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Research in Science Education (2006) 36: 111–139 © Springer 2006
DOI: 10.1007/s11165-005-3917-8
Promoting Self-Regulation in Science Education:
Metacognition as Part of a Broader Perspective on Learning
Gregory Schraw, Kent J. Crippen and Kendall Hartley
University of Nevada
Abstract
The purpose of this article is to review recent research on self-regulated learning and discuss the
implications of this research for science education. We draw on examples of self-regulated learning
from the science education literature to summarise and illustrate effective instructional methods and
the development of metacognitive understanding (Gunstone; 1999a; Rickey & Stacy, 2000; White &
Mitchell, 1994). We also focus on the crucial role that metacognition plays in self-regulation (Baird
& White, 1996; Nichols, Tippins, & Wieseman, 1997; White, 1998).
We divide our discussion into two main parts. The first focuses on three components of self-
regulated learning, including cognition, metacognition, and motivation. We relate these aspects of
self-regulation to current practices in science education. The second section focuses on six general
instructional strategies for improving self-regulation in the science classroom. We focus on the use
of inquiry based learning, the role of collaborative support, strategy and problem solving instruc-
tion, the construction of mental models, the use of technology to support learning, and the role of
personal beliefs such as self-efficacy and epistemological world views. These instructional strategies
are selected because they reflect extensive research agendas over the last decade within the science
education literature and are essential to metacognition and self-regulation (Butler & Winne, 1995;
Gunstone, 1999b).
Key Words:
Self-Regulated Learning Theory: The Role of Cognition, Metacognition,
and Motivation
Self-regulated learning refers to our ability to understand and control our learning
environments. To do so, we must set goals, select strategies that help us achieve
these goals, implement those strategies, and monitor our progress towards our goals
(Schunk, 1996). Few students are fully self-regulated; however, those with better
self-regulation skills typically learn more with less effort and report higher levels of
academic satisfaction (Pintrich, 2000; Zimmerman, 2000).
Self-regulated learning theory has a distinguished history in cognitive psychology,
with its origins dating back to the social-cognitive learning theory of Albert Bandura.
At the heart of Bandura’s theory is the idea of reciprocal determinism which sug-
gests that learning is the result of personal, environmental, and behavioural factors.
112 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
Personal factors include a learner’s beliefs and attitudes that affect learning and be-
haviour. Environmental factors include the quality of instruction, teacher feedback,
access to information, and help from peers and parents. Behavioural factors include
the effects of prior performance. Reciprocal determinism states that each of these
three factors affects the other two factors.
During the past two decades, researchers have applied Bandura’s (1997) social-
cognitive theory to many settings, including school learning. These attempts led to
the development of self-regulated learning theory which contends that learning is
governed by a variety of interacting cognitive, metacognitive, and motivational com-
ponents (Butler & Winne, 1995; Zimmerman, 2000). Social-cognitive perspectives
of self-regulated learning postulate that individuals learn to become self-regulated
by advancing through four levels of development: observational, imitative, self-con-
trolled, and self-regulated levels (Schunk, 1996; Zimmerman, 2000). Learning at
the observational level focuses on modeling, whereas learning at the imitative level
focuses on social guidance and feedback. Both of these levels emphasise a reliance
on external social factors. In contrast, as students develop they rely increasingly on
internal, self-regulatory skills. At the self-controlled level, students construct inter-
nal standards for acceptable performance and become self-reinforcing via positive
self-talk and feedback. At the self-regulatory level, individuals possess strong self-
efficacy beliefs, as well as a large repertoire of cognitive strategies, that enable them
to self-regulate their learning.
Self-regulated learning consists of three main components: cognition, meta-
cognition, and motivation. Cognition includes skills necessary to encode, memo-
rise, and recall information. Metacognition includes skills that enable learners to
understand and monitor their cognitive processes. Motivation includes beliefs and
attitudes that affect the use and development of cognitive and metacognitive skills.
Each of these three components is necessary, but not sufficient, for self-regulation.
For example, those who possess cognitive skills but are unmotivated to use them do
not achieve at the same level of performance as individuals who possess skills and are
motivated to use them (Zimmerman, 2000). Similarly, those who are motivated, but
do not possess the necessary cognitive and metacognitive skills, often fail to achieve
high levels of self-regulation.
The three main components of self-regulation can be further subdivided into the
subcomponents shown in Figure 1. We describe briefly each of these components
below, as well as several finer-grained subcomponents.
Cognition
The cognitive component includes three general types of learning skills, which
we refer to as cognitive strategies, problem solving strategies, and critical thinking
skills. Cognitive strategies include a wide variety of individual tactics that students
and instructors use to improve learning. One example is the use of student-generated
questions before or during reading to focus the learner’s attention (Chinn & Brown,
SCIENCE SELF-REGULATION 113
Figure 1: Components of self-regulation learning.
2002; Kahle & Boone, 2000). A second example is the use of active learning strate-
gies such as constructing graphs and tables (House, 2002). A third strategy is to
use cloze assessment tasks such as the Koch–Eckstein technique to promote deeper
understanding (Koch, 2001). Previous research indicates that self-regulated learners
of all ages use a variety of cognitive learning strategies in a flexible way (Pressley &
Wharton-McDonald, 1997).
Problem solving strategies are more complex in nature than cognitive strategies.
Problem solving strategy instruction usually focuses on either the development of
a general problem solving strategy or situated practice using that strategy. One ex-
ample is the predict–observe–explain (POE) technique studied by Rickey and Stacy
(2000). Recent studies report that general problem solving can be broken down into
smaller individual steps that are teachable and improve learning (Dhillon, 1998; Pe-
terson & Treagust, 1998). Explicit problem solving instruction helps students to
develop deeper levels of understanding compared to students who do not receive
problem solving training (Huffman, 1997).
Critical thinking involves a variety of skills such as the individual identifying the
source of information, analysing its credibility, reflecting on whether that informa-
tion is consistent with their prior knowledge, and drawing conclusions based on
their critical thinking (Linn, 2000). Research in argumentation (Kuhn, 1999) and
critical thinking (Halpern, 1998) indicates that many students fail to utilise sophisti-
cated reasoning even at the college level. Critical thinking can be improved through
instruction, although it typically requires an extended instructional sequence (e.g.,
three months) to do so (Baird & White, 1996; Chang, 1999; Huffman, 1997).
