Chapter 3.5
Computer Contexts for Supporting Metacognitive Learning
Xiaodong Lin
Teachers College, Columbia University
Florence R. Sullivan
University of Massachusetts, Amherst
Abstract: A major challenge for both educational researchers and practitioners is to understand
why some people seem to learn more effectively than others and to design tools that can help
less successful people improve their abilities to learn. In this chapter, we describe the most
frequently documented metacognitive learning outcomes including: recall/memory; content
learning/problem solving; and social interactions as knowledge acquisition. We then use each of
these metacognitive learning outcomes to examine how today’s computer tools have or have not
reached their fullest potential to support these learning outcomes and we suggest ways that
computers tools can be designed to achieve these outcomes.
Key words: metacognition; metacognitive learning; metamemory; content knowledge; problem
solving; social interaction; adaptive expertise
3.5.1 Common Metacognitive Learning Outcomes
Some 30 years ago, Brown and Flavell introduced the concept of “metacognition” to the
educational research community (Brown, 1975; Flavell, 1976). Metacognition is defined as an
awareness of one’s own thinking processes and the ability to control, monitor and self-regulate
one’s own learning behaviors so effective problem solving and deep understanding can be
reached. In 1983, Brown, Bransford, Ferrara and Campione did a comprehensive summary and
analysis of metacognitive research. They concluded the analysis by suggesting that a variety of
learning outcomes can be produced when people are engaged in metacognitive experiences.
For instance, people who are aware of the limitations of their own memory and deliberately use
rehearsal strategies recall more than those who are not aware of their own limitations (Wellman,
1977). In terms of content learning and problem solving, the research shows that people are
able to apply what they learn in new situations if they are involved in intentional instruction
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where they understand how, why, when and where the new information and strategies are
useful (Brown et al., 1983).
A third learning outcome, that has not been given enough attention, is the relationship
between social interactions and metacognition. This is particularly important in terms of
classroom teaching. Researchers have found that teachers interact with students with good and
poor reading skills quite differently. Good readers are questioned about the meaning behind
what they are reading, asked to evaluate and criticize materials, and so on. By contrast, poor
readers primarily receive drills (McDermott, 1978). What kinds of metacognitive understanding
get developed from these different kinds of social interactions for both students and teachers?
This is an interesting question to explore.
In this chapter, we discuss how different types of metacognitive learning outcomes can
be developed from different situations and how different situations require different
metacognitive skills. We focus on the following learning outcomes: (1) simple recall and
memorization of facts; (2) more complex learning outcomes, such as problem solving; (3)
domain subject learning; and (4) social knowledge. We then examine how today’s computer
tools have or have not reached their fullest potential to support these learning outcomes and we
suggest ways that computers tools can be designed to achieve these outcomes.
3.5.2 Recall and Memory What Research Says
Among the learning outcomes, recall seems to get the most attention for a variety of
reasons. The first is that the ability to recall or memorize is sensitive to developmental and
learning material changes. Older children remember better than younger ones and typical
children recall better than children with developmental delays. The research also shows that
when the materials are familiar and the items are distinct, age differences are minimal (Myers,
Clifton & Clarkson, 1987). The second reason that recall receives a great deal of attention is that
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it is one of the most frequently used assessment measures by teachers, school systems, and
national testing agencies.
Metamemory refers to learner awareness about his or her own memory systems and
memory strategies. Research indicates that young students and novice learners have difficulty
accurately estimating their comprehension and that metamemory strategy instruction should
focus on specific strategic knowledge. Metamemory can be divided into two types: explicit and
conscious knowledge and implicit and unconscious knowledge (Brown et al., 1983). An example
of explicit metacognitive knowledge, that even preschoolers are consciously aware of, is that it is
easier to remember a simple and short word than a long and complex word. Such selfmonitoring enables people to generate a feeling of knowing that can help them predict how well
they will remember later on. However, often, metacognitive knowledge is unconscious. For
instance, good readers slow down their reading when the texts become difficult without realizing
they are doing so (Siegler & Alibali, 2005).
