This article was downloaded by: [Monash University Library]On: 05 October 2014, At: 03:02Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Educational PsychologistPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/hedp20

Using Hypermedia as a Metacognitive Tool forEnhancing Student Learning? The Role of Self-Regulated LearningRoger AzevedoPublished online: 08 Jun 2010.

To cite this article: Roger Azevedo (2005) Using Hypermedia as a Metacognitive Tool for Enhancing Student Learning? The Roleof Self-Regulated Learning, Educational Psychologist, 40:4, 199-209, DOI: 10.1207/s15326985ep4004_2

To link to this article: http://dx.doi.org/10.1207/s15326985ep4004_2

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

AZEVEDOSELF-REGULATED LEARNING

Using Hypermedia as a Metacognitive Tool forEnhancing Student Learning? The Role of

Self-Regulated Learning

Roger Azevedo

Department of Human Development

University of Maryland, College Park

Research shows that learners of all ages have difficulties deploying key cognitive and

metacognitive self-regulatory skills during learning about complex and challenging topics when

using open-ended learning environments such as hypermedia. This article provides an overview

of the research my students and I have conducted on how the use of self-regulated learning can

foster and enhance students’learning about complex science topics using hypermedia. In this ar-

ticle, the term metacognitive tool isuseddeliberatelytohighlight (a) theroleofmetacognitiveand

self-regulatory processes used by learners during learning and (b) the role of computer environ-

ments in prompting, supporting, and modeling students’self-regulatory processes during learn-

ing inspecific learningcontexts (seeAzevedo,2005). Iprovideanoverviewof researchregarding

the use of hypermedia to learn about complex science topics and learning more generally, illus-

trate how self-regulated learning can be used as a guiding theoretical framework to examine

learning with hypermedia, and provide a synthesis of the laboratory and classroom research con-

ducted by our group. Last, I propose several methods for using our findings to facilitate students’

self-regulated learning of complex and challenging science topics.

Computer-based learning environments (CBLEs) are effec-

tive to the extent that they can adapt to the needs of individual

learners, for example, by systematically and dynamically

providing scaffolding of key processes during learning (An-

derson, Corbett, Koedinger, & Pelletier, 1995; Derry &

Lajoie, 1993; Lajoie, 2000; Shute & Psotka, 1996). The abil-

ity of CBLEs to provide adaptive, individualized instruction

rests on an understanding of how learner characteristics, sys-

tem features, and mediating learning processes interact

within particular contexts. A critical means of providing indi-

vidualized instruction is through scaffolding, or instructional

support in the form of guides, strategies, and tools that are

used during learning to support a level of understanding that

would be impossible to attain if students learned on their own

(Collins, Brown, & Newman, 1989; Hogan & Pressley, 1997;

Wood, Bruner, & Ross, 1976). Despite our ability to provide

scaffolding of learning of well-structured tasks within tradi-

tional CBLEs such as intelligent tutoring systems (ITSs, e.g.,

Aleven & Koedinger, 2002), providing adaptive scaffolding

for students’ learning about conceptually challenging do-

mains, such as the circulatory system and ecology, remains a

challenge for hypermedia instruction. We argue that harness-

ing the full power of hypermedia learning environments will

require empirical research aimed at understanding what

kinds of scaffolds are effective in facilitating self-regulated

learning (SRL) and when they are best deployed. Our re-

search therefore focuses on examining the effectiveness of

different kinds of human scaffolding in facilitating under-

graduate, high school, and middle school students’ learning

about the circulatory system and ecology using hypermedia

learning environments. The empirical results of our research

are then used to inform the design of adaptive hypermedia

learning environments.

The purpose of this article is to present an analysis of the

current research on learning with hypermedia; provide an

overview of SRL; propose SRL as a guiding framework

within which to examine the complex and dynamic interplay

between learner characteristics, system features, and learn-

ing context; present our model of SRL with hypermedia; and

present the results of our lab and classroom research based on

our model of SRL. Last, I discuss how the results of our stud-

ies can be applied to inform the design of adaptive

EDUCATIONAL PSYCHOLOGIST, 40(4), 199–209

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

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

hypermedia learning environments that can detect, trace,

model, and foster students’ SRL about complex and chal-

lenging science topics.

CBLEs TO FOSTER STUDENTS’ LEARNINGOF SCIENCE TOPICS

The use of computer-based learning environments (CBLEs)

to foster students’ learning of complex and challenging sci-

ence topics now has a 30-year history (see Derry & Lajoie,

1993; Goldman, Petrosino, & Cognition and Technology

Group at Vanderbilt, 1999; Graesser, McNamara, &

VanLehn, 2005; Jacobson & Kozma, 2000; Jonassen &

Land, 2000; Jonassen & Reeves, 1996; Lajoie, 2000; Lajoie

& Azevedo, in press; Land & Hannafin, 2000; Mandinach

& Cline, 2000; Pea, 1985; Mathan & Koedinger, 2005;

Shute & Psotka, 1996; White & Frederiksen, 2005). How-

ever, the recent widespread use of technological tools such

as Web-based learning environments, hypermedia, and

other open-ended learning environments has raised several

theoretical, empirical, and educational issues that, if left un-

answered, may undermine the potential of these powerful

learning environments to foster students’ learning

(Azevedo, 2002; Clark, 2004; Mayer, 2003; Shapiro &

Neiderhauser, 2004). Theoretically, our understanding of

the underlying learning mechanisms that mediate students’

learning with such environments lags in comparison to the

technological advances that have made these same environ-

ments commonplace in homes, school, and at work. We

therefore need to conduct research to fully understand the

complex nature of the learning mechanisms that may facili-

tate students’ learning with these environments. Em-

pirically, we need more research that uses multiple methods

and levels of converging data to better understand the com-

plex nature of learning with computer-based learning envi-

ronments. Educationally, we need theory and evidence to

drive instructional decisions for the effective use of com-

puters as metacognitive tools. Given the strong interest in

and widespread use of these new technologies for teaching

and learning, there is a need to extend our current theoreti-

cal frameworks, establish a solid research base of replicated

findings based on rigorous and appropriate research meth-

ods, and apply the findings to inform the design of CBLEs

(Mayer, 2003).

The majority of the research in the area of learning with

hypermedia has been criticized as being atheoretical and lack-

ing rigorous empirical evidence (see Dillon & Gabbard, 1998;

Tergan, 1997a, 1997b; Shapiro & Neiderhauser, 2004, for ex-

tensive reviews). To advance the field and our understanding

of the complex nature of learning with such hypermedia envi-

ronments, we need theoretically guided, empirical evidence

regarding how students regulate their learning in these envi-

ronments. In this article, I make the argument that SRL should

be used as a guiding framework within which to examine

learning with hypermedia environments.