114 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
Metacognition
Metacognition as we conceptualise it includes two main subcomponents gener-
ally referred to as knowledge of cognition and regulation of cognition (Schraw &
Moshman, 1995). Knowledge of cognition refers to what we know about our cogni-
tion, and may be considered to include three subcomponents. The first, declarative
knowledge, includes knowledge about ourselves as learners and what factors influ-
ence our performance. For example, most adult learners know the limitations of
their memory system and can plan accordingly. Procedural knowledge, in contrast,
refers to knowledge about strategies and other procedures. For instance, most adults
possess a basic repertoire of useful strategies such as note-taking, slowing down
for important information, skimming unimportant information, using mnemonics,
summarising main ideas, and periodic self-testing. Finally, conditional knowledge
includes knowledge of why and when to use a particular strategy. Individuals with
a high degree of conditional knowledge are better able to assess the demands of a
specific learning situation and, in turn, select strategies that are most appropriate for
that situation.
Research suggests that an individual’s knowledge of cognition is late develop-
ing and explicit (Alexander, Carr, & Schwanenflugel, 1995; Baird & White, 1996).
Adults tend to have more knowledge about their own cognition and are better able
to describe that knowledge than children and adolescents. However, many adults
cannot explain their expert knowledge and performance and often fail to sponta-
neously transfer domain-specific knowledge to a new setting. This suggests that
metacognitive knowledge need not be explicit to be useful and, in fact, may be
implicit in some situations (Butler & Winne, 1995).
Regulation of cognition typically includes at least three components, planning,
monitoring, and evaluation (Schraw & Moshman, 1995). Planning involves the selec-
tion of appropriate strategies and the allocation of resources. Planning includes goal
setting, activating relevant background knowledge, and budgeting time. Previous re-
search suggests that experts are more self-regulated compared to novices largely due
to effective planning, particularly global planning that occurs prior to beginning a
task. Monitoring includes the self-testing skills necessary to control learning. Adults
monitor at both the local (i.e., an individual test item) and global levels (i.e., all
items on a test). Further, even skilled adult learners may be poor monitors under
certain conditions (e.g., Pressley & Ghatala, 1990). Evaluation refers to appraising
the products and regulatory processes of one’s learning. Typical examples include
re-evaluating one’s goals, revising predictions, and consolidating intellectual gains.
Some researchers and theorists (Butler & Winne, 1995; Pressley, Borkowski, &
Schneider, 1989) suggest that self-regulatory processes, including planning, monitor-
ing, and evaluation, may not be conscious or explicit in many learning situations. One
reason is that many of these processes are highly automated, at least among adults.
A second reason is that some of these processes may develop without any conscious
reflection and therefore are difficult to report to others. Also, there is not a readily
available language for students and teachers to communicate about such issues. In
SCIENCE SELF-REGULATION 115
addition, some science educators believe that science education should reduce the
amount of instructional time devoted to conceptual understanding and increase the
amount of time devoted to procedural understanding (Duggan & Gott, 2002). The
rationale for this claim is that procedural competence in the form of expert problem
solving and critical thinking becomes increasingly more important at higher levels
of science education.
Motivation
The motivation component shown in Figure 1 includes two important subcom-
ponents, consisting of self-efficacy and epistemological beliefs. Self-efficacy refers
to the degree to which an individual is confident that he or she can perform a spe-
cific task or accomplish a specific goal (Bandura, 1997). Self-efficacy is extremely
important for self-regulated learning because it affects the extent to which learners
engage and persist at challenging tasks. Students with higher self-efficacy are more
likely to engage in a difficult task and more likely to persist at a task even in the
face of initial failures compared to low-efficacy students (Pajares, 1996). Higher
levels of self-efficacy are related positively to school achievement and self-esteem.
The trends observed with respect to student self-efficacy also generalise to teachers
and even schools. Teachers with higher levels of teaching self-efficacy, for example,
set higher goals and standards, give more autonomy to students, and help students
reach higher levels of achievement than do teachers with lower levels of self-efficacy
(Goddard, Hoy, & Hoy, 2000). A number of studies indicate that teacher and student
self-efficacy plays an important role in science education (Cannon & Scharmann,
1996; Schoon & Boone, 1998).
Self-efficacy is affected by a number of variables, but especially vicarious learning
and modeling. Vicarious learning occurs when individuals learn by observing others
perform a skill or discuss a topic. Vicarious learning is advantageous to learners
because they are not expected to perform the task, and therefore experience less
anxiety, and because they also can focus all of their resources on observing experts.
Modeling occurs when learners learn intentionally from other individuals such as
teachers and students. Modeling typically includes the teacher breaking a complex
task into manageable parts and asking students to demonstrate each part separately
in sequence. Bandura (1997) proposed that modeling is effective because it raises
expectations that a new strategy can be acquired, in addition to providing a great deal
of knowledge about the skill. Peer models are usually the most effective because they
are most similar to the learner. Indeed, students are most likely to increase their own
self-efficacy when observing a model of similar ability level performing the skill
(Schunk, 1996).
There are two main ways to increase students’ self-efficacy. One is to use both
expert (e.g., teacher) and non-expert (e.g., student peers) models. Modeling can
improve cognitive strategies and self-efficacy. The second is to provide as much
informational feedback to students as possible. Feedback should indicate not only
116 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
whether the skill was performed acceptably, but provide as much information as pos-
sible about how to improve subsequent performance. Given detailed informational
feedback, performance and self-efficacy can increase even after students experience
initial difficulty performing a skill.
Epistemological beliefs are those beliefs about the origin and nature of knowl-
edge. Researchers have focused on two aspects of epistemological beliefs in the
past decade. One aspect concerns the number of distinct beliefs. Schommer (1994)
created a taxonomy of four beliefs she refers to as, (a) quick learning (i.e., something
is learned immediately or not at all), (b) innate ability (i.e., learning is constrained
by native ability), (c) simple knowledge (i.e., most important ideas are really quite
simple), and (d) certain knowledge (i.e., most important ideas do not change over
time). Schommer-Aikins (2002) argued that each of these beliefs affects problem
solving and critical thinking. Partial support for this claim has been provided by
Kardash and Scholes (1996) who found that epistemological beliefs were related
positively to critical analysis of a scientific text on the transmission of AIDS. Neber
and Schommer-Aikins (2002) also found a relationship between epistemological
beliefs and scientific problem solving.
Other researchers have focused on the distinction between different epistemologi-
cal world views (Hammer & Elby, 2002; Kuhn, 1991). Kuhn and Weinstock (2002)
compared absolutists’ views (i.e., assertions are facts) to multiplists’ views (i.e.,
assertions are opinions), arguing that multiplists adopt situation world views that pro-
mote critical reflection and deeper understanding. In general, there is growing con-
sensus that students and teachers differ with respect to epistemological world views,
and that different world views shape instruction and student learning in different
ways (Roth & Tobin, 2001; Schraw & Olafson, 2002; Tsai, 2002).