Research on the relationship between memory and metacognition has been motivated by
the assumption that children’s increasing knowledge about their own memory and about the
strategies they use to facilitate memorization can help them choose more effective strategies for
memory. Whether or not metacognition facilitates memory is a somewhat tricky question. On the
one hand, research shows that young or learning disabled children tend not to use rehearsal or
other strategies to facilitate their memory because they may not know that their memory capacity
is limited (Brown et al., 1983). But once they are trained to use effective strategies, they greatly
improve their memory performance. If older students are prevented from using effective memory
strategies, they produce levels and patterns of performance that are very similar to younger
children or children with learning disabilities. In addition, knowing the relative usefulness of
strategies could improve children’s strategy choices in a wide range of situations (Brown, et al.,
1983; Siegler & Alibali, 2005). This is one of the most robust findings in the developmental
literature (Belmont & Butterfield, 1971; Brown, 1975; Kail & Hagen, 1977).
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However, metacognition alone may not improve memory – other ingredients need to be
in place. These ingredients include developmental capabilities (the ability to associate and
recognize things), use of broadly applicable memory strategies (such as rehearsal, organization,
and selective attention), and knowledge about the specific content (Siegler & Alibali, 2005).
Metacognition can considerably assist memory performance only when each of the ingredients
is present (Siegler & Alibali 2005).
3.5.3 Ways to Improve Memory Performance
There are several ways to help learners become effective in memory and recall tasks.
One way is simply to rehearse the facts until they are remembered. This approach usually does
not lead to understanding, especially when a task requires application of the facts learned
(Brown et al., 1983). More effective ways are to employ different kinds of metacognitive and
planful memory strategies, such as elaboration, identifying main ideas and categorization
strategies (Brown et al., 1983). The most frequently cited research on metamemory regard
interactions between understanding and strategies, and learning facts as they are applied in
varied-contexts.
Many researchers argue that the application of elaboration, categorization, and
generation strategies are important for comprehension and thus memory performance
(Anderson & Reder, 1979; Bransford, et al., 1982; Brown et al., 1983). However, the degree to
which any of these strategies are successful in improving memory is influenced by the
availability of relevant content knowledge (Chi, 1978). Nitsch (1977) showed when students
study the same concept in varying contexts; they are better able to understand the concept in
new situations. Research by Hatano & Inagaki (1986) also shows that experiencing varied
contexts is important to the development of adaptive expertise. Adaptive expertise is
characterized as procedural fluency complemented by explicit conceptual and principle
understanding that allows people to adapt what they learn to widely varied situations.
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3.5.4.Computers as Metacognitive Tools to Enhance Memory
A program developed by Bransford and his colleagues (Cognition and Technology Group
at Vanderbilt, 2000) - the Knock Knock™ game, offers a promising example of using computers
as metacognitive tools to enhance literacy and memory. Knock Knock™ helps children become
aware of constraints on their own learning that they need to address in order to be successful
with the game. For example, to achieve the best results children have to use broadly applicable
memory strategies, such as rehearsal, organization, generation, categorization, and selective
attention strategies. They also need to generate simple stories based on the letters they hear or
read. The children will also develop knowledge about the specific content that they are learning letters, sounds, and story writing. To facilitate metacognitive development, children are asked to
estimate how well they will apply the letters to a variety of different situations and discuss their
applications with peers. The discussions among peers and with teachers also offer students
social support and help students recognize the usefulness of the strategies in helping them
perform the memorization and application tasks. Knock Knock™ illustrates an approach of using
computers to support recall and learning that should help students develop skills that are
important for future success.
3.5.5.Content and Domain Subject Learning: What Research Says
In this section, we examine issues concerning the importance of acquiring content
knowledge of any given discipline from the perspective of adaptive expertise development.
Hatano and his colleagues introduced the concept of adaptive expertise in relation to masters in
using the abacus. They proposed that abacus masters should be termed routine experts if they
have only developed procedural knowledge and skills about the abacus they learned. Whereas,
adaptive experts understand the principles and concepts underlying the content and skills
learned. He and his colleagues contrasted routine experts with adaptive experts, and asked the
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educationally relevant question of how “novices become adaptive experts – performing
procedural skills efficiently, but also understanding the meaning and nature of their object.”
(Hatano & Inagaki, 1986, pp. 262-263). Procedural knowledge is often only useful for limited
types of problems and situations. Comprehending principles underlying problems and content
learned enables people to flexibly apply this knowledge to various new situations (Hatano &
Inagaki, 1986).
As such, adaptive experts usually verbalize the principles underlying one’s skills, judge
conventional and non-conventional versions of skills as appropriate, and modify or invent skills
according to local constraints. Wineburg (1998) and others (e.g., Bransford & Schwartz, 1999)
have added to this list by pointing out that adaptive experts are also more prepared to learn from
new situations and avoid the over-application of previously efficient schema (Hatano & Oura,
2003).
