Handbook of psychology volume 7 educational psychology


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CONCLUSION

The premise underlying information processing theory is that

human mental life consists of building and manipulating men-

tal representations. The information processing view has im-

portant implications for education, including implications for

how to improve instruction in subject matter areas such as read-

ing, writing, and mathematics. Research and theory on human

information processing points to the reciprocal relation be-

tween psychology and education: Educational practice can be

improved when it is informed by an understanding of how the

human mind works, and theories of how the human mind works

can be improved when they are informed by studies involving

how students perform on authentic academic tasks.


56

Memory and Information Processes

Admittedly, the information processing approach is lim-

ited. For example, by focusing mainly on cognition in indi-

vidual learners, it fails to incorporate affective, motivational,

emotional, social, and biological aspects of learning and in-

struction. All of these aspects must eventually be integrated

into a far-reaching theory of how the human mind works.

One promising approach is to include motivational strategies

along with cognitive strategies in teaching students how to

learn (Mayer, 2002).

Yet the information processing approach—now a domi-

nant force in psychology for nearly half a century—also

leaves a worthwhile legacy. The information processing ap-

proach enabled the rebirth of cognitive psychology by pro-

viding an alternative to behaviorism, created a unified

framework that stimulated useful research and theory, high-

lighted the role of mental representations and cognitive

processes, and fostered the transition toward studying cogni-

tion in more authentic contexts. Many of the current ad-

vances in educational research—ranging from cognitive

strategy instruction to the psychology of subject matter—

were enabled by the information processing approach in psy-

chology. Examples were provided in the foregoing sections,

but much more work is needed.

Overall, the information processing approach continues to

play a constructive role in the development of educationally

relevant theories of how the human mind works. In particu-

lar, the constructivist view of learners as sense makers and

mental model builders offers a potentially powerful concep-

tion of human cognition. A particularly useful approach in-

volves the refinement of techniques for analyzing academic

tasks into constituent processes that can be evaluated and

taught.

REFERENCES

Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruickshank,

K. A., Mayer, R. E., Pintrich, P. R., Raths, J., & Wittrock, M. C.

(2001). A taxonomy of learning, teaching, and assessing. New

York: Longman.

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A pro-

posed system and its control processes. In K. W. Spence & J. T.

Spence (Eds.), Advances in the psychology of learning and



motivation research and theory (Vol. 2, pp. 89–195). New York:

Academic Press.

Ausubel, D. P. (1968). Educational psychology: A cognitive view.

New York: Holt, Rinehart, & Winston.

Baddeley, A. L. (1998). Human memory. Boston: Allyn and Bacon.

Bartlett, E. J. (1982). Learning to revise: Some component

processes. In M. Nystrand (Ed.), What readers know. New York:

Academic Press.

Bartlett, F. C. (1932). Remembering. Cambridge, England: Cambridge

University Press.

Bransford, J. D., & Johnson, M. K. (1972). Contextual prerequisites

for understanding: Some investigations of comprehension and

recall. Journal of Verbal Learning and Verbal Behavior, 11, 717–

726.


Brown, J. S., & Burton, R. R. (1978). Diagnostic models for proce-

dural bugs in basic mathematical skills. Cognitive Science, 2,

155–192.

Brown, A. L., & Smiley, S. S. (1977). Rating the importance of

structural units of prose passages: A problem of metacognitive

development. Cognitive Development, 48, 1–8.

Chambliss, M. J., & Calfee, R. C. (1998). Textbooks for learning.

Oxford, England: Blackwell. 

Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R.

(1989). Self-explanations: How students study and use examples

in learning to solve problems. Cognitive Science, 13, 145–182.

Cook, L. K., & Mayer, R. E. (1988). Teaching readers about the

structure of scientific text. Journal of Educational Psychology,

80, 448–456.

De La Mettrie, J. O. (1912). Man a machine. La Salle, IL: Open

Court. (Original work published 1748)

Ebbinghaus, H. (1964). Memory. New York: Dover.

Fuson, K. C. (1992). Research on whole number addition and sub-

traction. In D. A. Grouws (Ed.), Handbook of research on math-



ematics teaching and learning (pp. 243–275). New York:

Macmillan.

Glynn, S. M., Britton, B. K., Muth, D., & Dogan, N. (1982). Writing

and revising persuasive documents: Cognitive demands. Journal



of Educational Psychology, 74, 557–567.

Gould, J. D. (1980). Experiments on composing letters: Some facts,

some myths, and some observations. In L. W. Gregg & E. R.

Steinberg (Eds.), Cognitive processes in writing (pp. 97–128).

Hillsdale, NJ: Erlbaum.

Hayes, J. R. (1996). A new framework for understanding cognition

and affect in writing. In C. M. Levy & S. Ransdell (Eds.), The

science of writing. Mahwah, NJ: Erlbaum. 