SRL is a theoretical framework that can encompass the

complexity of learning with hypermedia, and can account

for learner characteristics (e.g., prior knowledge, age),

hypermedia system features (e.g., access to multiple repre-

sentations of information, nonlinear structure of informa-

tion), and mediating contextual learning processes (e.g.,

metacognitive skills, strategy use), while also considering

how these various aspects interact during students’ learn-

ing about complex systems. Before proposing SRL as a

guiding framework with which to examine complex learn-

ing with hypermedia, I will provide a brief overview of the

existing research on learning about complex science to

pics with hypermedia.

STUDENTS’ LEARNING OFCOMPLEX SCIENCE TOPICS

Understanding complex and challenging science topics is a

critical part of learning and is crucial for solving real-world

problems (National Research Council, 2005). However,

such topics have many characteristics that make them diffi-

cult to understand (Hmelo, Holden, & Kolodner, 2000; Ja-

cobson & Archodidou, 2000). For example, to have a co-

herent understanding of the circulatory system, a learner

must comprehend a multitude of intricate multilevel sys-

tems of relations and feedback loops that exist at the physi-

ological, cellular, organ, and system level (Azevedo &

Cromley, 2004; Chi, 2000; Chi, 2005; Chi, de Leeuw, Chiu,

& LaVancher, 1994; Chi, Siler, & Jeong, 2004; Chi, Siler,

Jeong, Yamauchi, & Hausmann, 2001). Similarly, in eco-

logical systems, the complex relationships between vari-

ables related to water quality indicators (e.g., toxins) and

land use (e.g., agriculture) make it extremely difficult for

students to understand how mathematical, biological,

chemical, physical, and geographical concepts and princi-

ples need to be integrated to understand the complex nature

of the whole system (Azevedo, Winters, & Moos, 2004).

Complex science topics are inherently multilayered, with

nonlinear, recursive relations that can be difficult for novice

learners to quantify and grasp.

Understanding the complexity of these topics is also chal-

lenging for most students because the properties and behavior

of the components that comprise the topic and the interrela-

tionships of the components are not always available for direct

inspection. Inaddition, studentsmust integratemultiple repre-

sentations of information (e.g., text, diagrams, formulas,

structural models, animations, digitized video clips) to attain a

fundamental conceptual understanding and then must use

these representations to reason, solve problems, and make in-

ferences (Goldman, 2003; Kozma, Chin, Russell, & Marx,

2000). Some educators and researchers have turned to CBLEs

such as hypermedia as a means of enhancing students’under-

200 AZEVEDO

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

standings of complexandchallengingscience topics. There is,

however, a continuing debate about the effectiveness of such

technologies for learning (see Azevedo, 2002; Dillon &

Gabbard, 1998; Shapiro & Neiderhauser, 2004).

OVERVIEW OF RESEARCH ONLEARNING WITH HYPERMEDIA

Due to the educational potential of hypermedia, there has

recently been an increase in studies examining their effec-

tiveness in facilitating students’ learning (e.g., Hartley,

2001; Jacobson & Archodidou, 2000; Narayanan &

Hegarty, 1998; Shapiro, 2000). This research has begun to

address several cognitive issues related to learning, includ-

ing the role of basic cognitive structures (e.g., multimodal

short-term-memory stores), cognitive functions (e.g., men-

tal animation), multiple representations (text, diagrams,

video), navigation profiles, system structure (e.g., linear vs.

hierarchical), system features (e.g., advance organizers),

and several types of scaffolds (embedded static scaffolds

and adaptive human scaffolding from peers and teachers).

The research has also explored several different varieties of

tool use (e.g., frequency of use of embedded procedural

scaffolds), examined several different learning outcomes

(e.g., declarative knowledge, conceptual knowledge), and

begun to converge multiple sources of data to examine the

complex nature of students’ learning of challenging and

complex science topics with hypermedia. This research has

employed a variety of theoretical frameworks and models

of instruction (e.g., information processing theory, problem

solving, constructivism, cognitive flexibility theory [Spiro,

Coulson, & Thota, 2003], the construction–integration

model [Kintsch, 1988]), as well as a variety of research

methodologies; e.g., informal observations, case studies,

experimental designs, quasi-experimental designs). In gen-

eral, these studies indicate that students who lack key

metacognitive and self-regulatory skills learn very little

from these open-ended environments and that some kind of

scaffolding is usually necessary to support their learning of

conceptually challenging topics.

Some research has focused on learners characteristics.

For example, researchers have examined the role of prior

knowledge (Shapiro, 1999), use of instructional strategies

(Simons, Klein, & Brush, 2004), metacognitive skills

(Schwartz, Andersen, Hong, Howard, & McGee, 2004),

and individual differences and ability levels (Bera & Liu,

in press). Other research has focused on hypermedia sys-

tem features, such as the role of pedagogical agents

(Baylor & Ryu, 2003), reflection prompts (van den Boom,

Paas, van Merrienboer, & van Gog, 2004), embedded scaf-

folds (Jacobson & Archodidou, 2000; Liu, 2004), naviga-

tion support (Hegarty, Narayanan, & Freitas, 2002), expert

modeling (Pedersen & Liu, 2002), system structure

(Hargis, 2001), comprehension aids (Hartley & Bendixen,

2003), fixed scaffolds (Brush & Saye, 2001), and the na-

ture of the problem posed to learners (Shin, Jonassen, &

McGee, 2003).

Despite the recent wealth of research on hypermedia,

there are several outstanding issues related to learning with

hypermedia environments that have not yet been addressed

by educational and cognitive researchers. For example, there

is the question of how (i.e., with what processes) a learner

regulates his or her learning with a hypermedia environment.

Most of the research has used the product(s) of learning (i.e.,

learning gains based on pretest-posttest comparisons) to infer

the interplay between learner characteristics (e.g., low prior

knowledge), cognitive processes (e.g., strategy use,

metacognition), and structure of the system or the presence

or absence of system features. In our research, we have

adopted SRL because it allows us to directly theorize how

learner characteristics, cognitive processes, and system

structure interact during the cyclical and iterative phases of

planning, monitoring, control, and reflection while learning

with hypermedia environments.

SRL AS AN OVERARCHING THEORETICALFRAMEWORK

Can SRL be used as a guiding theoretical framework

within which to examine learning with hypermedia? By

adopting SRL, we make certain theoretically based as-

sumptions about learning (based on Pintrich, 2000;

Zimmerman, 2000, 2001). These include the belief that

self-regulated learners are active and efficiently manage

their own learning in many different ways (Boekaerts,

Pintrich, & Zeidner, 2000; Corno & Mandinach, 1983;

Paris & Paris, 2001; Winne, 2001; Winne & Perry, 2000;

Zimmerman, 2001; Zimmerman & Schunk, 2001). Stu-

dents are self-regulating to the degree that they are

cognitively, motivationally, and behaviorally active par-

ticipants in their learning process (Zimmerman, 1986).