A second perspective Hatano and Inagaki suggested is that in stable environments,
participation in one’s culture typically provides sufficient resources for learning and executing
routine expertise. People have many pockets of routine expertise where they are highly efficient
without a deep understanding of why. To develop adaptive expertise, people need to experience
a sufficient degree of situational variability to support the possibility of adaptation. This variation
can occur naturally, or people can actively experiment with their environments to produce the
necessary variability. Hatano and Inagaki (1986) proposed three factors that influence whether
people will engage in active experimentation.
One factor is whether a situation has “built-in” randomness or whether technology
has reduced the variability to the point where there is little possibility for exploration. Much
software we reviewed often eliminates situational variability to help students focus on the
procedural skill. This is particularly true of software aimed at helping students develop literacy
and numeracy. For example, many math programs, such as Math BlasterTM
(http://www.knowledgeadventure.com/mathblaster/), present students with a storyline or game-
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like interface, but these conceits are meant as a means of motivating students only, and in fact,
math learning is presented in a drill and skill format, wholly divorced from any meaningful context
in which math may be learned. Likewise, math-tutoring programs, such as Wayang Outpost
(http://k12.usc.edu/WO/ ) (Beal & Lee, 2005), while providing a motivating storyline and
individualized and helpful feedback to students on the procedure of solving a problem, do not
provide varied situations in which the math skills may be needed. This may have the unintended
consequence of preventing students from developing variations in that procedure in response to
new situations.
The second factor involves the degree to which people are enabled to take risks in
approaching a task. When the risk attached to the performance of a procedure is minimal,
people are more inclined to experiment. “In contrast, when a procedural skill is performed
primarily to obtain rewards, people are reluctant to risk varying the skills, since they believe
safety lies in relying on the ‘conventional’ version” (Hatano & Inagaki, 1986, p. 269). Game-like
software that provides rewards for successful performance of the procedure or skill will limit risktaking, thereby limiting students’ ability to adapt their understanding to new situations.
The third factor involves the degree to which the classroom culture emphasizes either
understanding or prompt performance. Hatano & Inagaki (1986) state, “A culture, where
understanding the system is the goal, encourages individuals in it to engage in active
experimentation. That is, they are invited to try new versions of the procedural skill, even at the
cost of efficiency” (p. 270). They proposed that an understanding-oriented classroom culture
naturally fosters explanation and elaboration, compared to a performance-oriented classroom
culture. Their views also echo the research findings by Bereiter & Scardamalia on the
importance of engaging students in a knowledge and understanding-oriented society and their
impact on adaptation and human development (Bereiter & Scardamalia, 2000; Scardamalia &
Bereiter, 1996). Central to these concerns is people’s ability to self-monitor their own
understanding at a deep principle level.
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3.5.6 Ways Metacognition can Improve Content Learning and Adaptive Expertise
Neither metacognitive monitoring skills nor content learning alone will do the job of
improving people’s deep understanding of the subject matter leading to adaptive expertise in a
specific domain. Rather, the two work in concert with one another in the following ways. First,
utilizing familiar content knowledge improves the effectiveness of using different metacognitive
strategies. Second, familiar content facilitates learning of new strategies such as elaboration
(Bransford et al., 1982). Familiar content may also serve as “a kind of practice field upon which
children exercise emerging memory strategies” (Siegler & Alibali, 2005, p. 262). Third, content
knowledge facilitates people’s metacognitive development by offering specific data and a
context in which to monitor and revise their strategies and procedures. Research shows that
metacognition works best when an individual has specific issues to work through (e.g., Chi,
DeLeeuw, Chiu, & LaVancher, 1994; Lin & Schwartz, 2003). This is because people think best
when they have a known specific context to work with (Gay & Cole, 1967). Indeed metacognitive
monitoring is often retrospective, capitalizing on a specific past as opposed to a vague future.
Ample research shows that effective metacognitive interventions can improve people’s
understanding of deep principles that underlie content and problems in a given domain. The
majority of metacognitive interventions involve either a strategy-training approach, or a
contextualizing knowledge and tools approach aimed at supporting students metacognitive
monitoring and revision of understanding. In recent years, researchers have also started to
recognize the importance of creating social interactions to support metacognition. Each of these
approaches will be discussed below.
