

In addition, the paper focuses on personal motivational beliefs and their role in cognition and learning. It does not consider the role of various classroom contextual features and how they shape the development of student motivation. Readers interested in the role of classroom context factors can consult other sources (e.g., Pintrich & Schunk, 2002; Stipek, 1996). This research paper first discusses four general outcomes of motivation; then it considers how different motivational constructs are related to these four outcomes. From this analysis, four generalizations are proposed for how motivational constructs can facilitate or constrain cognition and learning. The paper concludes with a discussion of future research directions for integrating motivation and cognition.

Motivational Theories and Student Outcomes

There are many different motivational theories related to achievement and learning (see Pintrich & Schunk, 2002; Graham & Weiner, 1996). These theories make some different metatheoretical assumptions about human nature and have proposed a large number of different constructs to explain motivated human behavior. In fact, the large number of different motivational constructs with different labels often makes it difficult for novices to understand and use the different constructs in their own research (Murphy & Alexander, 2000). Nevertheless, these different theories have some important commonalities in outcomes and motivational constructs that allow for some synthesis across theories. In this research paper, the focus is on four general outcomes with which all motivational theories are concerned, as well as three macrolevel motivational components that are inherent in most models of motivation. Accordingly, this research paper does not focus on different theoretical models of motivation; rather, it discusses how the three different motivational components are related to the four outcomes. Within the discussion of the three general motivational components, different theoretical perspectives and constructs are highlighted.

The term motivation comes from the Latin verb movere, which means to move. Motivation is evoked to explain what gets people going, keeps them going, and helps them finish tasks (Pintrich & Schunk, 2002). Most important is that motivational constructs are used to explain the instigation of behavior,thedirectionofbehavior(choice),theintensityofbehavior (effort, persistence), and actual achievement or accomplishments. Motivational theories focus both on developing general laws of behavior that apply to all people (a nomothetic perspective) as well as seeking explanations for individual differences in behavior (an idiographic perspective). Historically, cognitive researchers often ignored motivational research because it was assumed that motivational constructs were used to explain individual differences in behavior, which was not a useful perspective for general models of cognition. However, this classic distinction between nomothetic and idiographic perspectives has lessened over time as motivational researchers have developed general principles that apply to all individuals as well as constructs that can be used to explain individual differences.

Most motivational theories attempt to predict four general outcomes. First, motivational theories are concerned with why individuals choose one activity over another—whether it be the day-to-day decisions regarding the choice of working on a task or relaxing or the more momentous and serious choices regarding career, marriage, and family. In the academic domain, the main issues regarding choice concern why some students choose to do their schoolwork and others choose to watch TV, talk on the phone, play on the computer, play with friends, or any of the other activities that students can choose to do instead of their schoolwork. In addition, motivational theories have examined why students choose one major over another or choose to take certain classes over others when given a choice. For example, in high school, students are often allowed to choose some of their courses; motivational theories have examined why some students choose to take more academic math and science courses over less rigorous courses. Choice is an important motivational outcome, and choosing to do an academic task over a nonacademic task is important for classroom learning; however, it may not be as important to classroom learning as are some of the following outcomes.

A second aspect of motivated behavior that motivational research has examined is the students’ level of activity or involvement in a task. It is assumed that students are motivated when they put forth a great deal of effort in courses—from not falling asleep to more active engagement in the course. Behavioral indicators of this involvement could include taking detailed notes, asking good questions in class, being willing to take risks in class by stating ideas or opinions, coming after class to discuss in more detail the ideas presented in class, discussing the ideas from the course with classmates or friends outside of class time, spending a reasonable amount of time studying and preparing for class or exams, spending more time on one course than on other activities, and seeking out additional or new information from the library or other sources that goes beyond what is presented in class. Motivational theories have developed constructs that help to predict these types of behavioral outcomes.

Besides these behavioral indicators, there are more covert or unobservable aspects of engagement that include cognitive engagement and processing, such as thinking deeply about the material, using various cognitive and self-regulatory strategies to learn the material in a more disciplined and thoughtful manner, seeking to understand the material (not just memorize it), and integrating the new material with previously held conceptions of the content. All of these cognitive processes are crucial for deeper understanding and learning. It is important to note that it is not enough for students to just be behaviorally engaged in the course; they also must be cognitively engaged in order for true learning and understanding to occur. In this sense, cognitive engagement refers to the quality of students’ engagement, whereas sheer effort refers to the quantity of their engagement in the class. This outcome of cognitive engagement is the most important one for understanding classroom learning and is the main focus of this research paper.

The third general aspect of motivated behavior that has been examined in most motivational theories is persistence. If individuals persist at tasks even in the face of difficulty, boredom, or fatigue, it would be inferred that they are motivated to do that task. Persistence is easily observable in general because teachers do have opportunities to observe students actually working on course tasks during class time. It is common for teachers to comment on the students’ willingness to persist and try hard on the classwork. In this sense, persistence and behavioral engagement are much easier for teachers and others to judge than is cognitive engagement.

The fourth general outcome that motivational theories have examined is actual achievement or performance; in the classroom setting, this involves predicting course grades, scores on classroom tests, or performance on standardized achievement tests. These are important outcomes of schooling, although they may not always reflect what students actually learned or the quality of their cognition and thinking. This mismatch between the quality of cognition and the performance on the academic tasks or tests that students actually confront in classrooms can lead to some different conclusions about the role of different motivational components. It may be that some motivational components predict general course achievement or performance on standardized tests, and others are better predictors of the quality of cognition or cognitive engagement in learning tasks. This general idea of differential links between different motivational components and different outcomes is an important contribution of current motivational research. The field has moved past the search for a single magic motivational bullet that will solve all learning and instructional problems to the consideration of how different motivational components can facilitate or constrain different outcomes.

The remainder of this research paper discusses how motivational components can shape and influence cognition, learning, and the other important outcomes of schooling. Of course, a key assumption is that motivation and cognition are related, and that contrary to Brown et al. (1983), there is a need to examine how motivational and emotional components can facilitate or constrain cognition and learning. Accordingly, the remainder of this research paper discusses how motivational components can predict the four outcomes, including cognition and learning. At the same time, it should be clear that most current models of motivation assume that there is a reciprocal relation between motivation and cognition such that cognitive outcomes like learning and thinking or general outcomes like achievement and performance do have feedback effects on motivation. For example, as a student learns more and becomes more successful in achieving in the classroom (as indexed by grades or test scores), these accomplishments have an influence on subsequent motivation. Nevertheless, the emphasis in the motivational research has been on how motivation influences cognition and learning; therefore, that is the general orientation taken in this research paper.

The Role of Motivational Components in Classroom Learning

Although many models of motivation may be relevant to student learning (see Graham & Weiner, 1996; Heckhausen, 1991; Pintrich & Schunk, 2002; Weiner, 1992), a general expectancy-value model serves as a useful framework for analyzing the research on motivational components (Pintrich, 1988a, 1988b, 1989; Pintrich & Schunk, 2002). Three general components seem to be important in these different models: (a) beliefs about one’s ability or skill to perform the task (expectancy components); (b) beliefs about the importance, interest, and utility of the task (value components); and (c) feelings about the self or emotional reactions to the task (affective components).

Expectancy Components

Expectancy components are students’ answer to the question Can I do this task? If students believe that they have some control over their skills and the task environment and if they are confident in their ability to perform the necessary skills, they are more likely to choose to do the task, be cognitively involved, persist at the task, and achieve at higher levels. Different motivational theorists have proposed a variety of constructs that can be categorized as expectancy components. The main distinction is between how much control one believes one has over the situation and perceptions of efficacy to accomplish the task in that situation. Of course, these beliefs are correlated empirically, but most models do propose separate constructs for control beliefs and efficacy beliefs.