SRL is a constructive process whereby learners set goals

for their learning and then attempt to plan, monitor, regu-

late, and control their cognition, motivation, behavior,

and context (Pintrich, 2000; Zimmerman, 2001). Models

of SRL describe a recursive cycle of cognitive activities

central to learning and knowledge-construction activities

(e.g., Butler & Winne, 1995; Pintrich, 2000; Schunk,

2001; Winne, 2001; Zimmerman, 2001). The empirical

literature in educational psychology (e.g., Corno &

Randi, 1999; Newman, 2002; Perry, 2002; Pintrich &

Zusho, 2002), and in the learning sciences (e.g.,

Azevedo, Cromley, & Seibert, 2004; Chi, 2005; Chi et al.,

1994; Graesser et al., 2005; Quintana, Zhang, & Krajcik,

2005; Shapiro & Niederhauser, 2004) converge to sug-

gest that students have difficulties learning about com-

plex and challenging topics with CBLEs such as

SELF-REGULATED LEARNING 201

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

hypermedia because they do not deploy key self-regula-

tory processes during learning.

In general, models of SRL share certain basic assump-

tions about learning and regulation, despite the fact that

each model proposes different constructs and mechanisms

(see Boekaerts et al., 2000; Zimmerman & Schunk, 2001).

Pintrich (2000) and Zimmerman (2001) have recently

summarized five assumptions shared by all SRL models.

One assumption, derived from the cognitive perspective of

learning, is that learners are active, constructive partici-

pants in the learning process. Learners construct their own

meanings, goals, and strategies from the information

available from both their own internal environment (i.e.,

cognitive system) and the external environment (i.e., task

conditions, learning context). A second assumption is

based on the idea that learners are capable of monitoring,

controlling, and regulating aspects of their own cognition,

motivation, behavior, and context (e.g., the learning envi-

ronment). Third, biological, developmental, contextual,

and individual constraints can impede or interfere with a

learner’s ability to monitor or control his or her cognition,

motivation, behavior, or context. Fourth, all models tend to

assume that there is a goal, criterion, or standard against

which the learner makes comparisons to assess whether

the process should continue or if some type of change

(e.g., in strategies or metacognitive monitoring) is neces-

sary. In a learning situation, a learner sets goals or stan-

dards to strive for during learning; monitors progress to-

ward these goals; and then adapts and regulates cognition,

motivation, behavior, and context to reach the goals. Fifth,

self-regulatory activities are mediators between personal

and contextual characteristics and actual performance and

learning. In other words, it is not just the learner’s cultural

background, demographic characteristics, or personality

that influence achievement and learning directly; nor are

contextual characteristics of the classroom the only things

that shape achievement, but it is the learner’s self-regula-

tion of his or her cognition, motivation, and behavior that

mediates these relationships.

In addition to these basic assumptions, models of SRL

typically propose four phases of SRL (Pintrich, 2000;

Zimmerman, 2000). The first phase includes planning and

goal setting (Pintrich, 2000; Zimmerman, 2001), activation

of perceptions and knowledge of the task (Winne 2001;

Winne & Hadwin, 1998), and the self in relationship to the

task (Pintrich, 2000; Zimmerman, 2001). The second phase

includes various monitoring processes that represent

metacognitive awareness of different aspects of the self,

task and context (Schunk, 2001). Phase three involves the

student’s efforts to control and regulate different aspects of

the self, task, and context. Lastly, phase four represents var-

ious kinds of reactions and reflections on the self and the

task and/or context (Winne, 2001; Winne & Hadwin, 1998).

In short, self-regulated learners are goal-driven, motivated,

independent, and metacognitively active participants in

their own learning (Zimmerman, 1989, 2001). It should

also be noted that the complex interactions between internal

and external environmental factors and the nature of the

learning task influence whether these phases take place lin-

early and/or recursively during learning (Butler & Winne,

1995).

Based on a comprehensive analysis of the literature,

Pintrich (2000) advanced a taxonomy that is comprised of

four columns representing areas of SRL, and four rows

representing different phases of SRL previously dis-

cussed. A closer inspection of the columns in Pintrich’s

(2000, p. 454) taxonomy reveals specific aspects of SRL

that could inform the study of learning with CBLEs such

as hypermedia. For example, the cognitive column in-

cludes different cognitive strategies learners may use to

learn and perform a task as well as metacognitive strate-

gies individuals may use to control and regulate their cog-

nition. This includes both content knowledge (about a spe-

cific topic) and strategic knowledge (under what

circumstances should that specific knowledge be used?).

The motivation and affect column concerns various moti-

vational beliefs that individuals may have about the task.

For example, one’s self-efficacy beliefs as well as positive

and negative affective reactions to the self or task are in-

cluded in this column. Finally, any strategies that individu-

als may use to control and regulate their motivation and af-

fect are included in this column. The behavior column

includes an individual’s effort, persistence, help-seeking,

and choice behaviors. Context is included as the fourth

column in the framework; it represents the various aspects

of the task environment, general classroom, or cultural

context where the learning is taking place. This column

deals with the external environment, which is defined as

any aspect of the instructional environment which is di-

rectly “outside” of the learner’s cognitive system. How-

ever, in some models (e.g., McCaslin & Hickey, 2001) at-

tempts to control or regulate the context would not be

considered self-regulating because the context is not as-

sumed to be part of the individual. In these models,

self-regulation refers only to aspects of the self that are be-

ing controlled or regulated.

Overall, there is a burgeoning amount of research ad-

dressing whether students regulate their learning with

hypermedia and whether SRL fosters the learning of com-

plex and challenging science topics (e.g., the circulatory

system, ecological systems) from dynamic, nonlinear, ran-

dom-access instructional hypermedia environments.

There is a need for more detail regarding how the phases

(planning, monitoring, strategy use, handling of task diffi-

culty and demands, and interest) and areas (cognition, mo-

tivation, behavior, and context) of SRL mediate the learn-

ing of complex science topics with hypermedia

environments. In the next section, I present our emerging

model of SRL, which is rooted in the existing SRL models

described in the previous section.

202 AZEVEDO

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

SRL WITH HYPERMEDIA

Our emerging model has allowed us to examine the complex

interplay between learner characteristics (e.g., prior knowl-

edge, developmental level), elements of the hypermedia en-

vironment (e.g., nonlinear structure of hypermedia), and me-

diating self-regulatory processes (e.g., planning, strategy

use, monitoring activities) used by students during learning

with hypermedia. Based on our model, we hypothesize that

students learning with hypermedia need to analyze the learn-

ing situation, set meaningful learning goals, determine which

strategies to use, assess whether the strategies are effective in

meeting the learning goal, and evaluate their emerging un-

derstanding of the topic. Students also need to monitor their

understanding and modify their plans, goals, strategies, and

effort in relation to contextual conditions (e.g., cognitive,

motivational, and task conditions). Further, depending on the

learning task, students may need to reflect on the learning ep-

isode and modify their existing understanding of the topic.

