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Computer Environments as Metacognitive Tools for Enhancing LearningRoger Azevedo

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AZEVEDOINTRODUCTION

Computer Environments as Metacognitive Tools forEnhancing Learning

Roger Azevedo

Department of Human Development

University of Maryland, College Park

Thearticlesappearing in this special issueof EducationalPsy-

chologist reflect a growing interest by researchers from vari-

ous fields in examining the use of computers as metacognitive

tools for enhancing learning. This topic has become increas-

ingly important as computer-based learning environments be-

comeubiquitousandstudentsuse them extensivelyboth inand

out of school to learn about conceptually rich domains. It is ar-

gued that the effectiveness of these environments will only be

achieved if learners regulate their learning—that is, if they de-

ploy the metacognitive and self-regulatory processes neces-

sary to effectively learn about the relevant topics. Using com-

puter environments to learn about conceptually rich domains

involves a set of complex interactions between cognitive, mo-

tivational, affective, and social processes (Anderson &

Labiere, 1998; Collins, Brown, & Newman, 1989; Derry &

Lajoie, 1993; Jonassen & Land, 2000; Jonassen & Reeves,

1996; Lajoie, 2000; Pea, 1985; Shute & Psotka, 1996; Solo-

mon, Perkins, & Globerson, 1991; Wenger, 1987). Current re-

search on learning with computer environments from the

fields of cognitive science, learning sciences, psychology, ed-

ucation, and artificial intelligence (AI) in education provides

evidence that learners of all ages experience certain difficul-

ties when learning about conceptually rich domains such as

science, math, and social studies. This research indicates that

learning about these domains with computer environments is

particularlydifficultbecause it requiresstudents toanalyze the

learning situation, set meaningful learning goals, determine

which strategies to use, assess whether the strategies are effec-

tive in meeting the learning goals, and evaluate their emerging

understanding of the topic. Learners also need to deploy sev-

eral metacognitive processes to determine whether they un-

derstand what they are learning and to modify their plans,

goals, strategies, and effort as necessary, all in response to

changing contextual conditions (e.g., their cognitive states,

motivational level, and social support). Further, depending on

the learning situation, they may need to reflect on their learn-

ing and modify aspects of the learning context.

Researchers have previously used cognitive theories (e.g.,

Anderson & Labiere, 1998) or constructivist models of learn-

ing and instruction (e.g., Collins et al., 1989; Cognition and

Technology Group at Vanderbilt [CTGV], 1990; Greeno,

1998; Resnick, 1991; Rogoff, 1997) to explain different as-

pects of learning with computer environments. However, due

to the complexity in learning about conceptually rich domains

with computer environments, several researchers have re-

cently extended these theories and models by advancing mod-

els of metacognition (Bandura, 1986; Brown, 1975, 1987;

Flavell, 1979, 1985; Hacker, 1998; Hacker, Dunlosky, &

Graesser, 1998; Schraw & Moshman, 1995) and self-regu-

lated learning (SRL; Butler & Winne, 1995; Corno &

Mandinach, 1985; McCaslin & Hickey, 20001; Paris, Byrnes,

& Paris, 2001; Pintrich, 2000; Schunk, 2001; Winne, 2001;

Zimmerman, 1986, 2000, 2001) to describe the complex inter-

action of mediating cognitive, metacognitive, and social pro-

cesses involved instudents’learningofcomplex topicsanddo-

mains. These new models have been advanced to account for

the various phases (e.g., planning, metacognitive monitoring,

strategy use, and reflection) and areas (e.g., cognitive, af-

fect/motivation, behavior, and context) of SRL. Although

there is a wealth of research in various areas of academic

achievement (for recent reviews see Boekaerts, Pintrich, &

Zeidner, 2000; Zimmerman & Schunk, 2001), these frame-

worksare in their infancyin termsof their explanatoryandpre-

dictive adequacy for using computers as metacognitive tools

for enhancing learning. Therefore, much more research is

needed on the conceptual, theoretical, empirical, and design

issues related tousingcomputersasmetacognitive tools to fos-

ter learning about conceptually rich domains.