Summary
Self-regulated learning refers to learners’ abilities to understand and control their
learning environments. Self-regulated learning involves a combination of cognitive
strategy use, metacognitive control, and motivational beliefs. Cognitive strategies
take the form of simple, problem solving, and critical thinking strategies. Meta-
cognitive processing refers to knowledge and control of cognitive skills, and usually
involves planning, monitoring, and evaluating of learning. Finally, the motivational
component refers to students’ beliefs in their capacity to learn. Motivation takes
many forms including self-efficacy and personal epistemological beliefs. Each of
these components is necessary, but not sufficient, for skilled science learning. We
believe that the role of metacognition is especially important because it enables indi-
viduals to monitor their current knowledge and skill levels, plan and allocate limited
learning resources with optimal efficiency, and evaluate their current learning state.
A number of researchers have argued that cognitive strategies and high motivation
alone are insufficient for skilled self-regulation (Butler & Winne, 1995; White &
Mitchell, 1994). We believe there are a number of ways that self-regulation can be
SCIENCE SELF-REGULATION 117
increased in science classrooms to improve learning. In addition, there are a number
of ways to improve metacognition though classroom instruction (Baird & White,
1996; Beeth, 1998; Gunstone & Mitchell, 1998; Mason, 1994). In what follows we
describe a variety of ways to increase self-regulation in the science classroom.
Teaching for Metacognition and Self-Regulation in Science Education
Much of the research appearing in science education journals over the past decade
has focused on two broad areas; curriculum change in science education and the
use of multiple instructional strategies to improve learning (Hurd, 2002; Kelly &
Anderson, 2000). This section focuses on six general instructional strategies for
improving self-regulation and learning. There is strong consensus among science
educators that multiple approaches to learning are necessary to improve overall sci-
ence achievement (Anderson & Hogan, 2000). These include tested instructional
practices, collaborative support involving communities of learners, and the use of
technology to enrich the learning environment. Effective science instruction must
not only increase learning, but also help students develop the metacognitive life-
long learning skills needed to succeed at higher levels of science, and to reconstruct
their conceptual knowledge and procedural strategies when necessary. In addition,
effective instruction should help students and teachers become aware of the beliefs
they hold about science that affect their learning, or in the case of teachers, affect
their curricular and pedagogical decisions.
Based on a review of selected science education journals over the past decade
we have identified six general areas of instructional strategies for improving science
learning. We summarise research in each of the six strategic areas and discuss how
these instructional interventions relate to metacognition and self-regulation. These
areas are, (a) inquiry based learning, (b) the role of collaborative support, (c) strategy
instruction to improve problem solving and critical thinking, (d) strategies for help-
ing students construct mental models and to experience conceptual change, (e) the
use of technology, and (f) the impact of student and teacher beliefs. Because of the
broad scope of this paper, we do not propose to undertake in-depth reviews of these
six areas. Rather, our main goal is to summarise selected research in these areas from
a science education perspective and provide recent citations for the reader to pursue
in more detail if interested. Each of these six areas have been shown to improve
metacognitive awareness and self-regulation.
Inquiry Based Learning
Inquiry based learning and teaching is considered by many as the hallmark of
science education. Anderson (2002) distinguished between three types of inquiry.
Scientific inquiry is the general process of proposing hypotheses about the world
and testing them in a systematic manner. Inquiry learning is the process of students
118 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
being engaged in learning in which they pose questions and construct solutions; that
is, they construct conceptual understanding as the goal of the learning experience
(Gunstone & Mitchell, 1998). Inquiry teaching refers to creating a learning environ-
ment in which students are able to use a process-oriented approach to pose questions,
construct solutions, and test results. Inquiry teaching promotes self-regulation in
two ways. One is to stimulate students’ active engagement in the learning process
by using cognitive learning strategies and metacognitive strategies to monitor their
understanding. A second is to help increase motivation to succeed in science by using
modeling, but especially modeling active investigation strategies such as predict–
observe–explain (POE) (Windschitl, 2002), or question-asking (Chinn & Brown,
2002).
Not all classroom science learning is inquiry based, and not all inquiry based
instruction is authentic. Chinn and Malhorta (2002) distinguished between simple
inquiry activities and what they refer to as authentic inquiry. Simple and authentic
inquiry tasks differ in relation to a variety of important cognitive and epistemological
dimensions. For example, authentic inquiry is characterised by generation of research
questions, selection of variables, use of experimental controls, being aware of and
resisting potential interpretative bias, analysis and interpretation of findings within
a coherent theoretical framework, and a detailed account of mechanisms that cause
change. Simple inquiry does not meet any of these standards. For example, what
Chinn and Malhorta (2002) refer to as simple observation includes research questions
provided to students rather than being student generated, lack of experimental con-
trol, no protection against bias, construction of simple arguments, and conclusions
that do not investigate or validate a coherent theoretical framework.
Authentic inquiry takes years of practice and is not attainable in most science
classrooms in the short term (Anderson, 2002; Bell & Linn, 2002; Kuhn, 1989).
Nevertheless, it is possible to improve inquiry teaching such that students engage
in more than just simple inquiry. Anderson (2002) has summarised key components
of inquiry teaching for the teacher and student. Teachers are to facilitate student
thinking through scaffolded instruction and explicit reflective thinking. Of special
importance is that teachers demonstrate the use of theoretical models in constructing
and testing scientific arguments (Kuhn, 1989). Students are expected to take an active
role in their learning; to construct hypothesis, and to work collaboratively to test
hypotheses and interpret findings. In addition, students should be expected to explain
verbally or in writing the problem solving strategies used to solve problems. Doing so
promotes self-reflection, an essential component of metacognitive understanding and
self-regulation (Baird & White, 1996; Davis, 2003). These aforementioned activities
depend upon a number of metacognitive processes described throughout this pa-
per, including planning, monitoring, reflection, and self-evaluation of learning. More
explicit instructional interventions are described in the strategies section below.
Chinn and Malhorta (2002) compared differences in epistemologies between au-
thentic and simple inquiry learning environments. Scientists who engage in simple
inquiry record what they see. The goal of authentic inquiry is to build and test
theoretical models and explain unobservable mechanisms. In contrast, the goal of
SCIENCE SELF-REGULATION 119
simple observation is to observe objects in an attempt to describe their behaviour.