Thus, learning with hypermedia requires the student to adap-

tively regulate these activities to meet task demands. If learn-

ers do not regulate their learning when using hypermedia en-

vironments, we may erroneously conclude that the

environments are inherently ineffective, when in fact what is

needed is to foster students’ self-regulation while using these

powerful but complex learning environments. One way to

foster student self-regulation is through the use of various

kinds of contextual aids, which may include access to static

educational resources or a human tutor who provides adap-

tive scaffolding to foster students’ SRL. In studying these

contextual aids, it is critical that researchers not only exam-

ine what students do but also determine how students regu-

late their learning and how external regulating agents, such as

human tutors, can facilitate students’ SRL.

To address students’ documented difficulties in regulating

their own learning, we have further extended our model by

examining the role of a human tutor as an external regulating

agent. In doing so, we consider any scaffold (human or non-

human, static or dynamic) that is designed to guide or support

students’ learning with hypermedia to be a part of the task

conditions (and is therefore external to the learner’s cognitive

system; Winne, 2001). In our studies, we hypothesize that

human tutors can assist students in building their understand-

ing of a topic by providing dynamic scaffolding that helps

students deploy specific self-regulatory skills (e.g., activat-

ing the students’prior knowledge). In so doing, a human tutor

can be seen as an external regulatory agent who monitors,

evaluates, and provides feedback regarding a student’s

self-regulatory skills. This feedback may involve scaffolding

students’ learning by assisting them in planning their learn-

ing episode (e.g., creating subgoals, activating prior knowl-

edge), monitoring several activities during their learning

(e.g., monitoring progress towards goals, facilitating recall of

previously learned material), prompting effective strategies

(e.g., hypothesizing, drawing, constructing their own repre-

sentations of the topic), and facilitating the handling of task

demands and difficulty. Empirically testing the effectiveness

of externally regulated learning can elucidate how these dif-

ferent scaffolding methods facilitate students’ SRL and pro-

vide evidence that can be used to inform the design of

hypermedia learning environments.

RESEARCH ON SRL AND HYPERMEDIACONDUCTED AT THE COGNITION AND

TECHNOLOGY LAB

Researchers have successfully used adaptive scaffolding in

non-hypermedia based learning environments designed to

teach students about well-structured tasks such as math, ge-

ometry, and physics (e.g., Anderson et al., 1995; Aleven &

Koedinger, 2002). However, research on scaffolding with

hypermedia learning environments is scant. The recent wide-

spread use of hypermedia has outpaced our understanding of

how scaffolds can be implemented to adapt to students’ indi-

vidual learning needs. Though there are a few studies on

fixed, embedded scaffolds in hypermedia, very little research

has been conducted on the effectiveness of adaptive scaffold-

ing and how it may facilitate students’ learning within

hypermedia. It is critical that researchers conduct more em-

pirical research in this area to determine how different adap-

tive scaffolding methods invoke self-regulatory processes

that facilitate students’ learning of challenging topics. In our

research, we examine the effectiveness of SRL and externally

regulated learning (ERL) in facilitating qualitative shifts in

students’mental models (from pretest to posttest) and the use

of self-regulatory processes associated with these shifts in

conceptual understanding (e.g., Azevedo & Cromley, 2004;

Azevedo, Cromley, & Seibert, 2004; Azevedo, Cromley,

Winters, Moos, & Greene, in press; Azevedo, Guthrie, &

Seibert, 2004; Azevedo, Moos, Winters, Greene, Olson, &

Chaudhuri-Godbole, 2005; Azevedo, Winters, & Moos,

2004; Cromley, Azevedo, & Olson, 2005; Greene &

Azevedo, 2005; Moos, 2004; Vick, Azevedo, & Hofman,

2005; Winters & Azevedo, in press).

More specifically, the goals of our research are to conduct

laboratory and classroom research, which addresses the fol-

lowing questions: (a) Do different scaffolding conditions in-

fluence students’ ability to shift to more sophisticated mental

models of complex and challenging science topics? (b) Do

different scaffolding conditions lead students to gain signifi-

cantly more declarative knowledge of complex and challeng-

ing science topics? (c) How do different scaffolding condi-

tions influence students’ ability to regulate their learning of

complex and challenging science topics with hypermedia?

(d) What is the role of external regulating agents (i.e., human

tutors, classroom teachers, and peers) in students’ SRL of

complex and challenging topics with hypermedia? (e) Are

there developmental differences in college students’and ado-

SELF-REGULATED LEARNING 203

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

lescents’ ability to self-regulate their learning about complex

and challenging topics with hypermedia?

We address these questions by using mixed methodology

that combines true- and quasi-experimental designs with a

think-aloud method (Ericsson & Simon, 1993) to produce

both outcome measures (i.e., shifts in the quality of students’

mental models from pretest to posttest, and measures of de-

clarative knowledge) and process data (i.e., think-aloud pro-

tocols detailing the dynamics of SRL processes used by stu-

dents during learning, and classroom discourse analyses

detailing the interactions between external regulating agents

including human tutors, peers, and classroom teachers). We

code these think-alouds for self-regulatory processes (cogni-

tive, metacognitive, strategy use, etc.) used by learners and

human tutors during learning. Our primary purpose for using

think-aloud protocols is to map out how SRL processes influ-

ence qualitative shifts in students’ mental models during

learning with a hypermedia. We also triangulate different

data sources, both product and process data to begin to under-

stand the role of SRL in learning about complex and chal-

lenging science topics with hypermedia.

In general, our results show that students’ learning about a

challenging science topic with hypermedia can be facilitated

if they are provided with adaptive human scaffolding that ad-

dresses both the content of the domain and processes of SRL.

This type of sophisticated scaffolding is effective in facilitat-

ing students’ learning as indicated by (a) shifts in their mental

models, (b) gains in declarative knowledge from pretest to

posttest, and (c) process data regarding students’ self-regula-

tory behavior. In contrast, providing students with either no

scaffolding or fixed scaffolds (i.e., a list of domain-specific

subgoals) tends to lead to minor shifts in students’ mental

models and only small gains in declarative knowledge in

older students. Verbal protocols provide evidence that stu-

dents in different scaffolding conditions differentially deploy

key SRL processes, suggesting an association between these

scaffolding conditions, mental model shifts, and declarative

knowledge gains. To date, we have researched 33 different

regulatory processes whose use is related to such process and

product data. These processes include those related to plan-

ning (including subgoals, activating prior knowledge), moni-

toring activities (related to one’s cognitive system and

emerging understanding, the hypermedia system and its con-

tent, and dynamics of the learning task), effective and inef-

fective learning strategies, and methods of handling task dif-

ficulties and demands.

Our results indicate, first, that different scaffolding condi-

tions do have different effects on students’ ability to shift to

more sophisticated mental models for both the circulatory

system and ecology systems (e.g., Azevedo, Guthrie, &

Seibert, 2004). We have demonstrated that students who are

not provided with scaffolds tend to show little or no qualita-

tive mental model shifts from pretest to posttest (e.g.,

Azevedo & Cromley, 2004). In contrast, providing college

students with fixed scaffolds tends to interfere with their abil-

ity to develop sophisticated mental models of the topic (e.g.,

Azevedo & Cromley, 2004). However, fixed scaffolding

tends to facilitate shifts in mental models for adolescents

(middle and high school students; Azevedo et al., in press). In

general, adaptive scaffolding by a human tutor who provides

timely content and process-related scaffolding during learn-

ing tends to lead to significant qualitative mental model shifts

for middle school, high school, and college students.