COMPUTER ENVIRONMENTS ASMETACOGNITIVE TOOLS

I broadly define a computer environment as a

metacognitive learning tool as one that is designed for in-

EDUCATIONAL PSYCHOLOGIST, 40(4), 193–197

Copyright © 2005, Lawrence Erlbaum Associates, Inc.

Correspondence should be addressed to Roger Azevedo, Department of

Human Development, University of Maryland, 3304 Benjamin Building,

College Park, MD 20742. E-mail: razevedo@umd.edu

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structional purposes and uses technology to support the

learner in achieving the goals of instruction. This may in-

clude any type of technology-based tool, such as an intelli-

gent tutoring system, an interactive learning environment,

hypermedia, multimedia, a simulation, microworld, collab-

orative learning environment, and so on. The characteristics

explicitly stated by Lajoie (1993, p. 261) and several others

(see Derry & Lajoie, 1993; Jonassen & Reeves, 1996;

Lajoie, 1993, 2000; Lajoie & Azevedo, in press; Pea, 1985;

Perkins, 1985) serve as the foundational basis for the meta-

phor of computers as metacognitive tools—(a) assist learn-

ers to accomplish cognitive tasks by supporting cognitive

processes, (b) share the cognitive load by supporting lower

level cognitive skills so that learners may focus on higher

level thinking skills, (c) allow learners to engage in cogni-

tive activities that would be out of their reach otherwise be-

cause there may be no opportunities for participating in

such tasks (e.g., electronic troubleshooting, medical diag-

nosis; see Lajoie & Azevedo, in press), and (d) allow learn-

ers to generate and test hypotheses in the context of prob-

lem solving.

As such, a metacognitive tool is any computer environ-

ment that, in addition to adhering to Lajoie’s (1993)

characteristics of cognitive tool, also has the following addi-

tional characteristics:

1. It requires students to make instructional decisions re-

garding instructional goals (e.g., such as setting learning

goals; sequencing instruction; seeking, collecting, organiz-

ing, and coordinating instructional resources; deciding

which embedded and contextual tools to use and when to use

them to support their learning goals; deciding which repre-

sentations of information to use, attend to, and perhaps mod-

ify to meet instructional goals).

2. It is embedded in a particular learning context that may

require students to make decisions regarding the context in

ways that support successful learning (e.g., how much sup-

port is needed from contextual resources, what types of con-

textual resources may facilitate learning, locating contextual

resources, when to seek contextual resources, determining

the utility and value of contextual resources).

3. It is any computer-based environment that (to some de-

gree) models, prompts, and supports a learners’ self-regula-

tory processes, which may include cognitive (e.g., activating

prior knowledge, planning, creating subgoals, learning strat-

egies), metacognitive (e.g., feeling of knowing, judgment of

learning, evaluate emerging understanding), motivational

(e.g., self-efficacy, task value, interest, effort), or behavioral

(e.g., engaging in help-seeking behavior, modifying learning

conditions; handling task difficulties and demands)

processes.

4. It is any environment that (to some degree) models,

prompts, and supports learners to engage or participate

(alone, with a peer, or with a group) in using task-, domain-,

or activity-specific learning skills (e.g., skills necessary to

engage in online inquiry and collaborative inquiry), which

also are necessary for successful learning.

5. It is any environment that resides in a specific learning

context where peers, tutors, humans or artificial may play

some role in supporting students’ learning by serving as ex-

ternal regulating agents.

6. It is any environment where the learner’s use and de-

ployment of key metacognitive and self-regulatory processes

prior to, during, and following learning are critical for suc-

cessful learning.

Several researchers have recently questioned the educa-

tional potential of such computer environments because of

students’ failure to show learning gains. This criticism is

based on learners’ failure to deploy the key metacognitive

and self-regulatory skills necessary to regulate their learning

(see Azevedo, 2002; Azevedo & Hadwin, in press; Clark,

2004; de Jong et al., 2005; Lajoie & Azevedo, in press;

Mayer, 2003; Shapiro & Neiderhauser, 2004). This new met-

aphor using computers as metacognitive tools should follow

Mayer’s (2003) proposal for the scientific investigation of

how people learn with environments. Our research must in-

clude three basic elements—evidence, theory, and applica-

tions. Mayer’s proposal renews our concern about the lack of

theoretical and empirical evidence necessary to advance re-

search on open-ended electronic environments such as

Web-based learning environments, hypermedia, and hyper-

text in educational psychology and other related fields. Given

the strong interest in these new technologies for teaching and

learning, there is a need to extend our current theoretical

frameworks and establish a solid research base of replicated

findings based on rigorous and appropriate research methods

(Mayer, 2003; Winn, 2003).