Authentic inquiry leads to the coordination of theory and data, in which scientists use
data to evaluate theory, even in cases where separate sets of data conflict with each
other. Thought experiments, in which students consider hypothetical cause–effect
relationships, may also play an important role in this process (Gilbert & Reiner,
2000).
Several questions arise when considering inquiry based teaching. The first con-
cerns the mode of implementation of an inquiry based curriculum. There is a general
level of agreement that inquiry based learning and teaching should be project based.
At least three general inquiry based activities are essential (Chinn & Hmelo-Silver,
2002), including scaffolded experimental design (Khishfe & Fouad, 2002), discus-
sion of results (Halpern, 1998; Kuhn, 1999), and reflection on the process of inquiry
(Toth, Suthers, & Lesgold, 2002; Van See, 2000). Some evidence suggests that ex-
plicit instruction, especially among younger students, facilitates inquiry oriented
experimental design. In addition, there is evidence that students who experience
authentic inquiry are more apt to implement similar teaching strategies in their own
classrooms (Windschitl, 2002). Authentic inquiry promotes active reflection on prob-
lems, as well as construction of explicit conceptual understanding of the problem.
Authentic inquiry promotes metacognition and self-regulation because students are
better able to monitor their learning and evaluate errors in their thinking or gaps in
their conceptual understanding.
A second question concerns the effectiveness of instructional interventions based
on inquiry based learning. A recent review of the literature by Anderson (2002)
indicates positive, but modest gains in learning for inquiry based instructions. In-
fusing inquiry into the curriculum may benefit science attitudes and epistemologi-
cal beliefs, key influences on the motivational aspect of SRL, more than cognitive
processes. Inquiry based learning appears to increase the motivation component of
self-regulation, perhaps because it leads to a clearer conceptual understanding of the
problem. However, some types of cognitive activities such as problem solving may
benefit substantially. There appear to be three possible reasons for improved learning
from inquiry based instruction. Firstly, inquiry frequently provides communication
with some kind of expert who shares strategies and problem solving skills. Sec-
ondly, inquiry may increase motivation because the student takes greater ownership
and shares authority. Third, inquiry promotes self-reflection, a key component of
metacognition (Davis, 2003).
In addition to positive outcomes, several barriers to authentic inquiry instruction
have been identified as well. One is the problem of adequate teacher training to
use inquiry learning in an effective way (Windschitl, 2002). A second barrier is the
lack of agreement among participating teachers or parental reservations to the use of
such strategies. The larger goals of inquiry learning (e.g., the generation of research
questions) are not currently recognised in large-scale measures of science achieve-
ment. Thus, significant quantities of inquiry teaching may have detrimental effects
on these traditional measures. A third potential barrier is whether inquiry learning
reduces coverage that is seen by many as necessary for progression to higher levels
120 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
of the science curriculum. These barriers can pose formidable problems for science
educators.
Collaboration Among Students and Teachers
Collaboration of all forms is increasingly seen as an essential and important part of
education. In the past decade or so, socio-cultural models of learning such as situated
learning theory (Lave & Wegner, 1991), cognitive apprenticeships (Collins, Brown,
& Newman, 1989), and the work of Vygotsky (1978) have played a prominent role
in educational research and practice. In the context of the instructional strategies
presented here, collaboration can be viewed as a tool to support approaches that
encourage an inquiry orientation, the utilisation of strategies, the development and
sharing of mental models, and the making explicit of personal beliefs.
Collaboration in the form of help from teachers and students facilitates learning
and SRL for a variety of reasons. Firstly, teacher and student modeling provide ex-
plicit examples of how to perform a task and often provide explicit feedback (Schunk,
1996; Webb & Palincsar, 1996). Secondly, collaborative support such as tutors, peer-
models, or small groups provide an opportunity for explicit discussion of scientific
concepts and reflection that promotes metacognition and self-regulation. For exam-
ple, explicit discussion promotes planning and evaluation of whether students meet
learning goals (Davis, 2003). Students of similar achievement levels may be more
effective than teacher–student pairs because the former are able to discuss strategies
in the novice’s zone of proximal development (Feldman, Campbell, & Lai, 1999).
Thirdly, communities of learners have greater knowledge resources than individuals.
Finally, social interactions that cut across gender, economic, and ethnic lines pro-
mote social equity in the classroom, which enhance motivation and epistemological
awareness (Bell & Linn, 2002; Hogan, 1999).
Collaboration in the classroom may occur among students, teachers, and between
students and teachers (Hogan, 1999, 2002). Student collaboration usually involves
tutors or small collaborative work groups. Research suggests that peer tutors who are
judged to be of similar ability to their tutees increase the declarative and procedural
knowledge and self-efficacy of those students (Pajares, 1996). Sometimes students
are paired with expert mentors in what are referred to as cognitive apprenticeships.
These relationships can help novice students develop expertise quickly and provide
many opportunities for explicit reflection that builds metacognitive understanding.
Research suggests that tutors and cognitive apprenticeship can help novices achieve
a higher degree of in-depth learning in a particular domain (Ramaswamy, Harris, &
Tschirner, 2001).
One of the most common forms of collaboration are cooperative learning groups.
Hogan (1999) developed the Thinking Aloud Together (TAT) program as a means
to promote metacognition and self-regulation in a small group collaborative setting.
Students in the TAT programs demonstrated greater metacognitive awareness of their
learning than students in the control group. Small group collaboration appears to
SCIENCE SELF-REGULATION 121
be especially effective when students are engaged in inquiry based discussion of
problems (Meyer & Woodruff, 1997) and when students are given explicit train-
ing in how to work effectively in small groups (Bianchini, 1997). One potential
problem is that student-centred cooperative groups can be difficult to initiate and
manage. The Peer Instruction program developed at Harvard University by Eric
Mazur (http://galileo.harvard.edu/galileo/lgm/pi/) is a good example of a
successful student collaboration model for large science lecture sections. Guidelines
for such groups have also been provided by Webb and Palincsar (1996).
Collaborations among teachers are also important. Two ways to promote collab-
oration are through cross-level mentoring and co-teaching. Cross-level mentoring
refers to an experienced teacher mentoring a less-experienced teacher, usually as part
on in-service training (Feldman, Campbell, & Lai, 1999). Training is typically one-
on-one or in a small group and focuses on curricular choices and specific pedagogical
strategies for improving student learning. In contrast, co-teaching involves two teach-
ers of similar experience teaching in collaboration (Roth, 1998). One advantage
to co-teaching is that two teachers are able to make better use of their individual
expertise. A second advantage is that one of the teachers can allocate more time to
small group work with students while the other teacher direct the ongoing lesson.