Second, in terms of declarative knowledge, our results in-

dicate that for college students, each type of scaffolding con-

dition tends to lead to significant gains from pretest to

posttest. In contrast, younger students in adaptive scaffolding

conditions tend to show significant declarative knowledge

gains from pretest to posttest, whereas younger students in

nonadaptive scaffolding conditions do not (e.g., Azevedo et

al., in press).

Third, to trace students’ use of SRL we have developed

and refined a coding scheme that includes the following 33

self-regulatory processes: (a) planning variables including

planning, goal setting, activating prior knowledge, and recy-

cling goal in working memory; (b) monitoring activities in-

cluding feeling of knowing (FOK), judgment of learning

(JOL), monitoring progress towards goals, content evalua-

tion, identifying the adequacy of information, evaluating the

content as the answer to a goal, and self-questioning; (c)

learning strategies including hypothesizing, coordinating in-

formational sources, inferences, mnemonics, drawing, sum-

marizing, goal-directed search, selecting new informational

sources, free search, rereading, taking notes, knowledge

elaboration, finding location in environment, memorizing,

reading notes, and reading new paragraph; (d) handling task

difficulties and demands including help-seeking behavior,

expect adequacy of information, control of context, time and

effort planning, and task difficulty; and (e) interest in the task

or the content domain of the task.

Based on our coding scheme, our results indicate that the

use of SRL varies by condition. Students assigned to our con-

trol conditions (i.e., no scaffolding condition) tend to deploy

fewer self-regulatory processes during learning with

hypermedia. In addition, these students tend to use fewer

learning strategies. Students in the fixed scaffolding condi-

tions tend to regulate their learningbyusingmonitoringactivi-

ties that deal with aspects of the hypermedia learning environ-

ment (other than their own cognition), use several effective

and ineffective strategies, and tend to show interest in the

topic. By contrast, students in the adaptive scaffolding condi-

tions tend to rely on the tutor for externally regulated learning.

This external regulation leads students to regulate their learn-

ingbyactivatingpriorknowledgeandcreatingsubgoals;mon-

itoring their cognitive system by using FOK and JOL and

sometimes engaging in self-questioning; and using several ef-

fective strategies such as summarizing, making inferences,

drawing,andengaging inknowledgeelaboration,and,not sur-

prisingly, engaging in an inordinate amount of help-seeking

from the human tutor (e.g., Azevedo et al., 2005).

204 AZEVEDO

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

In classroom studies, we have found that teachers tend to

spend the majority of their instructional time scaffolding stu-

dents’ use of low-level strategies (e.g., copying information),

whereas other self-regulatory processes such as

metacognitive monitoring and handling task difficulties and

demands are rarely used to facilitate students’ learning (e.g.,

Azevedo, Winters, & Moos, 2004). In sum, the think-aloud

data and discourse analyses (from our lab and classroom re-

search) tend to indicate that successful students regulate their

learning by using significantly more metacognitive monitor-

ing processes and strategies. The data also indicate that cer-

tain key self-regulatory processes related to planning (e.g.,

prior knowledge activation and creating subgoals),

metacognitive monitoring activities (e.g., JOL, FOK,

self-questioning, monitoring progress toward goals), strate-

gies (e.g., summarizing, drawing, inferences, knowledge

elaboration, rereading, coordinating informational sources,

goal-directed search, and hypothesizing), and engaging in

help-seeking behavior have consistently been shown to facil-

itate students’ learning about complex science topics with

hypermedia.

IMPLICATIONS FOR THE DESIGN OFADAPTIVE HYPERMEDIA LEARNING

ENVIRONMENTS

Our results have implications for the design of hypermedia

environments intended to foster students’ learning of com-

plex and challenging science topics. Given the effectiveness

of adaptive scaffolding conditions in fostering students’men-

tal model shifts, it would make sense for a CBLE to emulate

the regulatory behaviors of the human tutors in these condi-

tions. To facilitate students’ understanding of challenging

science topics, the system would ideally need to dynamically

modify its scaffolding methods to foster the students’

self-regulatory behavior during learning. However, these de-

sign decisions should also be based on the successes of cur-

rent adaptive computer-based learning environments for

well-structured tasks (e.g., Aleven & Koedinger, 2002; An-

derson, Douglass, & Qin, 2005; Graesser, Hu, & NcNamara,

2005), technological limitations in assessing learning of

challenging and conceptually rich, ill-structured topics (e.g.,

Brusilovsky, 2001; Jacobson, in press; Lajoie & Azevedo, in

press), and conceptual discussions regarding what, when,

and how to model certain key SRL processes in hypermedia

environments (Azevedo, 2002). The system could be de-

signed to deploy several key SRL mechanisms such as plan-

ning (e.g., plan the learning session, create subgoals), moni-

toring the contents of the hypermedia environment (e.g.,

identifying the adequacy of information, monitoring prog-

ress toward goals), using effective strategies (e.g., coordinat-

ing information sources, drawing), and handling task diffi-

culties and demands (e.g., time and effort planning,

controlling the context, and acknowledging task difficulty)

based on an individual learner’s behavior. However, given

current technological limitations, it is impossible for a sys-

tem to fully emulate the scaffolding used by the human tu-

tors—to monitor students’emerging understanding of a chal-

lenging science topic and provide adaptive scaffolding by

modifying its scaffolding methods based on student requests

for assistance (e.g., through help-seeking behavior). For ex-

ample, embedding scaffolds to accommodate students’ judg-

ment of learning (JOL) poses a technical challenge—how

can the hypermedia environment “know” that, for example, a

student is not aware that he or she is reading too fast? Similar

technical challenges exist if designers wanted to have the

hypermedia environment “detect” that a student is monitor-

ing the content of the hypermedia environment, which is a

nondesirable monitoring activity used predominantly by stu-

dents in the control conditions. These two examples repre-

sent a set of issues that could be resolved with advances in the

technical aspects of designing hypermedia environments

(e.g., see Brusilovsky, 2001, 2004).

There are some nonadaptive scaffolds that could be pro-

vided during learning with hypermedia. First, during learning

of a science topic, students could be prompted periodically to

plan and activate their prior knowledge. This could be used as

an anchor from which to build new understandings and elabo-

rations based on the information presented in the hypermedia

environment. A planning net in the form of a static scaffold

could also be embedded to allow students access to the general

learning goal for the entire learning session with a list of

subgoals designed to facilitate their learning.