The goal of this special issue was to bring together cogni-

tive scientists, psychologists, and educational researchers to

both synthesize and advance our current understanding of the

role of metacognition and self-regulated learning (SRL) re-

lated to using computers as metacognitive tools for enhanc-

ing student learning (Azevedo, 2005; Graesser, McNamara,

& VanLehn, 2005; Lin, Schwartz, & Hatano, 2005; Mathan

& Koedinger, 2005; Quintana, Zhang, & Krajcik, 2005;

White & Frederiksen, 2005). The authors in this issue have

articulated how their programs of research (and their respec-

tive theories and conceptualizations of metacognition and

SRL) can provide evidence about computers acting as

metacognitive tools for enhancing students’ learning. The re-

searchers contributing to this special issue were invited to

provide scholarly reviews and critical analyses of both exist-

ing research and their own research. In their programs of re-

search, they have used different frameworks, research meth-

odologies, and quantitative and qualitative methods to

address issues related to students’ metacognitive and SRL.

The result is a group of articles that we feel has the potential

to define the emergence of a new paradigm—using comput-

ers as metacognitive tools for enhancing student learning.

194 AZEVEDO

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The contributing authors were asked to explicitly address

five issues: (a) Provide an overview of the context in which

computer-based learning environments (CBLEs) are used to

study and foster students’metacognitive and/or SRL; (b) pro-

vide an overview of their theoretical–conceptual framework

and the underlying assumptions, and an explanation of how

the particular theory/model addresses students’

metacognitive processes and SRL (e.g., which specific

phases and areas are being targeted); (c) review and summa-

rize the findings from their own studies using quantitative,

qualitative, and mixed methods as they related to how CBLEs

have been used to study and foster/develop students’

metacognitive and/or SRL; (d) describe how effective their

existing CBLEs are in detecting, tracing, and monitoring

learners’ metacognitive and self-regulatory behaviors during

learning; (e) discuss the implications for the design of

metacognitive tools to support learning, and which of these

components and/or aspects of metacognition and SRL can

and should be modeled and why? (f) Assess whether their ex-

isting framework can be extended into a unifying

metacognitive or SRL framework for studying the various

phases and areas of learning with CBLEs.

OVERVIEW OF ARTICLES IN THIS ISSUE

Azevedo describes the importance of self-regulation in learn-

ing about conceptually challenging science topics using

hypermedia learning environments. Based on a wealth of

contemporary research on academic achievement and SRL,

he advances SRL as a theoretical framework with which to

examine the complex and dynamic processes that mediate

the relationships between learner characteristics, the features

of hypermedia learning environments, and the learning con-

text. The article includes a synthesis of laboratory and class-

room research conducted by his research group, which uses a

mixed-method approach by converging product (i.e., shifts in

learning from pretest to posttest) and process (i.e.,

think-aloud protocols) data to investigate how the deploy-

ment to key self-regulatory processes are related to students’

knowledge gains. A model of SRL consisting of more than

30 planning, cognitive, and metacognitive self-regulatory

processes is described to account for the difficulties students

experience when using hypermedia to learn about challeng-

ing science topics. Last, implications for the design of adap-

tive hypermedia learning environments to support students’

SRL are presented.

White and Frederiksen’s article provides a theoretical

framework and approach to fostering metacognitive devel-

opment. Their article focuses on the nature of

metacognitive knowledge, its relationship to learning

through inquiry, and the CBLEs that can be used to foster

and assess its development in classrooms as students en-

gage in collaborative inquiry. They illustrate their approach

by providing examples from Inquiry Island, a CBLE that

provides learners with advisors, who can provide knowl-

edge, advice, and tools aimed at supporting students’

metacognitive development in the context of doing inquiry

science projects. Their pedagogical approach involves hav-

ing young students take on the role of various cognitive,

metacognitive, and social advisors as a way of enacting and

internalizing the forms of expertise the advisors embody.