Co-teaching helps promote the use of cognitive strategies, and better metacognitive
monitoring and evaluation, which support higher levels of student self-regulation
because teachers have more time to devote to individual students or a small group of
students.
Strategy Instruction
Science educators have become increasingly interested in the importance of strat-
egy instruction, which helps students focus their attention more selectively and better
integrate information. In addition to being important complements to the instruc-
tional suggestions made thus far (inquiry approach and collaboration), strategies
serve at least two important functions. One is that they offer the learner a specific
procedural routine for solving problems. A second function is that they often present
a broad conceptual model for how to solve problems. Linn (2000) proposed four
general goals of science education. These include making science accessible, making
thinking visible, helping students learn from each other, and promoting lifelong sci-
ence learning. To accomplish these goals, Linn proposed the Knowledge Integration
Environment (KIE) framework. At the heart of the KIE framework is the notion that
learners use support skills to integrate multiple sources of information into a unified
knowledge framework. Strategy instruction is an integral way to accomplish the four
goals described by Linn.
Earlier, we identified three levels of strategy instruction. Cognitive strategies focus
on the skilled use of a single learning tactic, problem solving strategies that integrate
several strategies into a unified plan for categorising and solving problems, and crit-
ical thinking strategies that involve gathering, analysing, evaluating, and integrating
122 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
information for the purpose of drawing a conclusion. We view these three types
of strategies as embedded in three levels of instruction. Cognitive strategies are
necessary for learning to occur. Problem solving strategies are more sophisticated
and are necessary for developing deeper understanding. Critical thinking strategies
are still more advanced and are essential for the highest levels of skilled reasoning
and decision-making. Critical thinking skills also may be essential to promote meta-
cognitive understanding (Kuhn, 1999). Cognitive strategies enable students to enact
individual strategies effectively. In contrast, problem solving and critical thinking
strategies enable students to regulate their learning through metacognitive skills such
as constructing multiple solutions, testing solutions, and evaluating their answers. In
what follows, we summarise and compare these three types of strategies.
Cognitive strategies. There are a wide variety of cognitive strategies that are taught
in science classrooms. One general strategy is to use analogies to help students link
familiar and unfamiliar concepts (Chinn & Brewer, 1993). A number of studies have
examined the effect of drawing an analogy between the human circulatory system
and the plumbing in a house (Chinn & Malhorta, 2002). In general, using analogies
to map unfamiliar concepts onto familiar schemata in memory has a strong posi-
tive effect on learning (Baker & Lawson, 2001; Beeth, 1998; Harrison & Treagust,
1996; Peterson & Treagust, 1998; White & Mitchell, 1994). A second example of
strategy instruction is the use of prompted reflection through the use of questions
(Blank, 2000; Chinn & Brown, 2002; Osman & Hannafin, 2001). King (1994) re-
ported that student generated questions before or during reading prompted deeper
understanding due to selective attention to important main ideas. Scripted problem
solving, in which students review the individual steps in problem solving, checking
whether each step has been met, also deepens understanding. Davis (2003) found that
elementary school students benefited from general reflection prompts that facilitated
metacognitive monitoring. Prompted students worked more effectively with other
students and constructed more coherent understandings of targeted concepts.
A number of authors have discussed the importance of teaching sets of learning
strategies to improve understanding. The effective use of these strategies, at least
until they become automated, requires metacognition. Comprehensive reviews by
Hattie, Biggs, and Purdie (1996) and Rosenshine, Meister, and Chapman (1996)
indicate that strategy instruction typically is moderately to highly successful. Strat-
egy instruction appears to be most helpful for younger and under-achieving stu-
dents and is most effective when it combines several interrelated strategies. Based
on their analysis, Hattie et al. (1996) suggested the following set of general cog-
nitive strategies: self-checking, creating a good study environment, planning and
goal setting, reviewing, summarising, and seeking teacher and peer assistance. Dole,
Duffy, Roehler, and Pearson (1991) recommended a similar set of five core learning
strategies that includes determining what is important to learn, summarising, draw-
ing inferences, generating questions before and during studying, and monitoring
one’s comprehension. A number of science educators have proposed core sets of
strategies to improve content learning (Brooks & Crippen, 2001; Kahle & Boone,
SCIENCE SELF-REGULATION 123
Figure 2: The problem solving process.
2000) and metacognitive awareness (Beeth, 1997; Koch, 2001). Baird and White
(1996) proposed four components as part of their Project for Enhancing Effective
Learning (PEEL), including increasing time to learn, opportunities to learn, teacher
guidance, and student support. Part of teacher guidance is to demonstrate cognitive
strategies and metacognitive problem solving. Student support offers a collaborative
framework for purposeful inquiry.
Problem solving. Effective problem solving depends on two key components: ex-
pert knowledge that supports metacognition and problem solving skills (Gunstone,
1999b). Developing expertise takes years, although students can develop surprising
amounts of expertise quickly through systematic instruction, scaffolded laboratory
experiences, and peer support (Ericsson, 1996). Using a systematic problem solv-
ing strategy/algorithm is also important. Figure 2 shows one such strategy, which
involves identifying the problem, representing the problem, selecting an appropriate
solution strategy, and evaluating the solution. Figure 2 also shows several important
feedback loops. One loop occurs when experts possess schematic knowledge that
enables them to bypass a detailed search for solution strategies. Two other feed-
back loops occur when solutions fail and individuals must return to a new problem
representation or solution strategy.
Research suggests that students benefit greatly from problem solving instruction.
Teaching a general strategy such as that shown in Figure 2 is less effective than
instruction geared toward solving representative problems in a specific situation. For
example, Chang (1999) found that training in solving earth science problems over
a six week period improved problem solving, but also especially improved transfer.
Dhillon (1998) found that instruction and modeling of the representation phase of
problem solving was especially helpful.
Research suggests that there are at least three general instructional principles for
improving problem solving (Chang, 1999; Huffman, 1997). One is to facilitate the
acquisition of expert knowledge, which, in itself, requires a set of strategies and an
124 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
appropriate view of knowledge. To do so, students must usually acquire as much
expert knowledge as efficiently as possible through some combination of organised
instruction from an expert, as well as reflective practice under the guidance of a
teacher or peer. A second principle is to develop an explicit awareness of a prob-
lem solving strategy such as that in Figure 2 that is appropriate for the types of
problems being solved. One way to do so is for the instructor to model his or her
preferred problem solving methods explicitly. A third is to use external representa-
tions whenever possible to reduce unnecessary cognitive load. These representations
can take the form of summary tables, flowcharts, causal diagrams, timelines, etc.