Scaffolds could be designed to encourage a student to en-

gage in several metacognitive monitoring activities during

learning. For example, the system could prompt students to

engage in feeling of knowing (FOK) by asking them periodi-

cally (e.g., after reading a section of text) to relate the infor-

mation presented in the hypermedia environment to what

they already know about the circulatory system (i.e., knowl-

edge elaboration). A static scaffold could be embedded in the

hypermedia environment to facilitate a student’s monitoring

of his or her progress toward goals by using the planning net

and subgoals mentioned earlier and having the student ac-

tively verify that he or she has learned about each of the

subgoals given the time remaining in the learning session.

A student’s use of effective strategies could be scaffolded

within a hypermedia environment by providing online

prompts. The system might also be able to detect uses of

some ineffective strategies (or lack of use of some effective

strategies) and provide prompts and feedback designed to

discourage students from using those ineffective strategies.

Based on the results of our previous research, such a

hypermedia environment should foster students’ use of hy-

pothesizing, coordinating informational sources, drawing,

mnemonics, and inferences. Time and effort planning sup-

ports in the system could include a monitoring mechanism

that would display a list of goals, marking goals that have not

been completed, and indicate the time remaining. Based on

SELF-REGULATED LEARNING 205

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

that amount of time remaining, the environment could rec-

ommend goals and strategies on which the student should fo-

cus in the time remaining. Interest could be fostered by al-

lowing a student to pursue his or her own goals but asking

them to periodically verify how their stated goals relate to the

planning net and the subgoals set for the learning task. Our

research adds to a line evidence suggesting a new way of

thinking about using technology to improve education that

focuses on the use of computers as metacognitive tools de-

signed to detect, trace, monitor, and foster learners’ SRL of

conceptually challenging topics (Azevedo, 2002; Lajoie &

Azevedo, in press).

CONCLUDING REMARKS

Our research provides a valuable characterization of the

complexity of self- and externally regulated learning pro-

cesses in both laboratory studies and learner-centered sci-

ence classrooms. We have begun to examine the dynamics of

SRL processes—cognitive, motivational/affective, behav-

ioral, and contextual—during the cyclical and iterative

phases of planning, monitoring, control, and reflection dur-

ing learning from hypermedia environments. One of the main

methodological issues related to SRL that we address in our

research is how students regulate their learning during a

knowledge construction activity. We have developed trace

methodologies of the type that are needed to capture the dy-

namic and adaptive nature of SRL during learning of com-

plex and challenging science topics with hypermedia.

We have also begun to address some of the theoretical,

methodological, and educational issues raised by several re-

searchers (Mayer, 2003; Pintrich, 2000; Winn, 2003; Winne,

Jamieson-Noel, & Muis, 2002; Winne & Perry, 2000;

Zeidner, Boekaerts, & Pintrich, 2001; Zimmerman, 2001;

Zimmerman & Schunk, 2001). We use mixed methodology

by combining experimental designs with a think-aloud

method to produce both outcome measures and process data.

Using think-aloud protocols has allowed us to map out how

SRL processes influence qualitative shifts in students’mental

models during learning with a hypermedia.

Our research has allowed us to examine the effectiveness of

scaffolding methods in facilitating students’ learning of com-

plex and challenging science topics. By doing so, we have

beenable toreconceptualize theexistingresearchonnaturalis-

tic human tutoring (e.g., Chi et al., 2004; Graesser, Bowers,

Hacker, & Person, 1997) by examining the role of scaffolding

on SRL, while concurrently addressing fundamental (see

Wood et al., 1976) and contemporary criticisms of the role of

scaffolds while learning with CBLEs (see Pea, 2004;

Puntambekar & Hubscher, 2005; Quintana et al., 2004). This

is a critical issue that is beyond the scope of this article.

Last, our findings provide the empirical basis for the design

of technology-based learning environments as metacognitive

tools to foster students’ learning of conceptually challenging

science topics. However, these design decisions must also be

based on the limitations and successes of current adaptive

computer-based learning environments for well-structured

tasks, current technological limitations in assessing learning

of challenging and conceptually rich, ill-structured topics in

hypermedia learning environments, and instructional deci-

sions regarding “what, when, and how” to model certain key

SRL processes in hypermedia environments.

ACKNOWLEDGMENTS

The research presented in this article has been supported by

funding from the National Science Foundation (Early Career

Grant REC#0133346), the University of Maryland’s College

of Education and School of Graduate Studies, a subcontract

from Vanderbilt University (Dr. John Bransford from the

U.S. Department of Education), and a subcontract from Si-

mon Fraser University (Dr. Philip H. Winne from the Social

Sciences and Humanities Research Council of Canada).

I also acknowledge and thank the members of my Cogni-

tion and Technology Laboratory who have made significant

contributions to the research reported in this article: Jennifer

Cromley, Fielding Winters, Daniel Moos, and Jeffrey

Greene. I also thank Diane Seibert, Huei-Yu Wang, Myriam

Tron, Lynn Xu, Debby Iny, Danielle Fried, Joe Carioti,

Travis Crooks, Angie Lucier, Ingrid Ulander, Megan Clark,

Daniel Levin, Laura Smith, Lucia Buie, Jonny Merrit, Evan

Olson, Pragati Godbole-Chadhuri, and Sonia Denis for as-

sisting with data collection and analyses; and the university

professors, high school and middle school teachers, and their

students for their participation in the studies reported in this

article. I also thank Susan Ragan, Mary Ellen Verona, and

Stacy Pritchett at the Maryland Virtual High School for their

collaboration. I also thank my colleagues for their invaluable

comments and feedback on the research presented in this ar-

ticle: John Guthrie, Allan Wigfield, James Byrnes, Karen

Harris, and Patricia Alexander.

REFERENCES

Aleven, V., & Koedinger, K. (2002). An effective metacognitive strategy:

Learning by doing and explaining with a computer-based Cognitive Tutor.

Cognitive Science, 26, 147–181.

Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995).

Cognitive tutors: Lessons learned. The Journal of the Learning Sciences,

4, 167–207.

Anderson, J. R., Douglass, S., & Qin, Y. (2005). How should a theory of

learning and cognition inform instruction? In A. Healy (Ed.), Experimen-

tal cognitive psychology and its applications (pp. 47–58). Washington,

DC: American Psychological Association.

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.

206 AZEVEDO

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learn-

ing facilitate students’ learning with hypermedia? Journal of Educational

Psychology, 96, 523–535.

Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffold-

ing facilitate students’ ability to regulate their learning with hypermedia?

Contemporary Educational Psychology, 29, 344–370.

Azevedo, R., Cromley, J. G., Winters, F. I., Moos, D. C., & Greene, J. A. (in

press). Adaptive human scaffolding facilitates adolescents’ self-regulated

learning with hypermedia. Instructional Science.

Azevedo, R., Guthrie, J. T., & Seibert, D. (2004). The role of self-regulated

learning in fostering students’ conceptual understanding of complex sys-

tems with hypermedia. Journal of Educational Computing Research,

30(1), 87–111.

Azevedo, R., Moos, D. C., Winters, F. W., Greene, J. A., Cromley, J. G.,

Olson, E. D., et al. (2005, June). External regulated learning: The role of a

human tutor in facilitating students’learning of science with hypermedia.