They present research findings that illustrate how such em-

bedded tools and learning activities can foster the develop-

ment of metacognitive knowledge and the skills needed for

successful collaborative inquiry.

Quintana, Zhang, and Krajcik’s article proposes a frame-

work for supporting metacognitive aspects of online inquiry

through software-based scaffolding. They base their ap-

proach on the fact that novice learners experience several

cognitive and metacognitive problems during online inquiry

and that these problems could be remedied by software that

can serve a scaffolding function to support students’

metacognition. Their framework focuses specifically on

three metacognitive processes: task understanding and plan-

ning, monitoring and regulation, and reflection. Based on

their existing studies, they discuss different types of scaffold-

ing that can support these three metacognitive processes by

making them explicit to learners.

Graesser, McNamara, and VanLehn’s article focuses on

the well-documented difficulties of students who do not

have adequate proficiencies in inquiry and metacognition,

to enable deeper levels of comprehension. Their article de-

scribes some of their recently designed CBLEs that facili-

tate inquiry and metacognition for students in Grades K–12

and college who are learning science and other domains.

They provide a theoretically based and empirically driven

approach to facilitating explanation-centered learning.

Based on their results, they present several approaches to

scaffolding students’ learning, which include (a) animated

conversational pedagogical agents that scaffold strategies

for inquiry, metacognition, and explanation construction;

(b) computer coaches who facilitate students’ answer gener-

ation to questions that require explanations by using

mixed-initiative dialogue; and (c) modeling and coaching

students in constructing self-explanations and the applica-

tion of metacomprehension strategies while reading text.

Lin, Schwartz, and Hatano’s article contrasts conven-

tional uses of metacognition in academic domains with the

kinds of metacognition required by the teaching profession.

They introduce the concept of adaptive metacognition, which

they argue is critical for teachers to deal with and success-

fully perform in highly variable classroom situations. Ac-

cording to the authors, successful teaching can benefit from

this adaptive metacognition, which involves changes in one-

self and one’s environment in response to a wide a range of

classroom social and instructional variables. Their approach

to metacognitive learning attempts to integrate both specific

cognitive skills and general adaptive and social abilities by

using critical-event-based instruction. They provide evi-

INTRODUCTION 195

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dence for how computers can assist with teachers’

metacognition about teaching by giving them a set of experi-

ences with specific and recurring classroom events where

personal decision making is required.

Mathan and Koedinger’s article deals specifically with two

important aspects of metacognition—how do students moni-

tor their ongoing performance to detect and correct errors and

how do students learn from those instances thorough reflec-

tion? They discuss the effects of providing such feedback re-

garding what they term an “intelligent novice” cognitive

model. Their model of desired performance posits that an in-

telligent novice—someone with general skills facing a novel

problem—is still likely to make errors. Therefore, the intelli-

gent novice model incorporates error detection and error cor-

rection activities as part of the task. Based on their research

with Cognitive Tutors, their approach allows students to make

certain reasonable errors and then provides guidance through

the exercise of error detection and correction skills. They ar-

gue that the opportunity to reason about the causes and conse-

quences or errors may allow students to form a better model of

the behavior of the domain operators, and that feedback sup-

ports both generative and evaluative aspects of a skill.

ACKNOWLEDGMENTS

I thank Philip H. Winne and Lyn Corno, Editors of Educa-

tional Psychologist, for the opportunity to produce this spe-

cial issue. I also acknowledge and thank the individuals who

served as reviewers for the manuscripts included in this spe-

cial issue.

REFERENCES

Anderson, J., & Labiere, C. (1998). The atomic components of thought.

Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Azevedo, R. (2002). Beyond intelligent tutoring systems: Computers as

MetaCognitive tools to enhance learning? Instructional Science, 30,

31–45.

Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhanc-

ing student learning? The role of self-regulated learning. Educational

Psychologist, 40, 199–209.

Azevedo, R., & Hadwin, A. (in press). Scaffolding self-regulated learning

and metacognition: Implications for the design of computer-based scaf-

folds. Instructional Science.

Bandura, A. (1986). Social foundation of thought and action: A social cogni-

tive theory. Englewood Cliffs, NJ: Prentice Hall.