Control Beliefs

There have been a number of constructs and theories proposed about the role of control beliefs for motivational dynamics. For example, early work on locus of control (e.g., Lefcourt, 1976; Rotter, 1966) found that students who believed that they were in control of their behavior and could influence the environment (an internal locus of control) tended to achieve at higher levels. Deci (1975) and de Charms (1968) discussed perceptions of control in terms of students’belief in self-determination. This self-determination perspective is crucial in intrinsic motivation theories of motivation (e.g., Deci & Ryan, 1985; Ryan & Deci, 2000) in which students are only intrinsically motivated if they feel autonomous and their behavior is self-determined rather than controlled by others. De Charms (1968) coined the terms origins and pawns to describe students who believed they were able to control their actions and students who believed others controlled their behavior. Connell (1985) suggested that there are three aspects of control beliefs: an internal source, an external source or powerful others, and an unknown source. Students who believe in internal sources of control are assumed to perform better than do students who believe powerful others (e.g., faculty, parents) are responsible for their success or failure or those students who don’t know who or what is responsible for the outcomes. In the college classroom, Perry and his colleagues (e.g., Perry, 1991; Perry & Dickens, 1988; Perry & Magnusson, 1989; Perry & Penner, 1990) have shown that students’ beliefs about how their personal attributes influence the environment—what they label perceived control—are related to achievement and to aspects of the classroom environment (e.g., instructor feedback).

Skinner and her colleagues (e.g., Skinner, 1995, 1996; Skinner, Wellborn, & Connell, 1990) distinguish three types of beliefs that contribute to perceived control and that are important in school. These three beliefs can be organized around the relations between an agent, the means or strategies and agent might use, and the ends or goals that the agent is trying to attain through the means or strategies (Skinner, 1995). Capacity beliefs refer to an individual’s beliefs about his or her personal capabilities with respect to ability, effort, others, and luck (e.g., I can’t seem to try very hard in school). These beliefs reflect the person’s beliefs that he or she has the means to accomplish something and are similar to efficacy judgments (Bandura, 1997) or agency beliefs (Skinner, 1995, 1996; Skinner, Chapman, & Baltes, 1988). Strategy beliefs are expectations or perceptions about factors that influence success in school, such as ability, effort, others, luck, or unknown factors (e.g., The best way for me to get good grades is to work hard.). These beliefs refer to the perception that the means are linked to the ends—that if one uses the strategies, the goal will be attained. They also have been called outcome expectations (Bandura, 1997) and means-ends beliefs (Skinner, 1995, 1996). Control beliefs are expectations about an individual’s likelihood of doing well in school without reference to specific means (e.g., I can do well in school if I want to). These beliefs refer to the relation between the agent and the ends or goals and also have been called control expectancy beliefs (Skinner, 1995, 1996). Skinner and colleagues (Skinner, 1995; Skinner et al., 1990) found that perceived control influenced academic performance by promoting or decreasing active engagement in learning and that teachers contributed to students’perceptions of control when they provided clear and consistent guidelines and feedback, stimulated students’interest in learning, and assisted students with resources.

In self-efficacy theory, outcome expectations refer to individuals’ beliefs concerning their ability to influence outcomes—that is, their belief that the environment is responsive to their actions, which is different from self-efficacy (the belief that one can do the task; see Bandura, 1986; Schunk, 1985). This belief that outcomes are contingent on their behavior leads individuals to have higher expectations for success and should lead to more persistence. When individuals do not perceive a contingency between their behavior andoutcomes,theymayshowpassivity,anxiety,lackofeffort, and lower achievement, often labeled learned helplessness (cf. Abramson, Seligman, & Teasdale, 1978). Learned helplessness is usually seen as a stable pattern of attributing many events to uncontrollable causes, which leaves the individual believing that there is no opportunity for change that is under their control. These individuals do not believe they can do anything that will make a difference and that the environment or situation is basically not responsive to their actions.

The overriding message of all these models is that a general pattern of perception of internal control results in positive outcomes (i.e., more cognitive engagement, higher achievement, higher self-esteem), whereas sustained perceptions of external or unknown control result in negative outcomes (lower achievement, lack of effort, passivity, anxiety). Reviews of research in this area are somewhat conflicting, however (cf. Findley & Cooper, 1983; Stipek & Weisz; 1981), and some have argued that it is better to accept responsibility for positive outcomes (an internal locus of control) and deny responsibility for negative or failure outcomes (an external locus of control; see Harter, 1985). Part of the difficulty in interpreting this literature lies in the use of different definitions of the construct of control, different instruments to measure the construct, different ages of the samples, and different outcomes measures used as a criterion in the numerous studies. In particular, the construct of internal locus of control confounds three dimensions of locus (internal vs. external), controllability (controllable vs. uncontrollable), and stability (stable vs. unstable). Attributional theory proposes that these three dimensions can be separated conceptually and empirically and that they have different influences on behavior (Weiner, 1986).

Attributional theory proposes that the causal attributions an individual makes for success or failure—not the actual success or failure event—mediates future expectancies. A large number of studies have shown that individuals who tend to attribute success to internal and stable causes like ability or aptitude will tend to expect to succeed in the future. In contrast, individuals who attribute their success to external or unstable causes (i.e., ease of the task, luck) will not expect to do well in the future. For failure situations, the positive motivational pattern consists of not an internal locus of control, but rather attribution of failure to external and unstable causes (difficult task, lack of effort, bad luck) and the negative motivational pattern consists of attributing failure to internal and stable causes (e.g., ability, skill). This general attributional approach has been applied to numerous situations and the motivational dynamics seem to be remarkably robust and similar (Weiner, 1986, 1995).

The key difference between attributional theory and intrinsic motivation theories of personal control (e.g., de Charms, 1968; Deci & Ryan, 1985; Skinner, 1995, 1996) is that attributions are post hoc explanations for performance after some feedback about success or failure has been provided to the student. The control beliefs that are of concern to intrinsic motivation theorists are prospective beliefs of the student before he or she begins a task. Both types of construct are important in predicting various outcomes, including cognitive engagement (see Pintrich & Schrauben, 1992), but the motivational dynamics are different, given the different temporal role of attributions and control beliefs in the theoretical models.

It also is important to note that from an attributional analysis, the important dimension that is linked to future expectancies (beliefs that one will do well in the future) is stability, not locus (Weiner, 1986)—that is, it is how stable you believe a cause is that is linked to future expectancies (i.e., the belief that your ability or effort to do the task is stable over time, not whether you believe it is internal or external to you). Attributional theory generally takes a situational view of these attributions and beliefs, but some researchers have suggested that individuals have relatively consistent attributional patterns across domains and tasks that function somewhat like personality traits (e.g., Fincham & Cain, 1986; Peterson, Maier, & Seligman, 1993). These attributional patterns seem to predict individuals’performance over time. For example, if students consistently attributed their success to their own skill and ability as learners, then it would be predicted that they would continually expect success in future classes. In contrast, if students consistently attribute success to other causes (e.g., excellent instructors, easy material, luck), then their expectations might not be as high for future classes.

Individuals’ beliefs about the causes of events can be changed through feedback and other environmental manipulations to facilitate the adoption of positive control and attributional beliefs. For example, some research on attributional retraining in achievement situations (e.g., Foersterling, 1985; Perry & Penner, 1990) suggests that teaching individuals to make appropriate attributions for failure on school tasks (e.g., effort attributions instead of ability attributions) can facilitate future achievement. Of course, there are a variety of issues to consider in attributional retraining, including the specification of which attributional patterns are actually dysfunctional, the relative accuracy of the new attributional pattern, and the issue of only attempting to change a motivational component instead of the cognitive skill that also may be important for performance (cf. Blumenfeld, Pintrich, Meece, & Wessels, 1982; Weiner, 1986).

In summary, individuals’ beliefs about the contingency between their behaviors and their performance in a situation are linked to student learning and achievement. In a classroom context, this means that students’ motivational beliefs about the link between their studying, self-regulated learning behavior, and achievement will influence their actual studying behavior. For example, if students believe that no matter how hard they study, they will not be able to do well on a chemistry test because they simply lack the aptitude to master the material, then they will be less likely to actually study for the test. In the same fashion, if students believe that their effort in studying can make a difference regardless of their actual aptitude for the material, then they will be more likely to study the material. Accordingly, these beliefs about control and contingency have motivational force because they influence future behavior.