Paper to be presented at the 12th International Conference on AI in Edu-

cation, Amsterdam, The Netherlands.

Azevedo, R., Winters, F. I., & Moos, D. C. (2004). Can students collabor-

atively use hypermedia to learn about science? The dynamics of self- and

other-regulatory processes in an ecology classroom. Journal of Educa-

tional Computing Research, 31, 215–245.

Baylor, A., & Ryu, J. (2003). The effects of image and animation in enhanc-

ing pedagogical and agent persona. Journal of Educational Computing

Research, 28(4), 373–394.

Bera, S., & Liu, M. (in press). Cognitive tools, individual differences, and

group processing as mediating factors in a hypermedia environment.

Computers in Human Behavior.

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

tion. San Diego, CA: Academic.

Brush, T., & Saye, J. (2001). The use of embedded scaffolds with

hypermedia-supported student-centered learning. Journal of Educational

Multimedia and Hypermedia, 10, 333–356.

Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and

User-Adapted Interaction, 11, 87–110.

Brusilovsky, P. (2004). Adaptive navigation support in educational

hypermedia: The role of student knowledge level and the case for

meta-adaptation. British Journal of Educational Technology, 34,

487–497.

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

oretical synthesis. Review of Educational Research, 65, 245–281.

Chi, M. T. H. (2005). Commonsense conceptions of emergent processes:

Why some misconceptions are robust. The Journal of the Learning Sci-

ences, 14, 161–199.

Chi, M. T. H., de Leeuw, N., Chiu, M. -H., & LaVancher, C. (1994).

Eliciting self-explanations improves understanding. Cognitive Science,

18, 439–477.

Chi, M. T. H., Siler, S., & Jeong, H. (2004). Can tutors monitor students’ un-

derstanding accurately? Cognition and Instruction, 22, 363–387.

Chi, M. T. H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. (2001).

Learning from human tutoring. Cognitive Science, 25, 471–534.

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

Brunning, C. Horn, & L. PytlikZillig (Eds.), Web-based learning: What

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

Publishing 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 learning and motivation. Educational Psychologist, 18,

88–109.

Corno, L., & Randi, J., (1999). A design theory for classroom instruction in

self-regulated learning? In C. Reigeluth (Ed.), Instructional-design theo-

ries and models (Vol. II): A new paradigm of instructional theory (pp.

293–318). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Cromley, J. G., Azevedo, R., & Olson, E. D. (2005, June). Self-regulation

with multiple representations in learning about science with hypermedia.

Paper to be presented at the 12th International Conference on AI in Edu-

cation, Amsterdam, The Netherlands.

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

NJ: Lawrence Erlbaum Associates, Inc.

Dillon, A., & Gabbard, R. (1998). Hypermedia as educational technology: A

review of the quantitative research literature on learner comprehension,

control, and style. Review of Educational Research, 68, 322–349.

Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as

data (2nd ed.). Cambridge, MA: MIT.

Goldman, S. (2003). Learning in complex domains: When and why do mul-

tiple representations help? Learning & Instruction, 13, 239–244.

Goldman, S., Petrosino, A., & Cognition and Technology Group at

Vanderbilt. (1999). Design principles for instruction in content domain:

Lessons from research on expertise and learning. In F. Durso, R.

Nickerson, R. Schvaneveldt, S. Dumais, D. Linsday, & M. Chi (Eds.),

Handbook of Applied Cognition (pp. 597–627). New York: Wiley.

Graesser, A. C., Bowers, C. A., Hacker, D. J., & Person, N. K. (1997). An

anatomy of naturalistic tutoring. In K. Hogan & M. Pressley (Eds.), Effec-

tive scaffolding of instruction (pp. 145–184). Cambridge, MA: Brookline

Books.

Graesser, A. C., Hu, X., & McNamara, D. (2005). Computerized learning

environments that incorporate research in discourse psychology, cogni-

tive science, and computational linguistics. In A. Healy (Ed.), Experimen-

tal cognitive psychology and its applications (pp. 183–194). Washington,

DC: American Psychological Association.

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

comprehension strategies through AutoTutor and iSTART. Educational

Psychologist, 40, 225–234.

Greene, J. A., & Azevedo, R. (2005, June). Are there key self-regulatory

skills that facilitate shifts in students’ mental models when learning sci-

ence with hypermedia? Paper to be presented at the 12th International

Conference on AI in Education, Amsterdam, The Netherlands.

Hargis, J. (2001). Can students learn science using the internet? Journal of

Research on Computing in Education, 33, 475–487.

Hartley, K. (2001). Learning strategies and hypermedia instruction. Journal

of Educational Multimedia & Hypermedia, 10, 285–305.

Hartley, K., & Bendixen, L. (2003). The use of comprehension aids in a

hypermedia environment: Investigating the impact of metacognitive

awareness and epistemological beliefs. Journal of Educational Multime-

dia and Hypermedia, 12, 275–289.

Hegarty, M., Narayanan, H., & Freitas, P. (2002). Understanding machines

from multimedia and hypermedia presentations. In J. Otero & J. León

(Eds.), Psychology of science text comprehension (pp. 357–384).

Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Hmelo, C. E., Holton, D., & Kolodner, J. L. (2000). Designing to learn about

complex systems. Journal of the Learning Sciences, 9, 247–298.

Hogan, K., & Pressley, M. (1997). Scaffolding student learning: Instruc-

tional approaches and issues. Cambridge, MA: Brookline Books.

Jacobson, M. (in press). From non-adaptive to adaptive educational

hypermedia: Theory, research, and design issues.

Jacobson, M., & Archodidou, A. (2000). The design of hypermedia tools for

learning: Fostering conceptual change and transfer of complex scientific

knowledge. Journal of the Learning Sciences, 9, 149–199.

Jacobson, M., & Kozma, R. B. (2000). Innovations in science and mathe-

matics education: Advanced designs for technologies of learning.

Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Jonassen, D. H., & Land, S. M. (Eds.). (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.

Kintsch, W. (1988). The role of knowledge in discourse comprehension: A

construction—integration model. Psychological Review, 95, 163–182.

SELF-REGULATED LEARNING 207

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

Kozma, R., Chin, E., Russell, J., & Marx, N. (2000). The roles of representa-

tions and tools in the chemistry laboratory and their implications for

chemistry learning. Journal of the Learning Sciences, 9, 105–144.

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

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

instructional 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, & G. Phye (Eds.),

Handbook of educational psychology (2nd ed.). Mahwah, NJ: Lawrence

Erlbaum Associates, Inc.

Land, S., & Hannafin, M. (2000). Student-centered learning environments.

In D. Jonassen & S. Land (Eds.), Theoretical foundations of learning en-

vironments (pp. 1–23). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Liu, M. (2004). Examining the performance and attitudes of sixth graders

during their use of a problem-based hypermedia learning environment.

Computers in Human Behavior, 20, 357–379.