Hayes, J. R., & Flower, L. S. (1980). Identifying the organization

of writing processes. In L. W. Gregg & E. R. Steinberg (Eds.),

Cognitive processes in writing. Hillsdale, NJ: Erlbaum.

Johnson-Laird, P. N. (1988). The computer and the mind.

Cambridge, MA: Harvard University Press.

Kellogg, R. T. (1994). The psychology of writing. New York: Oxford

University Press.

Kintsch, W., & Greeno, J. G. (1985). Understanding and solving

word problems. Psychological Review, 92, 109–129.

Kohler, W. (1925). The mentality of apes. New York: Liveright.

Lachman, R., Lachman, J. L., & Butterfield, E. C. (1979). Cognitive

psychology and information processing. Hillsdale, NJ: Erlbaum.

Leary, D. E. (1990). Metaphors in the history of psychology. New

York: Cambridge University Press.


References

57

Levy, C. M., & Ransdall, S. (Eds.). (1996). The science of writing.

Mahwah, NJ: Erlbaum.

Lewis, A. B. (1989). Training students to represent arithmetic word

problems. Journal of Educational Psychology, 79, 363–371.

Lewis, A. B., & Mayer, R. E. (1987). Students’ miscomprehension

of relational statements in arithmetic word problems. Journal of

Educational Psychology, 79, 363–371.

Loman, N. L., & Mayer, R. E. (1983). Signaling techniques that

increase the understandability of expository prose. Journal of

Educational Psychology, 75, 402–412.

Lorch, R. F. (1989). Text signaling devices and their effects on read-

ing and memory processes. Educational Psychology Review, 1,

209–234.


Markman, E. (1979). Realizing that you don’t understand: Elemen-

tary school children’s awareness of inconsistencies. Child Devel-



opment, 50, 643–655.

Mautone, P. D., & Mayer, R. E. (2001). Signaling as a cognitive

guide to multimedia learning. Journal of Educational Psychol-

ogy, 93, 377–389.

Mayer, R. E. (1987). Educational psychology: A cognitive ap-



proach. New York: HarperCollins.

Mayer, R. E. (1992a). Cognition and instruction: On their historic

meeting within educational psychology. Journal of Educational

Psychology, 84, 405–412.

Mayer, R. E. (1992b). Thinking, problem solving, cognition. New

York: Freeman.

Mayer, R. E. (1995). The search for insight: Grappling with Gestalt

psychology’s unanswered questions. In R. J. Sternberg & J. E.

Davidson (Eds.), The nature of insight (pp. 1–32). Cambridge:

MIT Press.

Mayer, R. E. (1996a). Learners as information processors: Legacies

and limitations of educational psychology’s second metaphor.

Educational Psychologist, 31, 151–161.

Mayer, R. E. (1996b). Learning strategies for making sense out

of expository text: The SOI model for guiding three cognitive

processes in knowledge construction. Educational Psychology



Review, 8, 357–371.

Mayer, R. E. (1999). The promise of educational psychology: Learn-



ing in the content areas. Upper Saddle River, NJ: Prentice-Hall.

Mayer, R. E. (2001). Multimedia learning. New York: Cambridge

University Press.

Mayer, R. E. (2002). The promise of educational psychology: Teach-



ing for meaningful learning. Upper Saddle River, NJ: Prentice-

Hall.


Mayer, R. E., & Hegarty, M. (1996). The process of understanding

mathematics problems. In R. J. Sternberg & T. Ben-Zeev (Eds.),



The nature of mathematical thinking (pp. 29–54). Mahwah, NJ:

Erlbaum.


Mayer, R. E., Sims, V. K., & Tajika, H. (1995). A comparison of how

textbooks teach mathematical problem solving in Japan and the

United States. American Educational Research Journal, 32,

443–460.


Metcalfe, J. (1986a). Feeling of knowing in memory and pro-

blem solving. Journal of Experimental Psychology: Learning,



Memory, and Cognition, 12, 288–294.

Metcalfe, J. (1986b). Premonitions of insight predict impending

error. Journal of Experimental Psychology: Learning, Memory,

and Cognition, 12, 623–634.

Metcalfe, J., & Wiebe, D. (1987). Intuition in insight and noninsight

problem solving. Memory & Cognition, 15, 238–246.

Meyer, B. J. F. (1975). The organization of prose and its effects on



memory. New York: Elsevier.

Meyer, B. J. F., & Poon, L. W. (2001). Effects of structure strategy

training and signaling on recall of text. Journal of Educational

Psychology, 93, 141–159.

Newell, A., & Simon, H. A. (1972). Human problem solving.

Englewood Cliffs, NJ: Prentice-Hall.

Paige, J. M., & Simon, H. A. (1966). Cognitive processes in solv-

ing algebra word problems. In B. Kleinmuntz (Ed.), Problem

solving: Research, method, and theory (pp. 51–118). New York:

Wiley.