Metacognitive strategy training. The main purpose of strategy training research is to
explore: (a) how specific sets of metacognitive strategies help people monitor conflicting
thoughts and build a coherent understanding of a subject domain; (b) how specific metacognitive
strategies will help people develop deep principles about the concepts learned; and (c) how
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different types of instructional supports for metacognitive strategies influence students’
engagement in metacognitive activities. Metacognitive strategy training is usually used during
the acquisition of either domain-specific or self-as-learner knowledge. Students usually stop at
fixed intervals while learning specific subject domains to reflect on and revise their work. The
interventions usually do not involve changing the existing school curriculum and classroom
culture. The most effective approach to strategy training seems to be prompting students to selfexplain or self-question as a way to engage in metacognitive thinking and modeling through
social interactions.
The act of explanation helps students become aware of the strategies they are using and
the content they are learning. For instance, Siegler and Jenkins' (1989) found children who were
aware of using a new strategy subsequently generalized it more to other problems. However,
research also indicates that students often fail to check and monitor whether or not they
understand the content knowledge they are learning if they are not explicitly trained to do so
(Brown et al., 1983). Chi et al., (1994) found that prompting self-monitoring in students leads to
such awareness and stronger learning outcomes. Moreover, the prompted students who
generated a large number of self-explanations (the high explainers) learned with greater
understanding than the low explainers. Chi et al., (1994) reported that such monitoring through
self-explanation helped students recognize principles underlying the content and procedures
learned, not just the procedures. This provides an important basis for the development of
adaptive expertise (Hatano & Inagaki, 1986).
Researchers have also used video technologies to model effective strategy applications.
For instance, Bielaczyc and her colleagues used video to model effective learning strategies
employed by good problem solvers in the domain of LISP programming (Bielaczyc, Pirolli &
Brown, 1995). Students were exposed to specific metacognitive strategies and received explicit
training in their use. They found that mere exposure to good learning models was not sufficient.
The key to the success in their design was to have students experience these strategies in their
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own learning, explicitly compare their own performance with that of the model, and take actions
to revise ineffective learning approaches.
Contextualizing knowledge and tools. Contextualizing content learning and metacognitive
acquisition is important in helping people recall and make sense of what they learn. Research
shows that people’s ability to understand the meaning of the concept learned seemed to depend
on cues provided by context-specific situations under which the concept is originally learned
(Bransford & Franks, 1976; Nitsch, 1977). This is because contexts provide constraints to the
concept learned and enhance the specificity of the encoding (Tulving, 1982). In addition,
contexts provide a framework that is needed for people to understand the purpose and
significance of learning specific concepts and strategies. This view is consistent with what Brown
and her colleagues (1983) call "informed training plus self-control" in which students are
informed of the contexts within which the new strategies are most useful. These strategies also
enhance self-control skills such as planning, checking, self-monitoring and evaluation. Without
such "conditionalized" knowledge, students face difficulties in using learned strategies in new
settings (Brown et al., 1983). The interventions that have resulted in failures of understanding
and transfer involve situations where students are taught strategies without understanding why,
when, and how they are useful (Duffy & Roehler, 1989).
3.5.7.Computers as Metacognitive Tools to Scaffold Content Learning and Metacognitive
Thinking
New computer technologies can provide powerful scaffolds and tools for principle-based
content learning and metacognitive thinking by (1) displaying problem-solving and thinking
processes (process display); (2) prompting students attention to specific aspects of strategies
while learning is in action (process prompts); (3) modeling metacognitive thinking processes that
are usually tacit and unconscious (process models); (4) creating social interactions through
community-based activities and (5) bringing exciting curricula based on real-world problems into
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the classrooms to provide meaningful contexts and purposes for the content learning (Bransford,
Brown & Cocking, 1999; Lin, Hmelo, Kinzer & Secules, 1999). Several software programs utilize
one or more of these elements in their design.
Process displays. Content-based software programs and on-line learning environments
have been created that feature process display in the design. For example, the Web-based
Inquiry Science Environment (WISE) (http://wise.berkeley.edu/) (Linn, Clark, & Slotta, 2003)
provides students with an inquiry map that displays the sequence of events the student will
execute as she works in WISE. Therefore, each student can clearly see and reflect upon the
activities she will perform, including engaging in discussion, gathering evidence, reflecting when
prompted to do so, and engaging in hands-on experiments. This type of process display is also
utilized in the Digital IdeaKeeper (Quintana, Zhang, & Krajcik, 2005).
