Computer Contexts for Supporting Metacognitive Learning

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 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 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) 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 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 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- 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, 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 3.5.6 Ways Metacognition can Improve Content Learning and Adaptive Expertise Neither metacognitive monitoring skills nor content learning alone will 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 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 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 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 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 10 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 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 16 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 17 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 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 18 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, 19 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 what they (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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Bransford, J D & Franks, J J (1976) Toward a framework for understanding learning In G Bower (Ed.), The psychology of learning and motivation, 10 (pp.93-127) New York: Academic Press Bransford,