EDUCATIONAL PSYCHOLOGIST, 40(4), 211–223 Copyright © 2005, Lawrence Erlbaum Associates, Inc. WHITE AND METACOGNITIVE FOSTERING FREDERIKSEN DEVELOPMENT
A Theoretical Framework and Approach for Fostering Metacognitive Development Barbara White Graduate School of Education University of California at Berkeley
John Frederiksen College of Education University of Washington
This article provides an overview of our work on the nature of metacognitive knowledge, its relationship to learning through inquiry, and technologies that can be used to foster and assess its development in classrooms as students engage in collaborative inquiry. To illustrate our theoretical ideas, we present examples from our Inquiry Island software. It provides learners with advisors, who contain knowledge, advice, and tools aimed at supporting students’ metacognitive development in the context of doing inquiry projects. Our pedagogical approach includes having young learners take on the roles of various cognitive, social, and metacognitive advisors as a way of enacting and internalizing the forms of expertise they represent. We describe a sequence of learning activities and indicate how students respond to them, using examples and findings from a 5th-grade class. Our work shows how such learning tools and activities can foster the development of metacognitive knowledge and skills needed for collaborative inquiry and reflective learning.
We are pursuing a vision of learning in which students work together on inquiry projects, guided by software advisors, such as a Planner and a Reflector, which become an important part of the students’ learning community. In this vision, students reflect on and try to improve the cognitive models of expertise that are embedded in the software advisors, so that the advisors can provide better advice and tools in the future. They also try to internalize the advisors’ expertise through activities such as taking on the role of one of the advisors in a class discussion or group work. Our premise is that by working with, modifying, and taking on the roles of software advisors, students will develop the various capabilities needed for collaborative inquiry and reflective learning that are initially conveyed by the software advisors. Further, through developing this expertise, students and classrooms will be transformed into self-aware, self-improving systems; that is, they will create theories about what they are doing and why as they constantly engage in cycles of planning, monitoring, reflecting, and improving. Correspondence should be addressed to Barbara White, Graduate School of Education, University of California at Berkeley, 4533 Tolman Hall #1670, Berkeley, CA 94720–1670. E-mail:
[email protected]
To achieve this vision, we argue that developing metacognitive expertise is crucial. It is crucial in fostering an individual’s learning through inquiry and in their learning how to improve their learning processes though inquiry. It is also crucial to groups who are learning how to work together and trying to improve as a team. Furthermore, in our software environment, it is crucial to being able to choose appropriate software advisors for a given task, as well as to making good use of and learning from the guidance and tools they provide. Thus we argue that everyone in a learning community needs to speak and do metacognition. This includes being able to talk about the different cognitive, social, and metacognitive capabilities that are needed, and when and why they are useful. It also means developing what are often called regulatory skills, like planning, monitoring, and reflecting. Finally, it includes what we refer to as “developmental expertise,” that is, expertise about how you improve your capabilities through inquiry and reflection. Unfortunately, little emphasis is placed on metacognitive knowledge and skills as critical goals for learning in the national curricular standards. The science standards, for example, emphasize developing knowledge of a domain through inquiry, but they place far less emphasis on metacognitive
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knowledge and skills (National Research Council, 1996). Yet, increasingly, research has shown that metacognitive expertise is needed in developing knowledge through inquiry (Frederiksen & White, 1997; Georghiades, 2004; Hogan, 1999; Kuhn & Pearsall, 1998; White & Frederiksen, 1998, 2000) and is critical in transferring ones’ capabilities for learning in one domain context to learning in new domains, as well as taking charge of one’s own learning (Bransford & Schwartz, 1999; Brown & Campione, 1996; Scardamalia & Bereiter, 1991). There is also evidence that feelings of self-efficacy in learning play a strong role in students’ motivation and interest in learning (Pintrich & de Groot, 1990; Schunk & Schwartz, 1993). Building metacognitive knowledge of oneself as a learner contributes to viewing oneself as an able learner, which influences not only success in learning, but also motivation to learn (Brown, 1988; Corno, 1986; Zimmerman, 1989; Zimmerman & Schunk, 2001). In this article, we will reflect on our work on learning through inquiry, on the nature of metacognitive knowledge, and on how technologies can be used to foster their development in classrooms, as students engage in collaborative inquiry and reflective learning. We will begin by presenting a theory of the nature of metacognitive knowledge and processes, how they foster learning across domains of study, and how they are linked to students’ views of themselves as learners. To illustrate aspects of metacognition, we will use samples of cognitive models and tools that are incorporated in our Inquiry Island software. Inquiry Island was designed to provide learners with a source of information, advice, tools, and work environments that support their development of metacognitive and inquiry expertise in the context of doing research projects, such as science-fair projects, which engage students in creating and testing theories. Our pedagogical approach emphasizes how the curriculum should also include learning activities away from the software environment, where students, working together in groups, take on cognitive, social, and metacognitive roles as a way of enacting and internalizing the expertise represented in the software advisors. Following the theoretical discussion, we will describe a sequence of learning activities we have developed, and illustrate how students respond to and benefit from these activities using examples from classroom research. We will end the article by arguing that technologies of the future will increase the need for students and their software advisors to be able to talk about and employ metacognition.