Boekaerts, M., Pintrich, P., & Zeidner, M. (2000). Handbook of self-regula-

tion. San Diego, CA: Academic.

Brown, A. L. (1975). The development of memory: Knowing, knowing

about knowing, and knowing how to know. In H. W. Reese (Eds.), Ad-

vances in child development and behavior (Vol. 10). New York: Aca-

demic.

Brown, A. L. (1987). Metacognition, executive control, self-regulation and

other more mysterious mechanism. In F. E. Weinert & R. H. Kluwe (Eds.),

Metacognition, motivation, and understanding (pp. 65–116): Hillsdale,

NJ: Lawrence Erlbaum Associates, Inc.

Butler, D., & Winne, P. H. (1995). Feedback as self-regulated learning: A

theoretical synthesis. Review of Educational Review, 65, 245–281.

Clark, R. E. (2004). Research on web-based learning: A half-full glass. In R.

Bruning, C. Horn, & L. PytlikZillig (Eds.), Web-based learning. What do

we know? Where do we go? (pp. 1–22). Greenwich, CT: Information Age.

Collins, C., Brown, J., & Newman, S. (1989). Cognitive apprenticeship:

Teaching the crafts of reading, writing, and mathematics. In L. Resnick

(Ed.), Knowing, learning, and instruction: Essays in honor of Robert

Glaser (pp. 453–494). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Corno, L., & Mandinach, E. (1983). The role of cognitive engagement in

classroom learningandmotivation.EducationalPsychologist,18,88–109.

Cognition and Technology Group at Vanderbilt. (1990). Anchored instruc-

tion and its relationship to situated cognition. Educational Researcher,

19(6), 2–10.

de Jong, T., Beishuizen, J., Hulshof, C., Prins, F., van Rijn, H., van Someren,

M., et al. (2005). Determinants of discovery learning in a complx simula-

tion learning environment. In P. Gardenfors & P. Johansson (Eds.), Cogni-

tion, education, communication, and technology (pp. 257–283). Mahwah,

NJ: Lawrence Erlbaum Associates, Inc.

Derry, S. J., & Lajoie, S. P. (1993). Computers as cognitive tools. Hillsdale,

NJ: Lawrence Erlbaum Associates, Inc.

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of

cognitive-developmental inquiry. American Psychologist, 34, 906–911.

Flavell, J. H. (1985). Cognitive Development. Englewood Cliffs, NJ:

Prentice Hall.

Graesser, R. C., McNamara, D., & VanLehn, K. (2005). Scaffolding deep

comprehension strategies through AutoTutor and iSTART. Educational

Psychologist, 40, 225–234.

Greeno, J. (1998). The situativity of knowing, learning, and research. Ameri-

can Psychologist, 53, 5–26.

Hacker, D. J. (1998). Definitions andempirical foundations. InD. J. Hacker, J.

Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory

andpractice (pp.1–23).Mahwah,NJ:LawrenceErlbaum Associates, Inc.

Hacker, D. J., Dunlosky, J., & Graesser, A. C. (Eds.). (1998). Metacognition

in educational theory and practice. Mahwah, NJ: Lawrence Erlbaum As-

sociates, Inc.

Jonassen, D. H., & Land, S. M. (2000). Theoretical foundations of learning

environments. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Jonassen, D., & Reeves, T. (1996). Learning with technology: Using com-

puters as cognitive tools. In D. Jonassen (Ed.), Handbook of research for

educational communications and technology (pp. 694–719). New York:

Macmillan.

Lajoie, S. P. (1993). Computer environments as cognitive tools for enhanc-

ing learning. In S. Derry & S. P. Lajoie (Eds.), Computers as cognitive

tools (pp. 261–288). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Lajoie, S. P. (2000). Computers as cognitive tools II: No more walls: Theory

change, paradigm shifts and their influence on the use of computers for in-

structional purposes. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Lajoie, S. P., & Azevedo, R. (in press). Teaching and learning in technol-

ogy-rich environments. In P. Alexander & P. Winne (Eds.), Handbook of

educational psychology (2nd ed.). Mahwah, NJ: Lawrence Erlbaum As-

sociates, Inc.