Of central importance is that the learner reduces the amount on information held in
memory by translating it onto paper or into a physical model (Gunstone & Mitchell,
1998; Perkins, 1993). These problem solving strategies have been found to improve
metacognition and self-regulation because that enable the student to reallocate lim-
ited resources and solve problems more efficiently (Butler & Winne, 1995; Kahle &
Boone, 2000).
Critical thinking. Developing a repertoire of critical thinking skills for the science
classroom is a challenging task. Halpern (1998) and Kuhn (1999) have written ex-
tensively about improving critical thinking skills. A core set of skills, which consist
of identifying relevant information, constructing arguments, testing the credibility of
information and hypotheses, and forming plausible conclusions is generally agreed
on (Bruning et al., 2003; Kuhn, 1991; Vosniadou, 1994). It is especially important
to help students develop better metacognitive monitoring through explicit reflection
and monitoring training in relation to the use of such critical thinking skills. For
example, Delclos and Harrington (1991) examined fifth and sixth-grader’s ability to
solve computer problems after assignment to one of three conditions. The first group
received specific problem solving training, the second received problem solving plus
self-monitoring training and practice, while the third received no training. The prob-
lem solving and monitoring group solved more of the difficult problems than either
of the remaining groups and also took less time to do so.
More recently, Blank (2000) proposed a model of critical thinking in science called
the metacognitive learning cycle (MLC). The MLC emphasises the systematic use of
discussions and reflection to promote explicit metacognitive understanding of critical
thinking and problem solving. The MLC consists of four interrelated steps, which
include concept introduction, concept application, concept assessment, and concept
exploration. Students are asked to reflect upon their progress at each step either
individually or in small groups. In comparison with groups that did not use explicit
reflection, the MLC experienced greater conceptual restructuring and understanding
of course content.
As previously suggested, many science educators believe that reflection is the most
important cognitive mechanism for promoting critical thinking and metacognition
(Davis, 2003; Gunstone, 1999a; Zembal-Saul, Blumenfeld, & Krajcik, 2000).
Nichols, Tippins, and Wieseman (1997) provided a comprehensive review of reflec-
tion and the role that critical reflection plays in critical thinking and self-regulation.
SCIENCE SELF-REGULATION 125
Critical reflection emphasises the use of alternative perspectives and uses of knowl-
edge and thinking. As noted in the prior section, collaboration plays a key role in
providing these alternative perspectives. In addition, Nichols et al. (1997) identified
a number of learning tools that promote critical reflection and metacognition in the
classroom, including portfolios, journals, and examination of cases.
The Development of Mental Models and Conceptual Change
One area of profound importance to science education and a key component of au-
thentic inquiry, is understanding how to help students and teachers construct mental
models of scientific phenomena and the role that metacognition plays in this process
(Beeth, 1998; Gunstone & Mitchell, 1998). Hogan and Thomas (2001) argue that
mental models are necessary to think metacognitively about complex systems. Stu-
dents have difficulty reflecting on complex phenomena without mental models, and
as a consequence, find it difficult to monitor and self-regulate their own learning.
Hogan and Thomas (2001) summarised a number of differences between experts
and novices when constructing mental models of a scientific phenomenon. Four
components of the model-construction process were of special importance, including
model construction, model quantification, model interpretation, and model revision.
In general, experts who construct mental models focus on the dynamic interrela-
tionships within the model, whereas novices focus to a greater extent on isolated
component variables. These differences may be due to experience and access to
skilled mentors, but also to differences in what Hogan and Thomas refer to as a mod-
eling epistemology (i.e., the beliefs people have about the utility and credibility of
models). Unfortunately, research suggests that many science teachers do not possess
strong skills related to the use of mental models (Beeth, 1997; Kuhn, 1989; Van Driel
& Verloop, 1999). However, technology may play an important role as the medium
for abstracting external representations of mental models that would supply teachers
with data upon which to provide instructional feedback.
Understanding and constructing mental models is an essential component of sci-
ence achievement. However, it is possible that a student may construct an inappro-
priate mental model that misrepresents important relationships and leads to inaccu-
rate conclusions. In such cases, teachers are faced with the dilemma of conceptual
change; that is, how to change a student’s or fellow teacher’s internal representation
of a domain or phenomenon. Much has been written on conceptual change since
the pioneering work of Posner, Strike, Hewson, and Gertzog (1982). Conceptual
change is not possible without some degree of intellectual conflict, although that
is sharp disagreement as to how much conflict is optimal. Most recent models of
conceptual change span a continuum from weak to radical conceptual change (Chinn
& Brewer, 1993; Chinn & Malhorta, 2002). The dilemma that science teachers face is
whether to help students restructure their knowledge through either weak or radical
conceptual change processes (Pintrich, Marx, & Boyle, 1993). Experts agree that
some degree of cognitive disequilibrium is necessary, although it is unclear how
126 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
much and under what circumstances (Chinn & Brewer, 1993; Gunstone & Mitchell,
1998; Pintrich et al., 1993). Greater levels of cognitive disequilibrium appear to fa-
cilitate conceptual change, although there are important differences across younger
and older students. One of the most common approaches is to ask students to model
anomalous data (Niaz, 2001; Nieswandt, 2001; Novak, 2002; Shepardson, 1999).
Constructing mental models based on data in the context of hands-on or laboratory
instruction is especially helpful because it promotes strategy use, reflection, and
evaluation (Weaver, 1998).
Genuine conceptual change involves at least three distinct stages, including reveal-
ing student preconceptions, creating conceptual conflict, and encouraging cognitive
accommodation. Research suggests that teacher directed discussions and collabora-
tive work among students are useful ways to reveal student preconceptions. Another
strategy is the use of metaphors to reveal naïve preconceptions (Tobin & Tippins,
1996; Thomas & McRobbie, 1999, 2001; Vosniadou, 1994). Yet a third strategy is
to induce the need for conceptual change through failed experiments (Tabachnick
& Zeichner, 1999). These strategies are important because they promote explicit
metacognitive awareness that enables students to self-regulate their learning in coop-
erative groups (Hogan, 1999) and individually (Beeth, 1998; Gunstone & Mitchell,
1998).