Self-Efficacy Beliefs

In contrast to control beliefs, self-efficacy concerns students’ beliefs about their ability to just do the task, not the linkage between their doing it and the outcome. Self-efficacy has been defined as individuals’ beliefs about their performance capabilities in a particular domain (Bandura, 1982, 1986; Schunk, 1985). The construct of self-efficacy includes individuals’ judgments about their ability to accomplish certain goals or tasks by their actions in specific situations (Schunk, 1985). This approach implies a relatively situational or domainspecific construct rather than a global personality trait or general perceptions of self-concept or self-competence. In an achievement context, it includes students’confidence in their cognitive skills to perform the academic task. Continuing the example from chemistry, a student might have confidence in his or her capability (a high self-efficacy belief) to learn the material for the chemistry test (i.e., I can learn this material on stoichiometry) and consequently exert more effort in studying. At the same time, if the student believes that the grading curve in the class is so difficult and that studying will not make much difference in his or her grade on the exam (a low control belief), that student might not study as much. Accordingly, self-efficacy and control beliefs are separate constructs, albeit they are usually positively correlated empirically. Moreover, they may combine and interact with each other to influence student self-regulation and outcomes.

An issue in most motivational theories regarding self-efficacy and control beliefs concerns the domain or situational specificity of the beliefs.As noted previously, selfefficacy theory generally assumes a situation-specific view— that is, individuals’ judgment of their efficacy for a task is a function of the task and situational characteristics operating at the time (difficulty, feedback, norms, comparisons with others, etc.) as well as their past experience and prior beliefs about the task and their current beliefs and feelings as they work on the task. However, generalized efficacy beliefs that extend beyond the specific situation may influence motivated behavior.Accordingly, students could have efficacy beliefs not only for a specific exam in chemistry, but also for chemistry in general, natural science courses in contrast to social science or humanities courses, or learning and schoolwork in general.At these more global levels, self-efficacy beliefs would become very similar to perceived competence beliefs or self-concept, at least in terms of the motivational dynamics and functional relations to student outcomes (Eccles, Wigfield, & Schiefele, 1998; Harter, 1999; Pintrich & Schunk, 2002). An important direction for future research will be to examine the domain generality of both self-efficacy and control beliefs. Nevertheless, it has been shown in many studies in many different domains—including the achievement domain—that students’ self-efficacy beliefs (or in more colloquial terms, their self-confidence in their capabilities to do a task) are strongly related to their choice of activities, their level of cognitive engagement, and their willingness to persist at a task (Bandura, 1986; Pintrich, 1999; Pintrich & De Groot, 1990; Pintrich & Schrauben, 1992; Schunk, 1985).

In terms of self-efficacy beliefs, results from correlational research (Pintrich, 1999, 2000b; Pintrich & De Groot, 1990) are very consistent over time and in line with more experimental studies of self-efficacy (Bandura, 1997). Self-efficacy is one of the strongest positive predictors of actual achievement in the course, accounting for 9–25% of the variance in grades, depending on the study and the other predictors entered in the regression (see review by Pintrich, 1999). Students who believe they are able to do the course work and learn the material are much more likely to do well in the course. Moreover, in these studies, self-efficacy remains a significant predictor of final achievement, although it accounts for less total variance, even when previous knowledge (as indexed by performance on earlier tests) or general ability (as indexed by SAT scores) are entered into the equations in these studies.

Finally, in all of these studies (see review by Pintrich, 1999), self-efficacy is a significant positive predictor of student self-regulation and cognitive engagement in the course. Students who are confident of their capabilities to learn and do the course work are more likely to report using more elaboration and organizational cognitive strategies. These strategies involve deeper cognitive processing of the course material—students try to paraphrase the material, summarize it in their own words, or make outlines or concept maps of the concepts in comparison to just trying to memorize the material. In addition, students higher in their self-efficacy for learning also are much more likely to be metacognitive and try to regulate their learning by monitoring and controlling their cognition as they learn. In our studies (see review by Pintrich, 1999), we have measures of these cognitive and selfregulatory strategies at the start of the course and at the end of the course, and self-efficacy remains a significant predictor of cognitive and self-regulatory strategy use at the end of the course, even when the earlier measure of cognition is included as a predictor along with self-efficacy. Accordingly, positive self-efficacy beliefs can boost cognitive and selfregulatory strategy use over the course of a semester.

In summary, an important first generalization about the role of motivational beliefs in classroom learning emphasizes the importance of self-efficacy beliefs.

Generalization 1: Self-efficacy beliefs are positively related to adaptive cognitive and self-regulatory strategy use as well as actual achievement in the classroom.

Accordingly, students who feel capable and confident about their capabilities to do the course work are much more likely to be cognitively engaged, to try hard, to persist, and to do well in the course. In fact, the strength of the relations between self-efficacy and these different outcomes in our research as well as others (Bandura, 1997; Eccles et al., 1998; Pintrich & Schunk, 2002; Schunk, 1991) suggests that self-efficacy is one of the best and most powerful motivational predictors of learning and achievement. Given the strength of the relations, research on the motivational aspects of student learning and performance needs to include self-efficacy as an important mediator between classroom contextual factors and student outcomes.

Value Components

Value components of the model incorporate individuals’ goals for engaging in a task as well as their beliefs about the importance, utility, or interest of a task. Essentially, these components concern the question Why am I doing this task? In more colloquial terms, value components concern whether students care about the task and the nature of that concern. These components should be related to cognitive and selfregulatory activities as well as outcomes such as the choice of activities, effort, and persistence (Eccles, 1983; Eccles et al., 1998; Pintrich, 1999). Although there are a variety of different conceptualizations of value, two basic components seem relevant: goal orientation and task value.

Goal Orientation

All motivational theories posit some type of goal, purpose, or intentionality to human behavior, although these goals may range from relatively accessible and conscious goals as in attribution theory to relatively inaccessible and unconscious goals as in psychodynamic theories (Zukier, 1986). In recent cognitive reformulations of achievement motivation theory, goals are assumed to be cognitive representations of the different purposes students may adopt in different achievement situations (Dweck & Elliott, 1983; Dweck & Leggett, 1988; Ford, 1992). In current achievement motivation research, there have been two general classes of goals that have been discussed under various names such as target and purpose goals (e.g., Harackiewicz, Barron, & Elliot, 1998; Harackiewicz & Sansone, 1991), or task-specific goals and goal orientations (e.g., Garcia & Pintrich, 1994; Pintrich & Schunk, 2002; Wolters, Yu, & Pintrich, 1996; Zimmerman & Kitsantas, 1997). The general distinction between these two classes of goals is that target and task-specific goals represent the specific outcome the individual is attempting to accomplish. In academic learning contexts, it would be represented by goals such as wanting to get a 85% out of 100% correct on a quiz, trying to get an A on a midterm exam, and so forth. These goals are specific to a task and are most similar to the goals discussed by Locke and Latham (1990) for workers in an organizational context such as wanting to make 10 more widgets an hour or to sell five more cars in the next week.

In contrast, purpose goals or goal orientations reflect the more general reasons individuals do a task and are related more to the research on achievement motivation (Elliot, 1997; Urdan, 1997). It is an individual’s general orientation (also called schema or theory) for approaching the task, doing the task, and evaluating his or her performance on the task (Ames, 1992; Dweck & Leggett, 1988; Pintrich, 2000a, 2000b, 2000c). In this case, purpose goals or goal orientations refer to why individuals want to get 85% out of 100%, why they want to get an A, or why they want to make more widgets or sell more cars as well as the standards or criteria (85%, an A) they will use to evaluate their progress towards the goal. Most of the research on classroom learning has focused on goal orientation—not specific target goals—so this research paper also focuses on the role of goal orientation in learning.