Mandinach, E., & Cline, A. (2000). It won’t happen soon: Practical, curricu-

lar, and methodological problems in implementing technology based

constructivist approaches in classrooms. In S. P. Lajoie (Ed.), Computers

as cognitive tools II: No more walls (pp. 377–395). Mahwah, NJ: Law-

rence Erlbaum Associates, Inc.

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.

McCaslin, M., & Hickey, D. T. (2001). Self-regulated learning and aca-

demic achievement: A Vygotskyan View. In B. Zimmerman, & D.

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

oretical perspectives (pp. 227–252). Mahwah, NJ: Lawrence Erlbaum

Associates, Inc.

Moos, D. C. (2004). The role of goal structure in undergraduates’ use of

self-regulatory variables in two hypermedia learning tasks. Unpublished

master’s thesis, University of Maryland, College Park, Maryland.

Narayanan, N. H., & Hegarty, M. (1998). On designing comprehensible in-

teractive hypermedia manuals. International Journal of Human–Com-

puter Studies, 48, 267–301.

National Research Council. (2005). How students learn: Science in the

classroom. Washington, DC: National Academies Press.

Newman, R. (2002). What do I need to do to succeed … When I don’t

understand what I’m doing!?: Developmental influences on students’

adaptive help seeking. In A. Wigfield & J. Eccles (Eds.), Develop-

ment of achievement motivation (pp. 285–306). San Diego, CA: Aca-

demic.

Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on

self-regulated learning. Educational Psychologist, 36, 89–101.

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

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

Pea, R. D. (2004). The social and technological dimensions of scaffolding

and related theoretical concepts for learning, education, and human activ-

ity. Journal of the Learning Sciences, 13, 423–451.

Pedersen, S., & Liu, M. (2002). The effects of modeling expert cognitive

strategies during problem-based learning. Journal of Educational Com-

puting Research, 26, 353–380.

Perry, N. (2002). Introduction: Using qualitative methods to enrich under-

standings of self-regulated learning. Educational Psychologist, 37, 1–3.

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. 451–502). San Diego, CA: Academic.

Pintrich, P., & Zusho, A. (2002). The development of academic self-regula-

tion: The role of cognitive and motivational factors. In A. Wigfield & J.

Eccles (Eds.), Development of achievement motivation (pp. 249–284).

San Diego, CA: Academic.

Puntambekar, S., & Hubscher, R. (2005). Tools for scaffolding students in a

complex learning environment: What have we gained and what have we

missed? Educational Psychologist, 40, 1–12.

Quintana, C., Reiser, B., Davis, E., Krajcik, J., Fretz, E., Dunca, R., et al.

(2004). A scaffolding design framework for support to support science in-

quiry. Journal of the Learning Sciences, 13, 337–386.

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

ments for supporting metacognitive aspects of online inquiry. Educa-

tional Psychologist, 40, 235–244.

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

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

achievement: Theoretical perspectives (pp. 125–152). Mahwah, NJ: Law-

rence Erlbaum Associates, Inc.

Schwartz, N., Andersen, C., Hong, N., Howard, B., & McGee, S. (2004).

The influence of metacognitive skills on learners’ memory of information

in a hypermedia environment. Journal of Educational Computing Re-

search, 31, 77–93.

Shapiro, A. (1999). The relationship between prior knowledge and interac-

tive overviews during hypermedia-aided learning. Journal of Educational

Computing Research, 20, 143–167.

Shapiro, A. (2000). The effect of interactive overviews on the development

of conceptual structure in novices learning from hypermedia. Journal of

Educational Multimedia & Hypermedia, 9, 57–78.

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.). Mahwah, NJ: Law-

rence Erlbaum Associates, Inc.

Shin, N., Jonassen, D., & McGee, S. (2003). Predictors of well-Structured

and ill-structured problem solving in an astronomy simulation. Journal of

Research in Science Teaching, 40, 6–33.

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.

Simons, K., Klein, J., & Brush, T. (2004). Instructional strategies utilized

during implementation of a hypermedia, problem–based learning envi-

ronment: A case study. Journal of Interactive Learning Research, 15,

213–233.

Spiro, R., Collins, B., & Thota, J. (2003). Cognitive Flexibility Theory:

Hypermedia for complex learning, adaptive knowledge application, and

experience acceleration. Educational Technology, 43, 5–10.

Tergan, S. (1997a). Conceptual and methodological shortcomings in hyper-

text/hypermedia design and research. Journal of Educational Computing

Research, 16, 209–235.

Tergan, S. (1997b). Misleading theoretical assumptions in hyper-

text/hypermedia research. Journal of Educational Multimedia and

Hypermedia, 6, 257–283.

van den Boom, G., Paas, F., van Merrienboer, J., & van Gog, Y. (2004). Re-

flection prompts and tutor feedback in a web-based learning environment:

Effects on students’ self-regulated learning competence. Computers in

Human Behavior, 20, 551–567.

Vick, J. E., Azevedo, R., & Hofman, N. S. (April, 2005). Expert-novice dif-

ferences in the understanding and detection of self-regulated learning

with hypermedia. Paper presented at the annual meeting of the American

Educational Research Association, Montréal, Canada.

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. Zimmerman & D. Schunk (Eds.), Self-regulated

learning and academic achievement: Theoretical perspectives (pp.

153–189). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In

D. J. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educa-

tional theory and practice (pp. 277–304). Hillsdale, NJ: Lawrence

Erlbaum Associates, Inc.

Winne, P. H., Jamieson-Noel, D., & Muis, K. (2002). Methodological issues

and advances in researching tactics, strategies, and self-regulated learn-

208 AZEVEDO

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4

ing. In P. Pintrich & M. Maehr (Eds.), New directions in measures and

methods (pp. 121–156). Amsterdam, The Netherlands: JAI.

Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In

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

tion (pp. 531–566). San Diego, CA: Academic.

Winters, F. I., & Azevedo, R. (in press). How do high-school students regu-

late their learning when using a computer-based environment to collabor-

atively engage on inquiry? Journal of Educational Computing Research

Wood, D., Bruner, J., & Ross, G. (1976). The role of tutoring in problem

solving. Journal of Child Psychology & Psychiatry & Allied Disciplines,

17, 89–102.

Zimmerman, B. (1986). Development of self-regulated learning: Which are

the key subprocesses? Contemporary Educational Psychology, 16,

307–313.

Zimmerman, B. (1989). Models of self-regulated learning and academic

achievement. In B. Zimmerman & D. Schunk (Eds.), Self-regulated learn-

ing and academic achievement: Theory, research, and practice (pp.

1–25). New York: Springer-Verlag.

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

spective. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of

self-regulation (pp. 13–39). San Diego, CA: Academic.

Zimmerman, B. (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.1–37).Mahwah,NJ:LawrenceErlbaum Associates, Inc.

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

demic achievement (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associ-

ates, Inc.

SELF-REGULATED LEARNING 209

Dow

nloa

ded

by [

Mon

ash

Uni

vers

ity L

ibra

ry]

at 0

3:02

05

Oct

ober

201

4