The Use of Technology
Technology has the potential to support self-regulated learning in science edu-
cation in a number of ways. Typically, this involves supporting the other instruc-
tional strategies. For example, using hardware and software (both digital computer
and other) in the process of inquiry, as a construction tool for creating representa-
tions of mental models, as a collaborative communication medium, to model expert
techniques and to provide feedback during problem solving. In this capacity, tech-
nology supports self-regulation by functioning as: a knowledge representation tool,
a cognitive scaffold, a feedback engine, and a collaborative communication device.
The development and use of cognitive strategies like creating multiple external
representations can be facilitated successfully with technology (Jonassen, Carr,
& Yueh, 1998). Students can use semantic networking software (e.g., Inspiration™)
to represent their understanding of complex phenomenon before, during, and after
instruction. These two dimensional representations illustrate the important relation-
ships between concepts (structural knowledge) and can serve to help students explic-
itly model their thinking in a medium that can be updated as student’s understanding
evolves. In addition, technology can be used to promote metacognitive activities such
planning and monitoring (Puntambekar, 1995). Puntambekar and duBoulay (1997)
developed a computer assisted instructional system called Metacognition in Studying
from Texts (MIST) to train students to reflect upon what they were reading and to
monitor their understanding.
SCIENCE SELF-REGULATION 127
Technology can be implemented as a cognitive scaffold for novice science problem
solvers. As such, ‘just-in-time’ examples of expert problem solving strategies and
individualised performance-related feedback can be generated and delivered. Engag-
ing students in studying expert strategies in the form of worked examples improves
novice student problem solving and metacognition (Atkinson et al., 2000). These
examples can be generated dynamically as students are working through materials, or
simply be delivered on demand. This can be viewed as a proxy for teacher modeling
as an instructional strategy.
Electronic assessment systems that provide students with instantaneous knowl-
edge of results and performance related feedback are effective for improving perfor-
mance in science courses (Dufresne et al., 2002; Penn, Nedeff, & Gozdzik, 2000).
These systems can serve a metacognitive function for students who use them as the
feedback provided can be individualised based upon student response patterns and
can include suggestions which make students cognition explicit (Butler & Winne,
1995). Instructional design in such systems is a serious consideration in terms of the
limited capacity of working memory and its implications for student performance
(Brooks & Crippen, 2001).
Solving analytical problems with data analysis, visualisation and organisation tools
such as spreadsheets and dynagrams (dynamic diagrams), allows students to focus on
larger problem solving issues while offloading certain necessary information (rela-
tionships, procedures and data) to the machine. In this way, students are better able to
focus their cognitive resources on monitoring and evaluating the quality of solution
produced (Pea, 1993a). An example of such a tool is the 2-D Optic Dynagram sim-
ulator that was developed to serve as a symbolic representation of optical scenarios
that were used to test questions related to light and lenses (Pea, 1993b).
Having science students use technology to create both static and dynamic models
is thought to broaden and strengthen their mental models and support critical think-
ing (Stratford, 1997). Integrating the functional and causal relationships of a complex
dynamic system like global climate, electron transport, or plate tectonics requires the
production of a quality mental model (Greca & Moreira, 2000). Dynamic computer
modeling software allows students to create functional analytical models. These
models can be built as either representations of mental models or representations
of observable physical phenomena. Variables within these models can be manipu-
lated and temporal patterns can be analysed against hypothesised predictions. Just as
semantic representations of student thinking can be updated to better represent their
current understanding, dynamic models of physical phenomena can continuously be
enhanced as the phenomenon evolves, or is better understood. Constructing models
using computers should enable students to metacognitively evaluate their learning.
Finally, electronic communication and collaboration tools like e-mail, chat, and
threaded discussion support self-regulation in a number of ways. One notable collab-
oration tool is the Computer Supported Intentional Learning Environment (CSILE)
developed to support student inquiry (Scardamalia & Bereiter, 1996). CSILE (now
Knowledge Forum) provides students and teachers with a shared space that is used to
organise course concepts and student ideas. Student contributions are scaffolded in
128 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
the sense that they are prompted to post notes that use language to support knowledge
building.
Student and Teacher Beliefs
Beliefs play a crucial role in science learning for both students and teachers
(McRobbie & Thomas, 2000; Roth & Tobin, 2001). Self-efficacy and epistemologi-
cal beliefs appear to be of particular importance. Self-efficacy refers to the degree that
individuals feel capable of accomplishing a particular task or goal (Bandura, 1997).
Higher levels of self-efficacy correspond to greater engagement in a difficult task
and higher levels of persistence when faced with setbacks. Self-efficacy is important
because students generally lose confidence and interest in science with age (Pell &
Jarvis, 2001). Effective instruction, peer modeling, and cooperative learning commu-
nities all appear to improve student self-efficacy (Pajares, 1996). Detailed informa-
tional feedback increases student self-efficacy as well, but especially self-regulatory
skills and metacognition (Butler & Winne, 1995; White, 1998).
A number of factors appear to affect pre-service teachers’ self-efficacy beliefs.
One is that simple beliefs about science phenomena, such as the structure of the
solar system, are related to lower teacher self-efficacy (Schoon & Boone, 1998).
Low self-efficacy teachers also had fewer alternative conceptions of relevant sub-
ject matter than high self-efficacy teachers. Access to high quality instruction in-
creases pre-service teachers’ self-efficacy (Ramey-Gassert, Shroyer, & Staver, 1998).
In addition, cooperative field experiences increase self-efficacy, especially when low
self-efficacy teachers participate with high self-efficacy teachers (Cannon & Schar-
mann, 1996). For practicing teachers, support from fellow teachers and adminis-
trators is an important determinant of self-efficacy. Continuing education also pre-
dicts self-efficacy, although it is still unknown whether continuing education causes
self-efficacy or the reverse.
Epistemological beliefs refer to individuals’ beliefs about the origin and nature of
knowledge (Reiner & Gilbert, 2000). Researchers have distinguished between realist
versus relativist epistemological world views (Elby & Hammer, 2001; Hammer &
Elby, 2002). Realism corresponds to the belief that knowledge is relatively simple,
fixed, and teachable in the same way to a wide array of students. Relativism corre-
sponds to the belief that knowledge is messy, changing, and must be personalised
through experience.
