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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
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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: [email protected]
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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-
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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
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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.
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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-
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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).
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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
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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.
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