There are a number of different models of goal orientation that have been advanced by different achievement motivation researchers (cf. Ames, 1992; Dweck & Leggett, 1988; Harackiewicz et al., 1998; Maehr & Midgley, 1991; Nicholls, 1984; Pintrich, 1988a, 1988b, 1989; Wolters et al., 1996). These models vary somewhat in their definition of goal orientation and the use of different labels for similar constructs. They also differ on the proposed number of goal orientations and the role of approach and avoidance forms of the different goals. Finally, they also differ on the degree to which an individual’s goal orientations are more personal and based in somewhat stable individual differences, or the degree to which an individual’s goal orientations are more situated or sensitive to the context and a function of the contextual features of the environment. Most of the models assume that goal orientations are a function of both individual differences and contextual factors, but the relative emphasis along this continuum does vary between the different models. Much of this research also assumes that classrooms and other contexts (e.g., business or work settings, laboratory conditions in an experiment) can be characterized in terms of their goal orientations (see Ford, Smith, Weissbein, Gully, & Salas, 1998, for an application of goal orientation theory to a work setting), but for the purposes of this research paper the focus is on individuals’ personal goal orientation.

Most models propose two general goal orientations that concern the reasons or purposes individuals are pursuing when approaching and engaging in a task. In Dweck’s model, the two goal orientations are labeled learning and performance goals (Dweck & Leggett, 1988), with learning goals reflecting a focus on increasing competence and performance goals involving either the avoidance of negative judgments of competence or attainment of positive judgments of competence. Ames (1992) labels them mastery and performance goals, with mastery goals orienting learners to “developing new skills, trying to understand their work, improving their level of competence, or achieving a sense of mastery based on self-referenced standards” (Ames, 1992, p. 262). In contrast, performance goals orient learners to focus on their ability and self-worth, to determine their ability in reference to besting other students in competitions, surpassing others in achievements or grades, and receiving public recognition for their superior performance (Ames, 1992). Harackiewicz, Elliot, and their colleagues (e.g., Elliot, 1997; Elliot & Church, 1997; Elliot & Harackiewicz, 1996; Harackiewicz et al., 1998) have labeled them mastery and performance goals as well. Nicholls (1984) has used the terms taskinvolved and ego-involved for similar constructs (see Pintrich, 2000c, for a review). In this research paper we use the labels of mastery and performance goals.

In the literature on mastery and performance goals, the general theoretical assumption has been that mastery goals foster a host of adaptive motivational, cognitive, and achievement outcomes, whereas performance goals generate less adaptive or even maladaptive outcomes. Moreover, this assumption has been supported in a large number of empirical studies on goals and achievement processes (Ames, 1992; Dweck & Leggett, 1988; Pintrich, 2000c; Pintrich & Schunk, 2002)—in particular, the positive predictions for mastery goals. The logic of the argument is that when students are focused on trying to learn and understand the material and trying to improve their performance relative to their own past performance, this orientation will help them maintain their self-efficacy in the face of failure, ward off negative affect such as anxiety, lessen the probability that they will have distracting thoughts, and free up cognitive capacity and allow for more cognitive engagement and achievement. In contrast, when students are concerned about trying to be the best, get higher grades than do others, and do well compared to others under a performance goal, there is the possibility that this orientation will result in more negative affect or anxiety, increase the possibility of distracting and irrelevant thoughts (e.g., worrying about how others are doing rather than focusing on the task), and that this will diminish cognitive capacity, task engagement, and performance.

The research on the role of mastery and performance goals in learning and performance is fairly straightforward for mastery goals but not for performance goals. This research has included student use of strategies that promote deeper processing of the material as well as various metacognitive and self-regulatory strategies (Pintrich, 2000c). Much of this research is based on self-report data from correlational classroom studies, although Dweck and Leggett (1988) summarize data from experimental studies. The classroom studies typically assess students’goal orientations and then measure students reported use of different strategies for learning either at the same time or longitudinally. Although there are some problems with the use of self-report instruments for measuring self-regulatory strategies (see Pintrich, Wolters, & Baxter, 2000), these instruments do display reasonable psychometric qualities. Moreover, the research results are overwhelmingly consistent—mastery goals account for between 10 and 30% of the variance in the cognitive outcomes. Studies have been done with almost all age groups from elementary to college students and have assessed students’ goals for school in general as well as in the content areas of English, math, science, and social studies.

The studies have found that students who endorse a mastery goal are more likely to report attempts to self-monitor their cognition and to seek ways to become aware of their understanding and learning, such as checking for understanding and comprehension monitoring (e.g., Ames & Archer, 1988; Dweck & Leggett, 1988; Meece, Blumenfeld, & Hoyle, 1988; Meece & Holt, 1993; Middleton & Midgley, 1997; Nolen, 1988; Pintrich, 1999; Pintrich & De Groot, 1990; Pintrich & Garcia, 1991, 1993; Pintrich, Smith, Garcia, & McKeachie, 1993; Pintrich & Schrauben, 1992; Wolters et al., 1996). In addition, this research has consistently shown that students’ use of various cognitive strategies for learning is positively related to mastery goals. In particular, this research has shown that students’ reported use of deeper processing strategies such as the use of elaboration strategies (i.e., paraphrasing, summarizing) and organizational strategies (networking, outlining) is positively correlated with the endorsement of mastery goals (Ames & Archer, 1988; Bouffard, Boisvert, Vezeau, & Larouche, 1995; Graham & Golen, 1991; Kaplan & Midgley, 1997; Meece et al., 1988; Pintrich, 1999; Pintrich & De Groot, 1990; Pintrich & Garcia, 1991; Pintrich et al., 1993; Wolters et al., 1996). Finally, in some of this research, mastery goals have been negatively correlated with the use of less effective or surface processing strategies (i.e., rehearsal), especially in older students (Anderman & Young, 1994; Kaplan & Midgley, 1997; Pintrich & Garcia, 1991; Pintrich et al., 1993). In contrast to this research on the use of various self-regulatory and learning strategies, there has not been much research on how mastery goals are linked to the use of other problem-solving or thinking strategies. This is clearly an area that will be investigated in the future.

The research on performance goals and cognitive outcomes is not as easily summarized as are the results for mastery goals. The original goal theory research generally found negative relations between performance goals and various cognitive and behavioral outcomes (Ames, 1992; Dweck & Leggett, 1988), although it did not discriminate empirically between approach and avoidance performance goals. The more recent research that has made the distinction between approach and avoidance performance goals does show some differential relations between approaching a task focused on besting others and approaching a task focused on trying not to look stupid or incompetent. In particular, the general distinction between an approach and an avoidance orientation suggests that there could be some positive aspects of an approach performance orientation. If students are approaching a task trying to promote certain goals and strategies, it might lead them to be more involved in the task than are students who are trying to avoid certain goals, which could lead to more withdrawal and less engagement in the task (Harackiewicz et al., 1998; Higgins, 1997; Pintrich, 2000c).

Most of the research on performance goals that did not distinguish between approach and avoidance versions finds that performance goals are negatively related to students’use of deeper cognitive strategies (e.g., Meece et al., 1988; Nolen, 1988; cf., however, Bouffard, Boisvert, Vezeau, & Larouche, 1995). This finding would be expected, given that performance goals that include items about besting others as well as avoiding looking incompetent would guide students away from the use of deeper strategies. Students focused on besting others may be less likely to exert the time and effort needed to use deeper processing strategies because the effort needed to use these strategies could show to others that they lack the ability, given that the inverse relation between effortability is usually operative under performance goals, and trying hard in terms of strategy use may signify low ability. For students who want to avoid looking incompetent, the same self-worth protection mechanism (Covington, 1992) may be operating, whereby students do not exert effort in their strategy use in order to have an excuse for doing poorly—lack of effort or poor strategy use.