Paivio, A. (1986). Mental representations. Oxford, England: Oxford

University Press.

Piaget, J. (1971). Science of education and the psychology of the

child. New York: Viking Press.

Pressley, M., & Woloshyn, V. (1995). Cognitive strategy instruc-



tion that really improves children’s academic performance.

Cambridge, MA: Brookline Books.

Reed, S. K. (1987). A structure-mapping model for word problems.

Journal of Experimental Psychology: Learning, Memory, and

Cognition, 13, 124–139.

Soloway, E., Lochhead, J., & Clement, J. (1982). Does computer

programming enhance problem solving ability? Some pos-

itive evidence on algebra word problems. In R. J. Seidel, R. E.

Anderson, & B. Hunter (Eds.), Computer literacy (pp. 171–189).

New York: Academic Press.

Sternberg, R. J. (1977). Intelligence, information processing, and

analogical reasoning. Hillsdale, NJ: Erlbaum.

Sternberg, R. J., & Ketron, J. L. (1982). Selection and implementa-

tion of strategies in reasoning by analogy. Journal of Educa-

tional Psychology, 74, 399–413.

Sweller, J. (1999). Instructional design in technical areas.

Camberwell, Australia: ACER Press.

Taylor, B. (1980). Children’s memory for expository text after read-

ing. Reading Research Quarterly, 15, 399–411. 

Thorndike, E. L. (1965). Animal intelligence. New York: Hafner.

Vosniadou, S., Pearson, P. D., & Rogers, T. (1988). What causes

children’s failures to detect inconsistencies in text? Represen-

tation versus comparison difficulties. Journal of Educational

Psychology, 80, 27–39.

Wittrock, M. C. (1990). Generative processes of comprehension.



Educational Psychologist, 24, 345–376.

CHAPTER 4

Self-Regulation and Learning

DALE H. SCHUNK AND BARRY J. ZIMMERMAN



59

THEORETICAL FORMULATIONS

59

Operant Theory

59

Information Processing Theory

61

Developmental Theory

63

Social Constructivist Theory

65

Social Cognitive Theory

67

RESEARCH FOCUS AREAS

68

Identification of Self-Regulatory Processes

68

Operation of Self-Regulatory Processes

During Learning

69

INTERVENTIONS TO ENHANCE SELF-REGULATION

72

AREAS OF FUTURE RESEARCH



73

Self-Regulation and Volition

74

Development of Self-Regulation in Children

74

Self-Regulation and the Curriculum

74

Self-Regulation Across the Life Span

75

CONCLUSION

75

REFERENCES



75

Current theoretical accounts of learning view students as ac-

tive seekers and processors of information. Learners’ cogni-

tions can influence the instigation, direction, and persistence

of achievement behaviors (Bandura, 1997; Schunk, 1995;

Zimmerman, 1998).

This chapter discusses the role of self-regulation during

learning. Self-regulation (or self-regulated learning) refers to

learning that results from students’self-generated thoughts and

behaviors that are systematically oriented toward the attain-

ment of their learning goals. Self-regulated learning involves

goal-directed activities that students instigate, modify, and sus-

tain (Zimmerman, 1994, 1998)—for example, attending to in-

struction, processing of information, rehearsing and relating

new learning to prior knowledge, believing that one is capable

of learning, and establishing productive social relationships

and work environments (Schunk, 1995). Self-regulated learn-

ing fits well with the notion that rather than being passive re-

cipients of information, students contribute actively to their

learning goals and exercise control over goal attainment. As

we show in this chapter, theory and research attest to the links

between self-regulation and achievement processes.

We begin by explaining five theoretical perspectives

on self-regulation: operant theory, information processing

theory, developmental theory, social constructivist theory,

and social cognitive theory. With this theoretical background

in place, we discuss self-regulation research that identified

self-regulatory processes and examined how self-regulatory

processes operate during learning. We also describe in detail

an intervention designed to enhance students’ self-regulation.

We conclude by suggesting that future research address such

topics as the links between self-regulation and volition, the

development of self-regulation in children, the integration of

self-regulation into educational curricula, and self-regulation

across the life span.

THEORETICAL FORMULATIONS

Operant Theory

The views of operant psychologists about self-regulation de-

rive primarily from the work of Skinner (1953). Operant be-

havior is emitted in the presence of discriminative stimuli.

Whether behavior becomes more or less likely to occur in the

future depends on its consequences. Behaviors that are rein-

forced are more likely to occur, whereas those punished

become less likely. For example, a teacher might praise a stu-

dent after the student studies hard during a class period. The

praise may encourage the student to continue studying hard.

Conversely, if a teacher criticizes a student after the student

misbehaves, the criticism may decrease the likelihood of dis-

ruptive behavior.