structures needed for learning through inquiry, (b) knowledge of how one organizes and manages one’s inquiry processes in the course of learning, and (c) knowledge of how to apply inquiry to improve one’s cognitive, social, and metacognitive capabilities for inquiry learning. We will argue that students need to develop explicit cognitive models of capabilities needed for inquiry. Such models help students learn how to do inquiry, as well as to understand its nature and purpose. For example, they allow students to think and talk about the characteristics of different capabilities, such as questioning and analyzing, and why they are useful. Being able to engage in this type of metacognitive thinking and talk makes it easier to manage these processes. Furthermore, one needs explicit models of processes used in inquiry to reflect on, or conduct research about, how to improve them. In the following, we will frame our arguments in terms of students learning through inquiry in (a) science and (b) reading, the areas in which we have carried out our work. However, we don’t see the arguments—or the approaches to using technology—as specific to these domains of learning.
A THEORY OF METACOGNITIVE KNOWLEDGE AND SKILL
The inquiry cycle: The top-level goal structure. As students carry out their inquiry projects, they record their ideas and findings in a research notebook, shown at the left of Figure 1. It is structured around a series of phases or steps of inquiry, referred to as the Inquiry Cycle, which is illustrated in Figure 2. Each notebook page represents a step in the Inquiry Cycle. This cycle provides a top-level goal structure for inquiry. In using this Inquiry Cycle, students (a) develop their
Our theory seeks to identify, model, and teach the kinds of metacognitive knowledge and skills that will enable students to develop strong capabilities to learn via inquiry in new domains. In outline, our theory emphasizes three types of knowledge: (a) knowledge about the capabilities and goal
Knowledge About Learning Through Inquiry Knowledge of how theories and models are formulated, evaluated, and revised through investigation and analysis is a critical aspect of metacognitive knowledge. If students are to develop a general cognitive theory of how to do inquiry, they need access to knowledge of the varieties of goals and purposes that inquiry entails, as well as the strategies and methods needed to achieve them. Such specifications of the processes needed for inquiry are cognitive models that represent target competencies for students to develop. To support students’ acquisition of these competencies, the cognitive models of inquiry processes need to be readily available to students in a form that makes them accessible and usable in the course of their inquiry, much as a tutor might present useful ideas to students, while they are engaged in a difficult task, and then explain how to use them. We have developed a learning environment for guiding students’ inquiry (Eslinger, 2004; Eslinger, White, & Frederiksen, 2004; Shimoda, White, & Frederiksen, 2002; White, Shimoda, & Frederiksen, 2000). It includes a community of software advisors who “live” on Inquiry Island. This learning environment, illustrated in Figure 1, has been designed to scaffold and support students’ learning in a number of ways as they carry out inquiry projects.
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FIGURE 1 A screen from the Inquiry Island learning environment, which includes the Research Notebook (left), in which students record work for their research projects; the Goal Sliders (far left), in which students evaluate their work; the Advisory Window (top left), in which advisors indicate when they have relevant advice; and the Advice Window (right), in which advisors present their advice. Inquiry Island can be downloaded from http://thinkertools.soe.berkeley.edu/ and its successor, the Web of Inquiry, can also be accessed from this Web site.
research question, (b) generate hypotheses, (c) design an investigation, (d) record and analyze their data, (e) create a model, and (f) evaluate the utility and limitations of their model, as well as their research processes, and identify new questions to investigate.