However, more recent research with measures that reflect only an approach or avoidance performance goal suggests that there may be differential relations between these two versions of performance goals. For example, Wolters et al. (1996) in a correlational study of junior high students found that—independent of the positive main effect of mastery goals—an approach performance goal focused on besting others was positively related to the use of deeper cognitive strategies and more regulatory strategy use. However, Kaplan and Midgley (1997) in a correlational study of junior high students found no relation between an approach performance goal and adaptive learning strategies, but approach performance goals were positively related to more surface processing or maladaptive learning strategies. These two studies did not include separate measures of avoid performance goals. In contrast, Middleton and Midgley (1997) in a correlational study of junior high students, found no relation between either approach or avoidance performance goals and cognitive self-regulation. Some of the differences in the results of these studies stem from the use of different measures, classroom contexts, and participants, making it difficult to synthesize the results. Clearly, there is a need for more theoretical development in this area and empirical work that goes beyond correlational self-report survey studies to clarify these relations.

One factor that adds to the complexity of the results in discussing approach and avoidance performance goals is that in Dweck’s original model (Dweck & Leggett, 1988), the links between performance goals and other cognitive and achievement outcomes were assumed to be moderated by efficacy beliefs—that is, if students had high perceptions of their competence to do the task, then performance goals should not be detrimental for cognition, motivation, and achievement, and these students should show the same basic pattern as mastery-oriented students. Performance goals were assumed to have negative effects only when efficacy was low. Students who believed they were unable and who were concerned with besting others or wanted to avoid looking incompetent did seem to show the maladaptive pattern of cognition, motivation, and behavior (Dweck & Leggett, 1988).

Other more correlational research that followed this work did not always explicitly test for the predicted interaction between performance goals and efficacy or did not replicate the predicted moderator effect. For example, both Kaplan and Midgley (1997) and Miller, Behrens, Greene, and Newman (1993) did not find an interaction between approach performance goals and efficacy on cognitive outcomes such as strategy use. Harackiewicz, Elliot, and their colleagues (Harackiewicz et al., 1998), using both experimental and correlational designs, did not find moderator or mediator effects of efficacy in relation to the effects of approach mastery or approach performance goals on other outcomes such as actual performance.

Nevertheless, it may be that approach performance goals could lead to deeper strategy use and cognitive self-regulation as suggested by Wolters et al. (1996) when students are confronted with overlearned classroom tasks that do not challenge them, interest them, or offer opportunities for much self-improvement (see also Pintrich, 2000b). In this case, the focus on an external criterion of besting others or being the best in the class could lead them to be more involved in these boring tasks and try to use more self-regulatory cognitive strategies to accomplish this goal. On the other hand, it may be that approach performance goals are not that strongly related to cognitive self-regulation in either a positive or negative way, as suggested by the results of Kaplan and Midgley (1997) and Middleton and Midgley (1997). Taken together, the conflicting results suggest that approach performance goals do not have to be negatively related to cognitive self-regulatory activities in comparison to avoidance performance goals. This conclusion suggests that there may be multiple pathways between approach and avoidance performance goals, cognitive strategy use and self-regulation, and eventual achievement. Future research should attempt to map out these multiple pathways and determine how approach and avoidance performance goals may differentially relate to cognitive self-regulation activities (Pintrich, 2000b, 2000c).

One of the most important behavioral outcomes is actual achievement or performance. Goals may promote different patterns of motivation, affect, and cognition, but they also should be linked to actual classroom achievement. The more experimental research on mastery goals has shown that students in mastery conditions usually achieve or perform at higher levels (Dweck & Leggett, 1988). In fact, given all the positive motivational, affective, and cognitive outcomes associated with mastery goals, it would be expected that mastery goals would also lead to higher levels of achievement. However, in some of the correlational classroom studies, this does not seem to be the case (e.g., Elliot, McGregor, & Gable, 1999; Harackiewicz et al., 1998; Harackiewicz, Barron, Carter, Lehto, & Elliot, 1997; Pintrich, 2000c; VanderStoep, Pintrich, & Fagerlin, 1996). The pattern that seems to emerge is that mastery goals are unrelated to performance or achievement in the classroom, usually indexed by grades or grade point average (GPA). In contrast, in some of these studies, approach performance goals (trying to be better than others) are associated with better grades or higher GPAs (Elliot et al., 1999; Harackiewicz et al., 1997, 1998).

This newer research on the role of performance goals has led some researchers to develop a revised goal theory perspective (e.g., Elliot, 1997; Harackiewicz et al., 1998; Pintrich, 2000c). They have suggested that there is a need to move beyond the simple dichotomy of mastery goals as good-adaptive versus performance goals as bad-maladaptive to a conceptualization of the different goals as being adaptive or maladaptive for different types of cognitive, motivational, affective, and behavioral outcomes. In other words, depending on what outcome is under consideration, goals may be adaptive or maladaptive—for example, mastery goals might lead to more interest and intrinsic motivation, but approach performance goals might lead to better performance (Harackiewicz et al., 1998). It is important to note that a revised perspective on goal theory and the normative perspective are in complete agreement about the detrimental effects of avoid performance goals. The main revision proposed is that approach performance goals may be adaptive for some outcomes. In addition, the concept of equifinality, or the idea that there are multiple means to accomplish a goal, suggests that there may be multiple pathways or trajectories of development that are set in motion by different goals, and these different pathways can lead to similar outcomes overall (Pintrich, 2000c; Shah & Kruglanski, 2000). Finally, there may be interactions between multiple goals, and these interactions can lead to different patterns of outcomes that are more complex than the simple linear relations suggested by normative goal theory under the mastery-good and performance-bad generalization (Pintrich, 2000c).

In contrast, Midgley, Kaplan, and Middleton (2001) have argued that there is no need to revise goal theory and that the basic assumption that mastery goals are adaptive and performance goals are maladaptive is still the best overall generalization from goal theory. They suggest that most of the research on the positive effects of approach performance goals are for special cases, such as for students high in self-efficacy (Dweck & Leggett, 1988), for students high in mastery goals as well approach performance goals (Pintrich, 2000c), or in contexts such as competitive college classrooms (Harackiewicz et al., 1998) in which there may be an advantage to adopting performance goals. Moreover, they note that classrooms and schools are often inherently performance-oriented and competitive to begin with, and that any suggestion by researchers that approach performance goals are adaptive would encourage teachers and school personnel to continue to stress the competitive nature of schooling, with the continued many detrimental effects for many schoolchildren. This issue is currently a very active area of research and there will no doubt be continued research and clarification of these issues as the field progresses.

In summary, the research on goal orientation suggests that at this point in time only one stable generalization can be made, given the diversity in findings.

Generalization 2: Mastery goals are positively related to adaptive cognitive and self-regulatory strategy use in the classroom. Students who adopt a mastery goal and focus on learning, understanding, and self-improvement are much more likely to use adaptive cognitive and self-regulatory strategies and to be deeply engaged in learning. Accordingly, classroom contexts that foster the adoption of mastery goals by students should facilitate motivation and learning. For example, classrooms that encourage students to adopt goals of learning and understanding through the reward and evaluation structures (i.e., how grades are assigned, how tasks are graded and evaluated) rather than just getting good grades or competing with other students should foster a mastery goal orientation. At the same time, this generalization does not mention higher levels of actual achievement, as indexed by grades, because the research is still mixed on this outcome.

Task Value

Goal orientation can refer to students’goals for a specific task (a midterm exam) as well as a general orientation to a course or a field. In the same way, students’task value beliefs can be rather specific or more general. Three components of task value have been proposed by Eccles (1983) as important in achievement dynamics: the individual’s perception of the importance of the task, his or her personal interest in the task (similar to intrinsic interest in intrinsic motivation theory), and his or her perception of the utility value of the task for future goals. These three value components may be rather parallel in children and college students but can vary significantly in adults (Wlodkowski, 1988).