Operant theorists have studied how individuals establish

discriminative stimuli and reinforcement contingencies

(Brigham, 1982). Self-regulated behavior involves choosing



60

Self-Regulation and Learning

among alternative courses of action (Mace, Belfiore, & Shea,

1989), typically by deferring an immediate reinforcer in

favor of a different and usually greater future reinforcer

(Rachlin, 1991). For example, assume that Brad is having

difficulty studying; he spends insufficient time studying and

is easily distracted. A key to changing his behavior is to es-

tablish discriminative stimuli (cues) for studying. With the

assistance of his school counselor, Brad establishes a definite

time and place for studying (6:00 to 9:00 p.m. in his room

with two 10-min breaks). To eliminate distracting cues, Brad

agrees not to use the phone, CD player, or TV during this pe-

riod. For reinforcement, Brad will award himself one point

for each night he successfully accomplishes his routine.

When he receives 10 points, he has earned a night off.

From an operant theory perspective, one decides which be-

haviors to regulate, establishes discriminative stimuli for their

occurrence, evaluates performance according to whether it

matches the standard, and administers reinforcement. The

three key subprocesses are self-monitoring, self-instruction,

and self-reinforcement.

Self-Monitoring

Self-monitoring refers to deliberate attention to some aspect

of one’s behavior, and often is accompanied by recording its

frequency or intensity (Mace & Kratochwill, 1988). People

cannot regulate their actions if they are not aware of what

they do. Behaviors can be assessed on such dimensions as

quality, rate, quantity, and originality. While writing a term

paper, students may periodically assess their work to deter-

mine whether it states important ideas, whether they will fin-

ish it by the due date, whether it will be long enough, and

whether it integrates their ideas in unusual fashion. One can

engage in self-monitoring in such diverse areas as motor

skills (how fast one runs the 100-m dash), art (how original

one’s pen-and-ink drawings are), and social behavior (how

much one talks at social functions).

Often students must be taught self-monitoring methods

(Belfiore & Hornyak, 1998; Lan, 1998; Ollendick & Hersen,

1984; Shapiro, 1987). Methods include narrations, frequency

counts, duration measures, time-sampling measures, behav-

ior ratings, and behavioral traces and archival records (Mace

et al., 1989). Narrations are written accounts of behavior and

the context in which it occurs. Narrations can range from

very detailed to open-ended (Bell & Low, 1977). Frequency

counts are used to self-record instances of specific behaviors

during a given period (e.g., number of times a student turns

around in his or her seat during a 30-min seatwork exercise).

Duration measures record the amount of time a behavior oc-

curs during a given period (e.g., number of minutes a student

studies during 30 min). Time-sampling measures divide a pe-

riod into shorter intervals and record how often a behavior

occurs during each interval. A 30-min study period might be

divided into six 5-min periods; for each 5-min period, stu-

dents record whether they studied the entire time. Behavior

ratings require estimates of how often a behavior occurs dur-

ing a given time (e.g., always, sometimes, never). Behavioral



traces and archival records are permanent records that exist

independently of other assessments (e.g., number of work-

sheets completed, number of problems solved correctly).

When self-recording is not used, people’s memory of suc-

cesses and failures becomes more selective and their beliefs

about outcomes do not faithfully reflect actual outcomes.

Self-recording often yields surprising results. Students hav-

ing difficulties studying who keep a written record of their

activities may learn they are wasting most of their study time

on nonacademic tasks.

Two important self-monitoring criteria are regularity and

proximity (Bandura, 1986). Regularity means observing be-

havior continually rather than intermittently, such as by keep-

ing a daily record rather than recording behavior once a

week. Nonregular observation requires accurate memory and

often yields misleading results. Proximity means observing

behavior close in time to its occurrence rather than long

afterwards. It is better to write down what we do at the time it

occurs rather than wait until the end of the day to reconstruct

events.


Self-monitoring places responsibility for behavioral as-

sessment on the person doing the monitoring (Belfiore &

Hornyak, 1998). Self-monitored responses are consequences

of behaviors; like other consequences, they affect future re-

sponding. Self-recordings are immediate responses that serve

to mediate the relationship between preceding behavior and

longer-term consequences (Mace & West, 1986; Nelson &

Hayes, 1981). Students who monitor their completion of as-

signments provide themselves with immediate reinforcers

that mediate the link between the work and distant conse-

quences (e.g., teacher praise, high grades).

Self-Instruction

Self-instruction refers to discriminative stimuli that set the

occasion for self-regulatory responses leading to reinforce-

ment (Mace et al., 1989). One type of self-instruction in-

volves arranging the environment to produce discriminative

stimuli. Students who realize they need to review class notes

the next day might write themselves a reminder before going

to bed. The written reminder serves as a cue to review, which

makes reinforcement (i.e., a good grade on a quiz) more

likely.


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