Task advisors: Cognitive models for inquiry processes. There are a number of important subtasks associated with each step of the Inquiry Cycle, which correspond to the workspaces within each page of the notebook. For in-
FIGURE 2 An inquiry cycle.
stance, the Investigate step includes creating a logical design for your investigation, planning how you will carry it out, collecting and recording data, and keeping track of problems you encountered that might affect your conclusions. To support this finer grain aspect of learning how to carry out inquiry, Inquiry Island includes a set of software advisors, called inquiry-task advisors, one for each of the steps of the inquiry cycle. Inquiry-task advisors house knowledge of goals for that inquiry step, strategies for accomplishing them, and criteria for monitoring their effectiveness. To make it easier for students to keep the advisors straight, each has a name, such as Quentin Questioner and Ivy Investigator. As an example, Ivy Investigator has six goals to work toward: (a) choosing an appropriate situation in which to observe the phenomenon you are studying, (b) making sure it will allow you to test your hypotheses, (c) planning carefully how you will carry out the investigation, (d) making sure you have the resources to carry it out, (e) checking that you have an adequate sample of data, and (f) fully documenting your research, including any changes or problems that occurred. The inquiry-task advisors provide detailed cognitive models for students to employ in performing all of the subtasks of each inquiry cycle step.
General-purpose advisors: Models of cognitive, social, and metacognitive competencies. In addition to developing an understanding of the inquiry task at hand, such
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as analyzing or modeling, students need to think about how that task is related to other inquiry tasks, and also about how their general cognitive, social, and metacognitive competencies can contribute to working on that task. To represent this idea, each page in the research notebook has a team of advisors who can offer relevant advice. These teams typically include task advisors for related inquiry steps, as well as an important addition: general-purpose cognitive, social, and metacognitive advisors, who have expertise that may be useful in working on that inquiry step. Examples of cognitive advisors include Ingrid Inventor (who has strategies for coming up with new ideas) and Sydney Synthesizer (who is concerned with fitting different ideas together). Examples of metacognitive advisors include Pablo Planner (who has strategies for effective planning) and Molly Monitor (who is concerned with monitoring the efficacy of work processes and the quality of products). Examples of social advisors include Keiko Collaborator (who has strategies to help people collaborate well with one another) and Manny Mediator (who helps members of a group work out their differences). The faces that appear above the students’ work area in Figure 1 reveal the team of advisors for the question step of the inquiry cycle. By clicking their mouse on them, students can consult any member of the current advisory team for each step in the inquiry cycle. When they consult an advisor, it presents its advice in a browser window (shown at the right of Figure 1). The learning environment has been designed to make advisors’ cognitive models as explicit and transferable as possible. This is achieved through (a) housing the advice in functional units, like the Planner and Inventor; (b) presenting advice in labeled categories, such as Goals, Plans, Strategies, and Motives; (c) providing advice in a generic, domain-independent form alongside several examples set in different domains; and (d) encouraging users to reflect on the quality and utility of the inquiry products and processes that result from using these models. Managing and improving one’s inquiry processes depends on students’ having full knowledge of the cognitive, social, and metacognitive capabilities that are needed for learning via inquiry. Inquiry Island has been designed to provide a structure for acquiring knowledge of these processes. To help students understand and keep track of the full system of advisors, we have adopted the social metaphor of a community of advisors living on Inquiry Island, each of whom has particular competencies and who call on one another in carrying out inquiry. Task advisors focus on organizing and accomplishing particular phases of work, and cognitive, social, and metacognitive advisors have general strategies that are useful in many situations. This personification of advisors’ roles allows students to think of the orchestration of their competencies as similar to organizing a group of experts for carrying out an inquiry task. In fact, when students carry out collaborative inquiry projects in their class, the community concept is actually more than just a metaphor; it is a way of organizing roles that students can take in managing and