The importance component of task value refers to individuals’perception of the task’s importance or salience for them. The perceived importance of a task is related to a general goal orientation, but importance could vary by goal orientation.An individual’s orientation may guide the general direction of behavior, whereas value may relate to the level of involvement. For example, a student may believe that success in a particular course is very important (or unimportant) regardless of his or her intrinsic or extrinsic goals—that is, the student may see success in the course as learning the material or getting a good grade, but he or she still may attach differential importance to these goals. Importance should be related to individuals’persistence at a task as well as choice of a task.

Student interest in the task is another aspect of task value. Interest is assumed to be individuals’general attitude or liking of the task that is somewhat stable over time and a function of personal characteristics. In an educational setting, this component includes the individual’s interest in the course content and reactions to the other characteristics of the course such as the instructor (cf. Wlodkowski, 1988). Personal interest in the task is partially a function of individuals’pBibliography: as well as aspects of the task (e.g., Malone & Lepper, 1987). However, personal interest should not be confused with situational interest, which can be generated by simple environmental features (e.g., an interesting lecture, a fascinating speaker, a dramatic film) but that are not long-lasting and do not necessarily inculcate stable personal interest (Hidi, 1990). Schiefele (1991) has shown that students’personal interest in the material being studied is related to their level of involvement in terms of the use of cognitive strategies as well as actual performance. There is a current revival in research on the role of interest in learning after a hiatus in research on this important motivational belief (see Renninger, Hidi, & Krapp, 1992; Sansone & Harackiewicz, 2000).

In contrast to the means or process motivational dynamic of interest, utility value refers to the ends or instrumental motivation of the student (Eccles, 1983). Utility value is determined by the individual’s perception of the usefulness of the task for him or her. For students, utility value may include beliefs that the course will be useful for them immediately in some way (e.g., help them cope with college), in their major (e.g., they need this information for upper-level courses), or their career and life in general (e.g., this will help them somehow in graduate school). At a task level, students may perceive different course assignments (e.g., essay and multiple-choice exams, term papers, lab activities, class discussion) as more or less useful and decide to become more or less cognitively engaged in the task.

Research on the value components has shown that they are consistently positively related to student engagement and cognition in the classroom setting (e.g., Pintrich, 1999). Not surprisingly, students who believe that schoolwork or course work is more important, interesting, and useful to them are more likely to be cognitively engaged in the learning activities. In this work, self-efficacy has been a stronger predictor of engagement,buttaskvaluebeliefsalsoshowpositiverelations (Pintrich, 1999). In longitudinal research on the role of expectancy and value components in academic settings, Eccles and her colleagues (Eccles et al., 1998) have found a similar pattern of results. Their work has shown that value beliefs are better predictors of choice behavior, whereas expectancy components (i.e., self-efficacy and perceived competence) are better predictors of actual achievement. In other words, task value beliefs help to predict what courses students might take (e.g., higher level math or science courses), but after students actually enroll in those courses, self-efficacy and perceived competence are better predictors of their performance. This differential prediction of outcomes for different motivational beliefs is an important finding in motivational research.

Arelated vein of research from an intrinsic motivation perspective (Deci & Ryan, 1985; Ryan & Deci, 2000) has suggested that interest (one of the components of task value) is an important associated process with being intrinsically motivated (enjoyment is another associated process). In this theoretical perspective, intrinsic motivation is represented by individuals choosing to do a task freely and feeling selfdetermined or autonomous in their behavior while doing the task. This form of intrinsic motivation should result in the most adaptive levels of motivation, cognition, and behavior. Students who are intrinsically motivated should be interested in the task, enjoy it, be more likely to be cognitively engaged, and also perform at high levels (Deci & Ryan, 1985). Although this perspective makes some different metatheoretical assumptions about human nature and human behavior, the functional role of intrinsic interest is similar to that of personal interest in an expectancy-value model.

In addition, in intrinsic motivation models, individuals can be motivated in more extrinsic ways as well, some of which are similar to the components of importance and utility from expectancy-value models. Deci and Ryan recognize that not all behavior is intrinsically motivated. They propose four levels of external regulation or extrinsic motivation (Ryan & Deci, 2000). The first level includes what they call external regulation. For example, students initially may not want to work on math but do so to obtain teacher rewards and avoid punishment.Thesestudentswouldreactwelltothreatsofpunishment or the offer of extrinsic rewards and would tend to be compliant. They would not be intrinsically motivated or show high interest, but they would tend to behave well and do try to do the work to obtain rewards or avoid punishment. Obviously, the control is external in this case and there is no selfdetermination on the part of the students, but this level of motivation could result in good performance or achievement.

At the next level of extrinsic motivation, students may engage in a task because they think they should and may feel guilty if they don’t do the task (e.g., study for an exam). Deci and Ryan call this introjected regulation because the source of motivation is internal (feelings of should, ought, guilt) to the person but not self-determined because these feelings seem to be controlling the person. The person is not doing the task solely for the rewards or to avoid punishment; the feelings of guilt or should are actually internal to the person, but the source is still somewhat external because he or she may be doing the task to please others (teacher, parents). Again, Deci and Ryan assume that this level of motivation also could have some beneficial outcomes for engagement, persistence, and achievement.

The third level or style is called identified regulation. Individuals engage in the activity because it is personally important to them. In this case, this style is similar to what Eccles and her colleagues (Eccles et al., 1998) call the importance and utility aspects of task value. For example, a student may study hours for tests in order to get good grades to be accepted into college. This behavior represents the student’s own goal, although the goal has more utility value (Wigfield & Eccles, 1992) than it does intrinsic value such as learning. The goal is consciously chosen by the student; in this sense, the locus of causality is somewhat more internal to the person as the person feels it is very important to him- or herself, not just to others such as teachers or parents. In this case, students want to do the task because it is important to them, even if it is more for utilitarian reasons rather than intrinsic interest in the task.

The final level of extrinsic motivation is integrated regulation, whereby individuals integrate various internal and external sources of information into their own self-schema and engage in behavior because of its importance to their sense of self. This final level is still instrumental rather than autotelic (as in intrinsic motivation), but integrated regulation does represent a form of self-determination and autonomy. As such, both intrinsic motivation and integrated regulation will result in more cognitive engagement and learning than do external or introjected regulation (Rigby et al., 1992; Ryan & Deci, 2000).

These findings from both expectancy-value, interest, and intrinsic motivation research lead to a third generalization.

Generalization 3: Higher levels of task value (importance, interest, and utility) are associated with adaptive cognitive outcomes such as higher levels of self-regulatory strategy use as well as higher levels of achievement. This generalization may not be surprising, but it is important to formulate because constructs like value, utility, and interest are often considered to be unrelated to cognitive outcomes or achievement, and they are considered to be important noncognitive outcomes. It is of course important to foster value, utility, and interest as outcomes in their own right, but the generalization suggests that by facilitating the development of task value in the classroom, an important by-product will be more cognitive engagement, self-regulation, and achievement. For example, the use of materials (e.g., tasks, texts, articles) that are meaningful and interesting to students can foster increased levels of task value. In addition, class activities (demonstrations, small group activities) that are useful, interesting, and meaningful to students will facilitate the development of task value beliefs and classroom learning.

Affective Components

Affective components include students’ emotional reactions to the task and their performance (i.e., anxiety, pride, shame) and their more emotional needs for self-worth or self-esteem, affiliation, and self-actualization (cf. Covington & Beery, 1976; Veroff & Veroff, 1980). Affective components address the basic question How does the task make me feel? In terms of the links between cognition and affect, there has been a long history of research on the causal ordering of cognition and affect (cf. Smith & Kirby, 2000; Weiner, 1986; Zajonc, 1980, 2000). Like many of these disagreements (i.e., the debate over the causal precedence of self-concept versus achievement; Wigfield & Karpathian, 1991), the current and most sensible perspective is that the influence is bidirectional. It is not clear that there is a need to continue to argue over whether cognition precedes affect or vice versa, but rather to develop models that help educational psychologists understand (a) how, why, and when (under what conditions) does cognition precede and influence affect and (b) how, why, and when affect precedes and influences cognition. Nevertheless, in this section we do focus on how affect might facilitate or constrain cognition and learning.