Process prompts. Betty’s Brain (http://www.teachableagents.org/betty) (Biswas,
Schwartz, Leelawong, Vye, & TAG-V, in press) is a software program that utilizes teachable
agents to help students learn topics in science, such as river ecosystems. The multiple agent
approach allows for students to engage with the software environment as both a learner and a
teacher. For example, one agent in this program is Mr. Davis, this agent is provided as a mentor
to the student using the system. Mr. Davis provides feedback to students in the form of
metacognitive prompts including the importance of goal setting, understanding chains of
reasoning and understanding how to self assess one’s own learning and knowledge. Meanwhile,
the teachable agent in the system, Betty, also incorporates metacognitive prompts by making
seemingly spontaneous comments about her own learning, which prompts the student who is
working with the software to reflect on how well he is teaching Betty.
Process models. iSTART (http://csep.psyc.memphis.edu/istart/front.htm ) (Graesser,
McNamara, & VanLehn, 2005) is an agent-based reading comprehension software program that
integrates both process prompts and process models in its design. Two of the agents in the
system are the Microsoft agents, Merlin and Genie. Merlin acts as a teaching agent and Genie
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acts as a student agent. In iSTART Merlin asks Genie a question and Genie provides an
answer, the student using the software is then shown a list of five metacognitive strategies and
asked to pick which ones Genie used to solve the problem. By picking the strategies Genie
used, the students are actively engaged in thinking about the metacognitive process. The
software also features a trio of agents (an instructor and two student agents) who interact with
one another to simulate and model the utilization of the targeted reading comprehension
strategies.
Inquiry Island (http://thinkertools.org/Pages/sciwise.html ) (White & Frederiksen, 2005) is
a science-learning environment that also utilizes a number of design elements to enhance
metacognitive understanding including process prompts, model prompts and collaboration.
Inquiry Island is a multi-agent environment featuring software advisors related to tasks involved
with inquiry, general cognitive, metacognitive and social aspects of science learning and
systems development issues (see Figure 1). The software provides a process display through
the organization of the agents. For example, there is a task advisor for each step in the cycle of
inquiry (e.g. Hugo Hypothesizer). These agents prompt students about processes through giving
solicited advice. The process is modeled through the use of a notebook interface that has tabs
for each of the steps in the cycle. Finally, Inquiry Island provides students an opportunity to
assess both their own learning and the learning of their peers. Software enabled peer
assessment may assist in the development of a robust learning community.
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Figure 1. Inquiry Island Interface
Social interactions. Knowledge Forum (http://www.knowledgeforum.com/ ) (Scardamalia
& Bereiter, 2006) is an excellent example of a software environment that makes thinking visible
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through process prompts and develops a community of learners through the interactive nature of
the notes system. Students working in Knowledge Forum are prompted to express their theories
and to provide evidence in support of these theories. These prompts help students to organize
their ideas and to learn scientific argumentation skills. Students may respond to one another’s
notes with confirming or disconfirming evidence. In this way, the group works collaboratively to
understand ideas and concepts and to build their knowledge.
Real world problems. The fifth design element that supports content learning and
metacognitive development is bringing real-world problems into the classroom as objects of
inquiry. The Technology Enhanced Learning in Science (TELS) project
(http://www.telscenter.org/ ) (Linn, Lee, Tinker, Husic, & Chiu, 2006) is a web-based, online
learning environment that focuses on real-world problems (see Figure 2). The TELS project is
devoted to making unseen science processes visible through animation and other graphical
representations and it emphasizes current dilemmas in science, heightening the real-life appeal
of the curriculum to students.
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Figure 2. TELS Real World Project Interface
Many of the software learning environments discussed in this section can be utilized to
present varied content and learning situations to students. An important research question to
pursue in relation to these environments is how they contribute to the development of adaptive
expertise in students.
Other software environments for developing students’ metacognitive abilities together
with content learning are Digital IdeaKeeper (http://hi-ce.org/digitalideakeeper/index.html
)(Quintana, Zhang, & Krajcik, 2005), Autotutor (http://www.autotutor.org/ ) (Graesser,
McNamara, & VanLehn, 2005).
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3.5.8. Social Interactions as Learning Mechanisms: What Research Says
In recent years, we have witnessed an increasing interest in research on what and how
people learn through social interactions. This section reviews several avenues of recent
research in social knowledge and ways such knowledge can profit from metacognitive thinking.