carrying out their group’s work. The metacognitive tasks of planning, monitoring, reflecting, and revising are activities of the group, and the social competencies become crucial in their accomplishment. There is no firm line to be drawn between the social and individual enactment of inquiry. What in the individual context are cognitive competencies become roles in the social context of their group. This first aspect of metacognitive expertise, which we have characterized as knowledge of the cognitive, social, and metacognitive processes needed for learning through inquiry, forms the foundation of our theory. It relates to what Brown (1987) referred to as knowledge about cognition in that it includes knowledge of widely applicable cognitive, social, and metacognitive processes. In addition, because this component of our theory includes models of how one learns through inquiry, it also incorporates aspects of what some researchers have referred to as knowledge of how one comes to know (e.g., Kuhn, 2000) or epistemological theories (e.g., Hofer & Pintrich, 1997). We have taken the view that a generic theoryof scientific inquiry, combined with models of widely applicable cognitive and metacognitive capabilities, can become a theory of learning through inquiry in general. Our view is consistent with the notion of the child as a scientist, who is constructing and testing theories about the world, as well as about his or her own cognitive and social processes (Gopnik & Wellman, 1994). We emphasize the need for education aimed at enabling children to develop increasingly sophisticated forms of these capabilities so that their ability to learn, adapt, and accomplish a wide variety of tasks can be enhanced. Metacognitive Knowledge for Action: Organizing and Managing One’s Inquiry Processes Learning through inquiry is complex as it consists of a large repertoire of capabilities, along with their underlying concepts, goals, strategies, and tools, which are used together. Students need to develop a detailed understanding of how, when, and why these capabilities are engaged in the course of work if they are to be able to function as responsible agents in managing their own inquiry. But how can students learn when and how to engage their cognitive, social, and metacognitive competencies as they undertake the phases of inquiry? One hypothesis is that they need to develop what is sometimes referred to as metacognitive knowledge for action, that is, knowledge of how one organizes and manages one’s cognitive and metacognitive processes in the course of their application. Our learning environment supports a number of approaches to developing this type of expertise. To start with, Inquiry Island includes metacognitive advisors, who provide explicit cognitive models of planning, monitoring, revising, and reflecting, which students can use to guide their work. These processes can be invoked in the management of all the other processes, including themselves. They represent a subset of the processes proposed by various researchers who the-
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orize about self-regulated learning (e.g., Azevedo, 2002; Boekaerts, Pintrich, & Zeidner, 2000; Butler & Winne, 1995; Flavell, 1979; Hacker, Dunlosky, & Graesser, 1998; Palincsar & Brown, 1989; Schoenfeld, 1987; Winne, 1995), and are aimed at supporting key aspects of what Zimmerman termed the forethought, performance, and self-reflection phases of self-regulation (in Schunk & Zimmerman, 1998). As a further support for monitoring and reflection, the software provides “Goal Sliders” (shown at the left side of the notebook in Figure 1). These enable students to self-assess and reflect on their progress for each goal of that inquiry step as they work. The slider settings for a goal incorporate level descriptors, which provide characterizations of work quality for each setting. To link self-assessment to the advisory system, the sliders send messages to the relevant software advisors, enabling them to provide advice that is appropriate for the current state of students’work. This way of accessing relevant advice further motivates students to self-assess. Thus, students learn that monitoring the quality of one’s products and processes is important in managing inquiry. In addition, aspects of planning and monitoring are represented in the system. First, there is the top-level structure of the Inquiry Cycle, which orders high-level inquiry goals. Students learn the logical purpose of the steps within the inquiry process, and the value of keeping track of where they are within the Inquiry Cycle. Second, the task advisors provide cognitive models for important subtasks. These models typically include a requirement that products created by other task advisors be available as a condition for the task to proceed. For example, the investigator needs alternative hypotheses to proceed with designing an experiment. The products of one task need to meet the requirements for the next task for it to be undertaken. And, if a new task is attempted prematurely, the deficiencies in products of precursor tasks will become apparent, leading students to go back and do additional work on them so that they are good enough to proceed. Although knowledge of the general structure of inquiry and interlinkages among task requirements is helpful in controlling the marshalling of inquiry tasks, these do not take into account how and when to invoke general cognitive, social, and metacognitive processes that are useful in inquiry. To learn how and when to apply the general-purpose advisors, these advisors are included on the team when working on a particular aspect of inquiry, and the ones that are included are based on an assessment of the needs of a typical group. In practice, if a project is being carried out by a group of students (which is usually the case), then, if the group feels that it has trouble communicating and coming to a consensus, the team should include the communicator and the mediator. Self-knowledge of the group members’ cognitive and social competencies is important in knowing what competencies are available, in deciding which to include, and in choosing which group members should assume each advisory role and responsibility. When advisors are included in the team, their cognitive models provide them with criteria for monitoring
