Handbook of psychology volume 7 educational psychology

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 de-

bate over the causal precedence of self-concept versus

achievement; Wigfield & Karpathian, 1991), the current and

most sensible perspective is that the influence is bidirec-

tional. It is not clear that there is a need to continue to argue

over whether cognition precedes affect or vice versa, but

The Role of Motivational Components in Classroom Learning

115

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 chapter illustrate all four of these routes

quite well; here, we give a brief overview of the four path-

ways as an advance organizer.

The first route by which emotions or mood might influ-

ence 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 associa-

tive network (Bower, 1981; Forgas, 2000). This leads to find-

ings 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 re-

call 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 infor-

mation. In other work, Linnenbrink and Pintrich (2000) and

Linnenbrink, Ryan, and Pintrich (1999) suggest that negative

affect might influence working memory by mediating the ef-

fects of different goal orientations. In this work, it appears

that negative affect might have a detrimental effect on work-

ing memory, but positive affect was unrelated to working

memory. This general explanation for the integration of en-

coding, retrieval, and affective processes is one of the main

thrusts of the personal and situational interest research that is

discussed later in this chapter.

The second mediational pathway that Pekrun (1992) sug-

gests is that affect influences the use of different cognitive, reg-

ulatory, and thinking strategies (cf. Forgas, 2000), which could

then lead to different types of achievement of performance

outcomes. For example, some of the original research sug-

gested that positive mood produced more rapid, less detailed,

and less systematic processing of information, whereas nega-

tive mood resulted in more systematic, analytical, or detailed

processing of information (Forgas, 2000; Pekrun, 1992). How-

ever, 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, in-

cluding seeking out novelty. In contrast, he suggests that neg-

ative 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 af-

fect 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 gen-

erally aversive state, it makes sense that students who experi-

ence negative affect are less likely to use deeper processing

strategies because such strategies require much more engage-

ment and a positive approach to the academic task. In con-

trast, 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 chapter.

The third cognitive pathway that Pekrun (1992) suggests

is that affect can increase or decrease the attentional re-

sources 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 in-

crease 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 inter-

fere with the cognitive processing needed to do the academic

task (Hembree, 1988; Zeidner, 1998). In fact, this general in-

terference or cognitive load explanation is a hallmark of work

on test anxiety that is discussed in more detail later in this

chapter. 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

116

Motivation and Classroom Learning

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 in-

trinsic and extrinsic motivational processes. Linnenbrink and

Pintrich (2000) also have suggested that motivational and af-

fective processes can interact to influence cognitive and behav-

ioral 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 effec-

tively integrate affective processes with the motivational and

cognitive processes that have been examined in much more de-

tail. 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 re-

sponse (Liebert & Morris, 1967). The worry component con-

sists 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 in-

volves 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 anxi-

ety 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 anxi-

ety lead to less concentration on the task, difficulties in the ef-

ficient use of working memory, more superficial processing

and less in-depth processing, and problems in using metacog-

nitive 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 gen-

eralization.

Generalization 4: High levels of test anxiety are generally

not adaptive and usually lead to less adaptive cognitive pro-

cessing, 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 pur-

poses is one example of a self-regulating motivational strat-

egy 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 diffi-

culties 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 per-

formance 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 sug-

gests 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

Conclusion and Future Directions for Research

117

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 emo-

tion has been shown repeatedly to be helpful in understand-

ing achievement dynamics (Weiner, 1986), and there is a

need for much more research on these other affective reac-

tions in the classroom.

Emotional Needs

The issue of an individual’s emotional needs (e.g., need for af-

filiation, power, self-worth, self-esteem, self-actualization) is

related to the motivational construct of goal orientation, al-

though 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 sug-

gested (e.g., Veroff & Veroff, 1980; Wlodkowski, 1988), but

the need for self-worth or self-esteem seems particularly rele-

vant. 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 individu-

als are always motivated to establish, maintain, and promote

a positive self-image. Given that this hedonic bias is as-

sumed 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. Covington and his colleagues (e.g., Covington,

1984; Covington & Berry, 1976; Covington & Omelich,

1979a, 1979b) have documented how several of these strate-

gies can have debilitating effects on student perfor-

mance. 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 indi-

viduals’ actual skill level. Students may choose this strategy

by continually electing easy tasks, easy courses, or easy

majors. A second failure-avoiding strategy involves procras-

tination. 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 pro-

crastination 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 compo-

nents 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 motiva-

tional components.

CONCLUSION AND FUTURE DIRECTIONS

FOR RESEARCH

The four generalizations about the relations between motiva-

tional 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

motivation and affect (Pintrich, Marx, & Boyle, 1993) and that

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 learn-

ing 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

118

Motivation and Classroom Learning

doing a laboratory task. However, at this point in the develop-

ment 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 chapter has focused on use of general learning strategies

and self-regulated learning. It has not examined in much detail

how motivation relates to domain-specific knowledge activa-

tion 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 con-

strain these cognitive and learning processes.

A third issue relates to the general developmental progres-

sion 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 rela-

tions 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 na-

ture 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 devel-

opment 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 mod-

erate the relations between motivation and cognition. Gender

may be one, although there have not been many gender differ-

ences in the relations between motivation and cognition, albeit

there can be gender differences in levels and quality of moti-

vation (Eccles et al., 1998; Pintrich & Schunk, 2002). More

important is that for building generalizable models of motiva-

tion and cognition, there is a need to understand whether these

generalizations hold across different ethnic groups and cul-

tures. 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 generaliza-

tions about motivation and cognition, then these generaliza-

tions 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 mod-

erate the relations between motivation and cognition. There is

a clear need for more research on these possibilities.

Finally, although this chapter 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 curricu-

lum changes that are being implemented. These new class-

room 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 devel-

opment of our scientific understanding. Much more remains

to be done to be sure, but the theoretical foundation and em-

pirical base are solid and should provide important guidance

not only to researchers, but also to educators who wish to im-

prove student motivation and learning in the classroom.