There are several reasons why knowledge creation is viewed as a social act. First of all,
interaction with other people is a significant catalyst to knowledge and skill building. For
instance, there are many active lines of research in developmental psychology showing that
adults and older siblings provide pivotal social scaffolding to support children’s task performance
and knowledge development (Rogoff, 2003; Siegler & Alibali, 2005). Such social interactions
allow children to extend the range of their activities and to perform tasks that would be
impossible for them to perform alone. However, not all social interactions will lead to improved
knowledge and performance. In scaffolding children, adults have to tailor their support to
children’s level of skill development (Greenfield, 1984; Kermani & Brenner, 2001). Research also
shows that social interactions play an important role in children’s language development (Siegler
& Alibali, 2005).
Second, our own perspectives and knowledge are often broadened and deepened as a
result of social interaction. For example, children with siblings perform better on a false belief
task than children with no siblings because they have more chances to learn about other
people’s thinking (Jenkins & Astington, 1996). Studies on social recognition memory show that
people’s memories benefit more from social interactions and conversations than individual
learning, especially for difficult subjects (Wright, Mathews & Skagerberg, 2005). This is because
social interactions provide more access and perspective cues that can be used to facilitate
memory and recognition. People tend to neglect relevant and useful information that they do
have in hand when they are left alone to learn and assess themselves (Dunning, Heath & Suls,
2004). Therefore, other people’s views can expand metacognitive knowledge about one’s own
learning and understanding.
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Third, social knowledge is important in helping people understand the social world and
social interactions. There is evidence that one’s behavior with respect to others is influenced in
various ways by what one knows (e.g., believes, assumes) about what specific others know. For
example, when college students are preparing for a test, knowledge about the instructor can
help them anticipate what questions the instructor might ask them and how detailed their
knowledge needs to be to pass the test. Knowledge about other people is particularly important
in developing harmonious social interactions with others. Such knowledge helps people form
mental models about what others know and feel, which can reduce the chance of offending
other people and lead to better predictions and understanding about how others will behave and
what others are thinking about and talking about in specific situations (Nickerson, 1999). This is
particularly important for collaborative learning where communication among group members is
critical to the success of group performance. In a series of four experiments, Karabenick (1996)
found that participants’ awareness of their co-learners question asking activity affected
judgments of their own and others’ levels of comprehension. In order to coordinate and
communicate effectively with other group members, people must have a reasonably accurate
idea about what specific other people know and say. This is especially true for teaching.
Teaching knowledge about students and parents is critical in order for teachers to effectively
communicate and interact with students of other cultures (Lin, Schwartz & Hatano, 2005).
3.5.9.Ways Metacognition can Improve Social Interactions and Vice Versa
Research literature portrays a symbiotic relationship between metacognition and social
knowledge. On the one hand, metacognition has shown to have positive effects on social
interactions. On the other hand, certain kinds of social interactions have shown to help people
develop productive metacognitive skills.
Meta-social interaction. Meta-social interactions means “…keep[ing] track of how it is
going and taking appropriate measures whenever it needs to go differently. Because this last
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suggests a regulatory as well as a feedback function for the monitoring process…” (Flavell,
1981; pp. 272-273). For instance, social metacognitive comments might include, “I sense that
what I said has hurt your feelings” or “why did you say that” or “how did you come up with such a
conclusion…” An awareness of what one knows and others know or do not know, and
clarifications of group goals and responsibilities, which are metacognitive in nature, have been
shown to facilitate social learning (Barron et al., 1998; Lin, 2001; Lin, Schwartz & Bransford,
2007).
According to Flavell (1981), there are four kinds of metacognition that affect social
interactions. They are: (1) metacognitive knowledge (all the things you could come to know or
believe about self and other people or group); (2) metacognitive experiences (any conscious
cognitive or affective experiences or states of awareness related to social interactions; e.g.,
sudden awareness that you don’t know what your collaborators are up to); (3) goals and sub
goals (the various objectives that may be pursued during a social interaction) and (4) strategies
(behaviors one carries out to attain these social goals and sub goals).