Students holding realist world views characterised by unsophisticated epistemo-
logical beliefs typically achieve less than students with relativist world views, even
when other variables are held constant (Hofer & Pintrich, 1997; King & Kitchener,
1994; Schommer-Aikins, 2002). Jehng, Johnson, and Anderson (1993) found that
epistemological beliefs differ across academic disciplines among college undergrad-
uate and graduate students. Students in disciplines, such as the humanities, were
more likely to believe that knowledge is uncertain than students in disciplines, such
as physics. Compared with undergraduates, graduate students were more likely to
SCIENCE SELF-REGULATION 129
believe that knowledge is uncertain and develops incrementally (they did not believe
in quick learning). Bendixen, Schraw, and Dunkle (1998) found that epistemological
beliefs were related to moral reasoning among adults. Individuals adopting beliefs in
complex, incremental knowledge reasoned at a higher level on the Defining Issues
Test. Kardash and Scholes (1996) reported that beliefs in certain knowledge were
associated with lower scores on the Need for Cognition Scale and in written measures
of cognitive reasoning. Collectively, these findings suggest that students with more
sophisticated epistemological beliefs are more likely to reflect on problems and reach
more sophisticated conclusions.
Kuhn and colleagues (Kuhn, 1991; Kuhn, Cheney, & Weinstock, 2000) found that
epistemological beliefs are related to one’s ability to argue persuasively and to use
metacognitive skills and knowledge to self-regulate one’s learning. In these studies,
individuals were classified as an absolutist (one who believes that knowledge is ab-
solutely right or wrong), a multiplist (one who believes that knowledge is completely
relative), or an evaluative theorist (one who believes that knowledge, though relative,
is constrained by situational factors such as commonly accepted rules) on the basis
of their beliefs about the certainty of knowledge. Evaluative theorists were more
likely than absolutists to provide legitimate evidence in support of an argument. In
addition, compared with absolutists, evaluative theorists generated a greater number
of plausible alternative theories and provided better counterarguments.
Teachers’ epistemological beliefs affect their curricular and pedagogical decisions
(Reybold, 2001; Roth & Tobin, 2001; Schraw & Olafson, 2002; White, 2000). For
example, teachers who endorse realist beliefs are more likely to rely on textbooks and
standardised curriculum, use conventional text, minimise field experiences, and limit
the role of hypothesis testing and thought experiments (Bell & Linn, 2002; Elby &
Hammer, 2001; Hogan, 2000; Neber & Schommer-Aikins, 2002; Reiner & Gilbert,
2000; Tsai, 2001).
Teachers with unsophisticated epistemological beliefs conduct less challenging
classrooms. One problem with the classrooms of such teachers is that they convey
and promote images of science as static and/or beyond the reach of all but the most
capable students (Bell & Linn, 2002). Realist teachers also spend more time engaged
in direct instruction and set work and less time in critical inquiry, which many have
argued is the cornerstone of scientific investigation. Moreover, realist teachers may
be less able or unwilling to model skilled argumentation in their classrooms (Kuhn,
1999). In contrast, teachers characterised by sophisticated epistemological beliefs
promote inquiry and argumentation, using specific strategies such as debate, gener-
ating and critiquing arguments, group based projects that facilitate synthesis of ideas
(Bell & Linn, 2002; Roth, McRobbie, Lucas, & Boutonne, 1997). Research indicates
that expert science teachers may promote sophisticated reasoning and argumentation
skills through co-teaching (Roth, Tobin, & Zimmermann, 2002).
Overall, self-efficacy and epistemological beliefs help to motivate students and
teachers motivation (Elby & Hammer, 2001; Neber & Schommer-Aikins, 2002). Stu-
dents who are more self-efficacious are better self-regulators, in part, due to better use
of metacognitive skills and knowledge (Baird & White, 1996; Blank, 2000). Mod-
eling helps students increase their self-efficacy (Pajares, 1996) and metacognitive
130 GREGORY SCHRAW, KENT J. CRIPPEN AND KENDALL HARTLEY
skills (Kuhn, 1999). Strategy instruction also improves self-efficacy (Pressley et al.,
1989), self-regulated learning (Butler & Winne, 1995), and metacognitive awareness
(Alexander et al., 1995; Schraw & Moshman, 1995).
Summary and Conclusions
Self-regulated learning theory evolved from Bandura’s (1997) social-cognitive
learning theory. Contemporary self-regulated learning theory focuses on the tran-
sition from dependent to autonomous learner. Several main themes emerge from
this research. The first is that self-regulated learners rely on an integrated repertoire
of cognitive, metacognitive, and motivational skills. Second, self-regulated learners
use these skills to plan, set goals, implement and monitor strategy use, and evaluate
their learning goals. Third, self-regulated learners use a wide variety of strategies
in flexible ways, augmenting these strategies with a variety of adaptive motivational
beliefs such as high self-efficacy and epistemological world views.
This review summarised six general instructional strategies that promote self-
regulation by helping students develop a repertoire of cognitive skills, metacognitive
awareness, and resilient motivational beliefs. There are many ways that cognitive,
metacognitive, and motivational skills are enhanced using these strategies. Table 1
summarises a few of the main ways that each of the six instructional strategies
improves cognition, metacognition and motivation.
Our review of the science education literature reveals that while there has been
some research focused on metacognition little is available on the broader topic of
self-regulation. We argue that self-regulation is of tremendous importance to all
learners and the general education literature supports this view. Schools need to
prepare students as life-long learners in science and other academic domains as
well. We feel that there is a great deal that science educators currently do, and could
do in the future, to promote self-regulation and that past research on metacognition
provides something of a platform for moving forward in promoting and researching
self-regulated learning. Research suggests that when these instructional strategies
are implemented, science learning and achievement improves. We hope our review
prompts science educators and researchers to think more carefully about the infusion
of instructional strategies that do so.
Correspondence: G. Schraw, University of Nevada, Las Vegas, 4505 Maryland
Parkway, Box 453003, Las Vegas, NV 89154-3003, USA
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Table 1
Ways the Six Instructional Strategies Increase Cognitive, Metacognitive, and
Motivational Processes.
Cognitive
processes
Metacognitive
processes
Motivational
processes
Inquiry Promotes critical
thinking through
experimentation
and reflection
Improves explicit
planning, monitoring,
and evaluation
Provides expert
modeling
Collaboration Models strategies
for novices
Models self reflection Provides social
support from peers
Strategies Provides a variety
of strategies
Helps students
develop conditional
knowledge
Increases self-effi-
cacy to learn
Mental Models Provides explicit
model to analyse
Promotes explicit re-
flection and evaluation
of the proposed model
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restructuring and
conceptual change
Technology Illustrates skills
with feedback.
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and simulates
data
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evaluate, and revise
models
Provides informa-
tional resources
and collaborative
support
Personal Beliefs Increases engage-
ment and persis-
tence among stu-
dents
Promotes conceptual
change and reflection
Promotes modeling
epistemology char-
acteristic of expert
scientists
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