In terms of the relations between affect and subsequent cognition, learning, and performance, Pekrun (1992) has suggested that there are four general routes by which emotions or mood might influence various outcomes (see also Linnenbrink & Pintrich, 2000). Three of these routes are through cognitive mediators, and the fourth is through a motivational pathway. The different models and constructs discussed in this research paper illustrate all four of these routes quite well; here, we give a brief overview of the four pathways as an advance organizer.

The first route by which emotions or mood might influence learning and performance is through memory processes such as retrieval and storage of information (Pekrun, 1992). There is quite a bit of research on mood-dependent memory with the general idea being that affective states such as mood get encoded at the same time as other information and that the affect and information are intimately linked in an associative network (Bower, 1981; Forgas, 2000). This leads to findings such as affect-state dependent retrieval, in which retrieval of information is enhanced if the person’s mood at the retrieval task matches the person’s mood at the encoding phase (Forgas, 2000). Forgas (2000) also notes that some findings show that mood or affective state facilitates the recall of affectively congruent material, such that people in a good mood are more likely to recall positive information and people in a bad mood are more likely to recall negative information. In other work, Linnenbrink and Pintrich (2000) and Linnenbrink, Ryan, and Pintrich (1999) suggest that negative affect might influence working memory by mediating the effects of different goal orientations. In this work, it appears that negative affect might have a detrimental effect on working memory, but positive affect was unrelated to working memory. This general explanation for the integration of encoding, retrieval, and affective processes is one of the main thrusts of the personal and situational interest research that is discussed later in this research paper.

The second mediational pathway that Pekrun (1992) suggests is that affect influences the use of different cognitive, regulatory, and thinking strategies (cf. Forgas, 2000), whichcould then lead to different types of achievement of performance outcomes. For example, some of the original research suggested that positive mood produced more rapid, less detailed, and less systematic processing of information, whereas negative mood resulted in more systematic, analytical, or detailed processing of information (Forgas, 2000; Pekrun, 1992). However, recent work suggests that this position is too simplistic, and more complex proposals have been made. For example, Fiedler (2000) has suggested that positive affect as a general approach orientation facilitates more assimilation processes including generative, top-down, and creative processes, including seeking out novelty. In contrast, he suggests that negative mood reflects a more aversive or avoidance orientation and can result in more accommodation including a focus more on external information and details, as well as being more stimulus bound and less willing to make mistakes.

Other research on the use of cognitive and self-regulatory strategies in school settings has not addressed the role of affect in great detail; the few studies that have, however, show that negative affect decreases the probability that students will use cognitive strategies that result in deeper, more elaborative processing of the information (Linnenbrink & Pintrich, 2000). For example, Turner, Thorpe, and Meyer (1998) found that negative affect was negatively related to elementary students’deeper strategy use. Moreover, negative affect mediated the negative relation between performance goals and strategy use. If negative affect or emotion is a generally aversive state, it makes sense that students who experience negative affect are less likely to use deeper processing strategies because such strategies require much more engagement and a positive approach to the academic task. In contrast, positive affect should result in more engagement and deeper strategy use. This latter argument is also consistent with some of the findings from the personal and situational interest research discussed later in this research paper.

The third cognitive pathway that Pekrun (1992) suggests is that affect can increase or decrease the attentional resources that are available to students. Linnenbrink and Pintrich (2000) make a similar argument. As Pekrun (1992) notes, emotions can take up space in working memory and increase the cognitive load for individuals. For example, if a student is trying to do an academic task and at the same time is having feelings of fear or anxiety, these feelings (and their accompanying cognitions about worry and self-doubt) can take up the limited working memory resources and can interfere with the cognitive processing needed to do the academic task (Hembree, 1988; Zeidner, 1998). In fact, this general interference or cognitive load explanation is a hallmark of work on test anxiety that is discussed in more detail later in this research paper. Under this general cognitive load hypothesis, it might be expected that any emotion—positive or negative— would take up attentional resources and result in reduced cognitive processing or performance. However, this does not seem to be the case, given the differential and asymmetrical findings for positive and negative affect (Forgas, 2000), so it is clear that there is a need for further exploration of how emotions and mood can influence attentional resources and ultimately performance.

The fourth and final general pathway that Pekrun (1992) suggests is that emotions can work through their effect on intrinsic and extrinsic motivational processes. Linnenbrink and Pintrich (2000) also have suggested that motivational and affective processes can interact to influence cognitive and behavioral outcomes. Under this general assumption, positive emotions such as the experience of enjoyment in doing a task or even anticipatory or outcome-related joy of a task may lead to intrinsic motivation for the task. Of course, negative emotions such as boredom, sadness, or fear should decrease intrinsic motivation for doing the task, albeit some of them (e.g., fear) might increase the extrinsic motivation for the task. It seems clear that affective and motivational processes can interact and through these interactions can influence cognition, learning, and performance (Linnenbrink & Pintrich, 2000).At the same time, there is a need for much more research on how to effectively integrate affective processes with the motivational and cognitive processes that have been examined in much more detail. This question is sure to be one of the major areas of future research in achievement motivation research. We now turn to some of the specific constructs and models that have integrated affective processes with motivational and cognitive processes to better explain learning and achievement.

Anxiety

There is a long history of research on test anxiety and its general negative relationship to academic performance (Covington, 1992; Zeidner, 1998). Test anxiety is one of the most consistent individual difference variables that can be linked to detrimental performance in achievement situations (Hill & Wigfield, 1984). The basic model assumes that test anxiety is a negative reaction to a testing situation that includes both a cognitive worry component and a more emotional response (Liebert & Morris, 1967). The worry component consists of negative thoughts about performance while taking the exam (e.g., I can’t do this problem. That means I’m going to flunk, what will I do then?) that interfere with the students’ ability to actually activate the appropriate knowledge and skills to do well on the test. These self-perturbing ideations (Bandura, 1986) can build up over the course of the exam and spiral out of control as time elapses, which then creates more anxiety about finishing in time. The emotional component involves more visceral reactions (e.g., sweaty palms, upset stomach) that also can interfere with performance.

Zeidner (1998) in his review of the research on test anxiety and information processing notes that anxiety generally has a detrimental effect on all phases of cognitive processing. In the planning and encoding phase, individuals with high levels of anxiety have difficulty attending to and encoding appropriate information about the task. In terms of actual cognitive processes while doing the task, high levels of anxiety lead to less concentration on the task, difficulties in the efficient use of working memory, more superficial processing and less in-depth processing, and problems in using metacognitive regulatory processes to control learning (Zeidner, 1998). Of course, these difficulties in cognitive processing and self-regulation usually result in less learning and lower levels of performance.

In summary, research on test anxiety leads to a fourth generalization.

Generalization 4: High levels of test anxiety are generally not adaptive and usually lead to less adaptive cognitive processing, less adaptive self-regulation, and lower levels of achievement. This generalization is based on a great deal of both experimental and correlational work as reviewed by Zeidner (1998). Of course, Zeidner (1998) notes that there may be occasions when some aspects of anxiety may lead to some facilitating effects for learning and performance. For example, Garcia and Pintrich (1994) have suggested that some students, called defensive pessimists (Norem & Cantor, 1986), can use their anxiety about doing poorly to motivate themselves to try harder and study more, leading to better achievement. The harnessing of anxiety for motivational purposes is one example of a self-regulating motivational strategy that students might use to regulate their learning. Nevertheless, in the case of test anxiety, which is specific to testing situations, the generalization still holds that students who are very anxious about doing well do have more difficulties in cognitive processing and do not learn or perform as well as might be expected. One implication is that teachers need to be aware of the role of test anxiety in reducing performance and try to reduce the potential debilitating effects in their own classrooms.