What sort of impact can metacognitive knowledge have on social interactions? It can lead
one to select, establish, evaluate, revise, and terminate social cognitive tasks, goals and
strategies; it can lead one to take into consideration one’s relationships with others and with
one’s own interests in the social interaction (Flavell, 1981). Metacognitive experiences can be
brief or lengthy in duration, simple or complex in content. For instance, you may feel confused
about what others are saying or you may feel that others are confused about what you are
saying. Such awareness is helpful in strengthening social communication and relationship
development because these confusions can be addressed and clarified while the conversations
are ongoing (Flavell, 1981). Several studies find that monitoring and regulation of social
interactions in group work can help students overcome obstacles in their progress towards
successful solution of mathematical problems (Goos, 2002; Goos & Geiger, 1995; Shoenfeld,
1999). Goos (2002) reported that in the classroom, collaborative metacognitive activities were
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characterized by students offering their thoughts to peers for inspection, while acting as a critic
of their partners’ thinking. Such reciprocal interaction improved student learning significantly in
comparison with groups that did not engage in such social monitoring and regulation.
Social interactions as a means to develop metacognitive knowledge and skills. Research
indicates that certain kinds of social interactions can lead to metacognitive development. One
way to encourage this is to develop communities where metacognitive discourse and deep
understanding are the shared goals. For example, cooperative group work, whether in jigsaw or
other approaches, requires that an individual reflect not only on his or her own efforts, but also
on how those efforts relate to the group’s goals. Alternatively, metacognitive thinking can benefit
from social interactions when an individual seeks constructive criticism from a community and
modifies his or her practices based on group feedback.
The Fostering Communities of Learners (FCL) program provides an excellent example of
developing learning communities to support metacognitive practice (Brown & Campione, 1996).
Brown and Campione’s interventions brought changes to the social structure in first through
eighth grade classrooms in the subject areas of ecology and biology. There are three key
components in FCL. Metacognitive activities are embedded in each of the components and are
arranged into a learning cycle. The cycle begins by researching a set of topics in a specific
domain, moves into sharing the research, and ends by performing consequential tasks to
demonstrate learning.
At the beginning of the learning cycle in the FCL model, teachers and students make
decisions jointly about which metacognitive activities to engage in, based on the learning tasks
at hand. For instance, reciprocal teaching activities (e.g., Palincsar & Brown, 1984) are called for
when a research group senses trouble in understanding and explaining reading materials. Group
collaboration is encouraged when students and adults take turns being the leaders, so that
students are exposed to mature modeling of self-control, comprehension, and monitoring
strategies and are then given the opportunity to practice these strategies (Brown & Campione,
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1996). At this stage of the FCL model students may engage in guided writing and composing
activities or in face-to-face or online consultation and reflection with peers or domain experts.
In the sharing section of the cycle, students communicate their research findings with
members from other groups, by engaging in jigsaw and cross-talk activities. During cross-talk,
the whole class engages in discussion led by both the students and the teacher. They take on
metacognitive roles and ask each other to self-assess and report their research findings to date.
The learning cycle ends by performing a consequential task, where a variety of forms of
assessment are offered. These assessment activities include clinical interviews, transfer tests,
and thought experiments. The consequential tasks are intended to help students revise their
own learning, understand why they do what they do (rather than following a set of procedures)
and provide teachers opportunities for feedback before the next instructional unit.
3.5.10 Computers as Metacognitive Tools to Enhance Social Interaction
Social interaction as a learning mechanism has many potential implications for the
design and development of computer technologies as metacognitive tools. One is that people’s
knowledge can be conceptualized in terms of their ability to perform tasks with supportive social
interactions. A second implication is that knowledge acquired through social interactions can be
used to expand and deepen one’s own knowledge and perspectives, which in turn can enhance
social interactions and communications. A third implication is that certain types of social
interactions, such as guided participation or scaffolding based on sensitive understanding of the
learners, should be emphasized in the computer tool development process. Therefore, it may be
valuable to design computer tools accompanied by classroom lessons and other types of
educational activities to facilitate these types of social interactions.
Both Knowledge Forum and Inquiry Island are software environments that address these
implications. For example, in Knowledge Forum the emphasis is on community knowledge
building (Scardamalia & Bereiter, 2006). Social interaction is an integral part of learning in this
20
environment. Student knowledge building is scaffolded not only through the note prompts
discussed in the previous section, but by learning from interaction with peers. In this
environment, students learn to consider other’s opinions or evidence and to resolve
inconsistencies through discussion and argumentation. Likewise, Inquiry Island is an
environment that not only emphasizes peer assessment, but also features agents that model
social aspects of learning. These agents are the collaboration manager, the equity manager, the
communication manager, and the mediation manager (White & Frederiksen, 2005). Students
working in the Inquiry Island environment interact with these managers to learn more about how
to work together in small groups to solve a problem.