Lin, X., Schwartz, D. L., & Hatano, G. (2005). Toward teachers’ adaptive

metacognition. Educational Psychologist, 40, 245–255.

Mathan, S., & Koedinger, K. R. (2005). Benefits of tutoring error detection

and self-correction skills. Educational Psychologist, 40, 257–265.

Mayer, R. (2003). Learning environments: The case for evidence-based practice

and issue-driven research. Educational Psychology Review, 15, 359–373.

Paris, S. G., Byrnes, J. P., & Paris, A. H. (2001). Constructing theories, iden-

tities, and actions of self-regulated learners. In B. Zimmerman & D.

Schunk (Eds.), Self-regulated learning and academic achievement: Theo-

retical perspectives (pp. 253–288). Mahwah, NJ: Lawrence Erlbaum As-

sociates, Inc.

Pea, R. D. (1985). Beyond amplification: Using the computer to reorganize

mental functioning. Educational Psychologist, 20, 167–182.

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning.

In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self regu-

lation (pp. 452–502). New York: Academic.

196 AZEVEDO

Downloaded By: [Pontificia Universidad Católica de Chile] At: 17:10 27 September 2010

Quintana, C., Zhang, M., & Krajcik, J. (2005). Scaffolded software environ-

ments for supporting metacognitive aspects of online inquiry. Educa-

tional Psychologist, 40, 235–244.

Resnick, L. B. (1991). Shared cognition: Thinking as a social practice. In L.

B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially

shared cognition (pp. 1–20). Washington, DC: American Psychological

Association.

Rogoff, B. (1997). Apprenticeship in thinking: Cognitive development in so-

cial context. New York: Oxford University Press.

Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational

Psychology Review, 7, 351–371.

Schunk, D. (2001). Social cognitive theory of self-regulated learning. In &

B. Zimmerman & D. Schunk (Eds.), Self-regulated learning and aca-

demic achievement: Theoretical perspectives (pp. 125–152). Mahwah,

NJ: Lawrence Erlbaum Associates, Inc.

Shapiro, A., & Niederhauser, D. (2004). Learning from hypertext: Research

issues and findings. In D. H. Jonassen (Ed.), Handbook of research for ed-

ucation communications and technology (2nd ed., pp. 605–620).

Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Shute, V., & Psotka, J. (1996). Intelligent tutoring system: Past, present, and

future. In D. Jonassen (Ed.), Handbook of research for educational com-

munications and technology (pp. 570–600). New York: Macmillan.

Solomon, G., Perkins, D. N., & Globerson. T. (1991). Partners in cognition:

Extending human intelligence with intelligent technologies. Educational

Researcher, 20(3), 2–9.

Wenger, E. (1987). Artificial intelligence and tutoring systems. Lost Altos,

CA: Morgan Kaufmann.

White, B., & Frederiksen, J. (2005). Cognitive models and instructional en-

vironments that foster young learners’ metacognitive development. Edu-

cational Psychologist, 40, 211–223.

Winn, W. (2003). Research methods and types of evidence for research in

educational technology. Educational Psychology Review, 15, 367–373.

Winne, P. H. (2001). Self-regulated learning viewed from models of infor-

mation processing. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regu-

lated learning and academic achievement: Theoretical perspectives (2nd

ed., pp. 153–189). Mahwah, New Jersey: Lawrence Erlbaum Associates,

Inc.

Zimmerman, B. (1986). Becoming a self-regulated learner: Which are the key

sub-processes? Contemporary Educational Psychology, 11, 307–313.

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive per-

spective (pp. 13–39). In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.),

Handbook of self regulation. New York: Academic Press.

Zimmerman, B. J. (2001). Theories of self-regulated learning and academic

achievement: An overview and analysis. In B. Zimmerman & D. Schunk

(Eds), Self-regulated learning and academic achievement: Theoretical

perspectives (pp. 1–38). Mahwah, NJ: Lawrence Erlbaum Associates,

Inc.

Zimmerman, B., & Schunk, D. (Eds.). (2001). Self-regulated learning and

academic achievement (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Asso-

ciates, Inc.

INTRODUCTION 197

Downloaded By: [Pontificia Universidad Católica de Chile] At: 17:10 27 September 2010