Other Affective Reactions

Besides anxiety, other affective reactions can influence choice and persistence behavior. Weiner (1986, 1995) in his attributional analysis of emotion has suggested that certain types of emotions (e.g., anger, pity, shame, pride, guilt) are dependent on the types of attributions individuals make for their successes and failures. For example, this research suggests that a instructor will tend to feel pity for a student who did poorly on an exam because of some uncontrollable reason (e.g., death in family) and would be more likely to help that student in the future. In contrast, a instructor is more likely to feel anger at a student who did poorly through a simple lack of effort and be less willing to help that student in the future. In general, an attributional analysis of motivation and emotion has been shown repeatedly to be helpful in understanding achievement dynamics (Weiner, 1986), and there is a need for much more research on these other affective reactions in the classroom.

Emotional Needs

The issue of an individual’s emotional needs (e.g., need for affiliation, power, self-worth, self-esteem, self-actualization) is related to the motivational construct of goal orientation, although the needs component is assumed to be less cognitive, more affective, and perhaps less accessible to the individual. There have been a number of models of emotional needs suggested (e.g., Veroff & Veroff, 1980; Wlodkowski, 1988), but the need for self-worth or self-esteem seems particularly relevant. Research on student learning shows that self-esteem or sense of self-worth has often been implicated in models of school performance (e.g., Covington, 1992; Covington & Beery, 1976). Covington (1992) has suggested that individuals are always motivated to establish, maintain, and promote a positive self-image. Given that this hedonic bias is assumed to be operating at all times, individuals may develop a variety of coping strategies to maintain self-worth; at the same time, however, these coping strategies may actually be self-defeating.Covingtonandhiscolleagues(e.g.,Covington, 1984; Covington & Berry, 1976; Covington & Omelich, 1979a, 1979b) have documented how several of these strategies can have debilitating effects on student performance. Many of these poor coping strategies hinge on the role of effort and the fact that effort can be a double-edged sword (Covington & Omelich, 1979a). Students who try harder will increase the probability of their success, but they also increase their risk of having to make an ability attribution for failure, followed by a drop in expectancy for success and self-worth (Covington, 1992).

There are several classic failure-avoiding tactics that demonstrate the power of the motive to maintain a sense of self-worth. One strategy is to choose easy tasks. As Covington (1992) notes, individuals may choose tasks that ensure success although the tasks do not really test the individuals’ actual skill level. Students may choose this strategy by continually electing easy tasks, easy courses, or easy majors. A second failure-avoiding strategy involves procrastination. For example, a student who does not prepare for a, test because of lack of time, can—if successful—attribute it to superior aptitude. On the other hand, this type of procrastination maintains an individual’s sense of self-worth because if the student is not successful, he or she can attribute the failure to lack of study time, not poor skill. Of course, this type of effort-avoiding strategy increases the probability of failure over time, which will result in lowered perceptions of self-worth; it is thus ultimately self-defeating.

In summary, although less researched, affective components can influence students’ motivated behavior. Moreover, as the analysis of the self-worth motive shows (Covington, 1992), the affective components can interact with other more cognitive motivational beliefs (i.e., attributions) as well as self-regulatory strategies (management of effort) to influence achievement. However, we do not offer any generalizations for these components, given that they have not been subject to the same level of empirical testing as the other motivational components.

Conclusion and Future Directions for Research

The four generalizations about the relations between motivational constructs and classroom cognition and learning demonstrate the importance of considering how motivation can facilitate or constrain cognition. There is no longer any doubt that academic learning is hot, so to speak, and involves motivationandaffect(Pintrich,Marx,&Boyle,1993)andthat contrary to Brown et al., academic cognition is not cold and concerned only with the efficiency of knowledge and strategy use. However, that being said, there is still much we still do not understand, and there are a number of directions for future research.

First, much of the work on motivation and classroom learning has been conducted from a motivational perspective and— following a motivational paradigm—has used self-report questionnaires to measure both motivation and strategy use and self-regulated learning in actual classrooms. This work has provided us with insight into how different motivational beliefs can facilitate or constrain cognition; it has also been ecologically valid, given its focus on classrooms.At the same time, due to the inherent limitations of self-reports (Pintrich et al., 2000), the work has not been able to delve deeply into the cognitive processes and mechanisms, at least not at the level at which most cognitive psychologists operate in their own research. Accordingly, there is a need for more detailed and fine-grained analysis of the linkages between motivation and cognition, more akin to what cognitive psychologists have undertaken in their laboratory studies of cognition. Of course, this will require more experimental and laboratory work, which of course immediately lowers the ecological validity and makes it difficult to assess the participants’motivation for doing a laboratory task. However, at this point in the development of our science, these trade-offs are reasonable because we need to build on these generalizations to really understand how motivation influences basic cognitive and learning processes.

Related to this first issue, much of the work reported on in this research paper has focused on use of general learning strategies andself-regulatedlearning.Ithasnotexaminedinmuchdetail how motivation relates to domain-specific knowledge activation and use, such as conceptual change (Pintrich, Marx, & Boyle, 1993), or to other types of cognition such as thinking, reasoning, and problem solving in general or in domains such as mathematics or science. Accordingly, there is a need both for correlational field studies and for more experimental work on how different motivational beliefs can facilitate and constrain these cognitive and learning processes.

Athird issue relates to the general developmental progression of the relations between motivation and cognition. The four generalizations offered here have been derived from work that has focused on elementary school through college students but has not really been developmental in focus. There have not been many longitudinal studies of these relations and there may important changes in the nature of these relations over time. In addition, there has not been very much research on the development of expertise or on how the nature of the relations between motivation and cognition may change as a individual gains more experience and knowledge with a particular domain of tasks (Pintrich & Zusho, 2001). Accordingly, there is a need for microgenetic studies of how motivation and cognition unfold over the course of the development of expertise with a task, as well as more macrolevel longitudinal studies of motivation and self-regulation over the life course.

Besides developmental differences, there are of course other potential individual difference variables that may moderate the relations between motivation and cognition. Gender may be one, although there have not been many gender differences in the relations between motivation and cognition, albeit there can be gender differences in levels and quality of motivation (Eccles et al., 1998; Pintrich & Schunk, 2002). More important is that for building generalizable models of motivation and cognition, there is a need to understand whether these generalizations hold across different ethnic groups and cultures. Graham (1992, 1994) has already pointed out the lack of research on African American students’ motivation, let alone research on motivation and cognition in diverse populations. If educational psychologists are able to propose generalizations about motivation and cognition, then these generalizations should apply to all ethnic groups.At this time, however, little empirical research has been conducted to support the generalizations in different groups. In addition, there is a need to test these generalizations in different cultures to see whether the same relations obtain. There may be important differences in ethnic groups or in different cultures that moderate the relations between motivation and cognition. There is a clear need for more research on these possibilities.

Finally, although this research paper has not focused on the role of classroom factors in generating, shaping, and scaffolding student motivation and cognition, classrooms do have clear effects on motivation and cognition (Bransford et al., 1999; Pintrich & Schunk, 2002). However, following the general logic of potential moderator effects for different ethnic or cultural groups, we do not know whether different classroom cultures might also moderate these four generalizations about motivation and cognition. There may be classrooms in which self-efficacy, interest, goals, or anxiety play different roles in supporting or constraining different types of cognition than in traditional classrooms. A great deal of school and classroom reform is currently on-going, and classrooms are becoming quite different places because of the technology and curriculum changes that are being implemented. These new classroom environments might afford quite different opportunities for student motivation and cognition, and we have little empirical work on such possibilities.

Nevertheless, we do know more about how motivation and cognition relate to one another in classroom settings than we did even 20 years ago. The four generalizations presented here do represent our best knowledge at this time in the development of our scientific understanding. Much more remains to be done to be sure, but the theoretical foundation and empirical base are solid and should provide important guidance not only to researchers, but also to educators who wish to improve student motivation and learning in the classroom.

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