Lin has also been developing a social metacognition software environment called the
Ideal Student (see http://www.idealoquy.com/). In this environment, students advise an agent
who is portrayed as a new student in their school. The students are asked to give the agent
advice on the ideal qualities of a student in their school in order to help the agent adjust to this
school. The ultimate goal of this environment is to make explicit students’ social mental models
of their school in order to help both teachers and students become aware of their own social
mental models and possible sources contributing to such social mental models (see Lin, 2001;
Lin, Hmelo, Kinzer & Secules, 1999; Lin, Schwartz & Hatano, 2005). Such awareness is the prerequisite for changing ineffective attitudes and social mental models. Teachers can also use the
software to make explicit their social mental model of the ideal student. The environment gathers
and aggregates data from many schools. This data is than available to users of the system. In
this way, teachers may compare their own social mental models with their students and with
students from other schools, as a result teachers can begin to use such “contrasting cases” to
see their own classrooms more explicitly and clearly. This gives them a vantage point from
which they can begin to use their knowledge about students to inform classroom instruction.
Another approach Lin and her colleagues are currently experimenting with is an
environment that will help students develop knowledge of the self-as-learner (Lin, et al, 2005).
21
Their approach is to have students develop a sense of self-as-learner by teaching others in a
virtual learning environment (e.g., technology-based social simulations). These “virtual kids” are
equipped with many different kinds of personalities. The students’ job in the classroom is to
teach these virtual kids how to develop appropriate personalities and goals for learning,
including self-beliefs, attitudes, and knowledge, for a wide range of learning situations. In
addition, students are also asked to create different social environments that support these
personalities. It is hoped that by teaching others and creating a supportive virtual environment,
students will, in turn, develop a stronger metacognitive knowledge of self-as-learner. This kind of
learning may also help students identify factors they need to consider in designing a supportive
social environment. There are some exciting research opportunities in this area.
An intriguing question for future research and for software development is: how much
metacognitive knowledge can people develop about themselves and the culture of their
communities through the use of computer tools? Our view is that ones’ culture can make a
difference in the development of metacognitive knowledge, and software designed specifically
for cultural awareness can highlight important aspects of learning and community practices that
affect both teachers and students. Such software can help people see different perspectives and
it can aid in an overall process of coming to know one another in a classroom environment.
3.5.11 Conclusion
In conclusion, there are various types of metacognition for different kinds of learning
situations. Recall and metamemory, content knowledge and problem solving, and social
interactions are all areas of learning that can be improved through metacognition. Recall and
metamemory is enhanced through the metacognitive strategies of generation, elaboration and
categorization. The Knock Knock™ game is a good example of literacy software that utilizes
these strategies.
22
We addressed content knowledge and solving problems in a domain through the lens of
the development of adaptive expertise (Hatano & Inagaki, 1986). In general, content software
will be improved by providing the situational variability needed to begin developing the skills
related to adaptive expertise. Having noted this, we did find a number of outstanding pieces of
software and online learning environments that have been designed to develop student’s
metacognitive abilities in concert with the development of content knowledge. These excellent
environments include WISE (Linn, Clark, & Slotta, 2003), Betty’s Brain (Biswas, Schwartz,
Leelawong, Vye, and TAG-V, in press), Digital IdeaKeeper (Quintana, Zhang, and Krajcik,
2005), Autotutor (Graesser, McNamara, & VanLehn, 2005), iSTART (Graesser, McNamara, &
VanLehn, 2005), Inquiry Island (White & Frederiksen, 2005), Knowledge Forum (Scardamalia
and Bereiter, 2006) and the TELS project (Linn, Lee, Tinker, Husic, & Chiu, 2006).
Finally, we addressed the social aspects of learning and the role of metacognition in
developing certain types of social knowledge. Social knowledge is a key aspect to successful
group work and to classroom interactions as a whole. Both the Knowledge Forum and Inquiry
Island are excellent examples of software that aims at fostering learning communities. Lin and
her colleagues have also been engaged in the development of this type of software. These
technology-based social simulations focus on developing both a sense of self-as-learner in the
student, as well as an understanding of the social aspects of the learning environment they
inhabit. We argue that reflection on this type of social knowledge will aid in the creation of
productive classroom learning environments. In summation, an excellent first generation of
software environments for recall/memory, content learning, and learning through social
interaction has been created, the second generation may well concern itself with the question of
how these environments can be improved to assist in the development of metacognitive
adaptive expertise.
23
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