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and principles for deciding when they should intervene and for selecting advice they might offer to improve the inquiry process. Our theory of enabling students to learn how to organize and manage their various capabilities thus integrates many of the pedagogical principles espoused by advocates of self-regulated learning (e.g., Zimmerman & Schunk, 2001). It includes initially presenting students with goal structures for inquiry and self-regulation and subsequently asking students to decide which processes are going to be needed for a given task. It also includes engaging students in various forms of self-assessment and reflection, while providing them with explicit models of self-regulatory processes like planning and monitoring. Further, it includes incorporating knowledge about when a given process is relevant within the cognitive model for each of the software advisors on Inquiry Island, so that students can see when that process might be useful. We conjecture that this ability to utilize intelligent software agents and to have students adopt their roles as they work together on projects will prove to be a powerful pedagogical tool in fostering self-regulated learners. Knowledge of How to Apply Inquiry to Improve One’s Competencies for Inquiry Perhaps our most important pedagogical goal in developing inquiry and metacognitive expertise is to transform students, their learning tools, and classroom environments into a self-aware, self-improving system. We want students to become learners who create theories about what they are doing and why as they engage in cycles of planning, monitoring, reflecting, and improving. There are a number of ways to enable them to do this, and they all involve an interesting metacognitive move: the recursive application of their inquiry processes to understanding their own processes of inquiry learning to improve them. When we work with students, we call this “inquiry about inquiry.” Research on inquiry has to be carried out around another inquiry that is going on in a group. In other words, two inquiries are going on at the same time: inquiry in the domain of study and inquiry about how you develop knowledge through inquiry. Inquiry about inquiry can take a number of forms, which can range from simply reflecting on weaknesses and thinking about how to improve, all the way to full-fledged scientific inquiry, in which, for example, one invents new strategies and carries out controlled comparisons to determine which are the most effective. For instance, students may focus on a particular advisor’s strategies, evaluate them to see if they are helpful, and, if they are not, then modify them, perhaps by adding some new strategies to make the advice better. They could then test the new strategies as they do their next project, or they could have other groups try out the new strategies and study how well the strategies work for them. In doing this, they would be developing hypotheses, designing an investigation (thinking of data they will collect), and interpreting
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their findings in ways that will enable them to improve the advisor’s strategies. Testing the strategies on themselves is an example of reflective learning. Testing the strategies on others is essentially doing educational research. The instructional idea associated with inquiry about inquiry is to have students represent their ideas in the form of cognitive models that are embedded in the advisors. To test these ideas, they follow the advisor’s advice or take on its role in their group, while the group undertakes an inquiry project. Students evaluate the group’s ability to use the advisor’s advice in their task situation and its usefulness in doing the task. They then use this information to revise the advisor, or to change their ideas for where it is useful. There is a built-in validity check to this process for improvement: If their ideas can’t be enacted or aren’t understandable, or if they are not functionally effective for the task at hand, students will have evidence that their ideas need to be revised. Thus they are applying their knowledge of modeling and inquiry to their own cognitive and social processes of inquiry to improve them. This notion of inquiry about inquiry is consistent with Schraw and Moshman’s (1995) claim that it would be beneficial for students to develop explicit formal theories of their cognitive processes, as well as with Bandura’s (1997) idea of self-experimentation and Scardamalia and Bereiter’s (1983) notion of children as coinvestigators who work with researchers to investigate cognitive processes needed for writing. Inquiry about inquiry represents an extreme position, arguing that not only will children benefit from developing explicit models of their own cognitive and metacognitive capabilities, but that they will also benefit from conducting theory-based, empirical research in which they test competing hypotheses about these capabilities and how to improve them. In what follows, we describe how we implemented these pedagogical ideas within a classroom and provide evidence that they do indeed enhance children’s capabilities for inquiry, while also developing their metacognitive expertise.
PEDAGOGICAL ACTIVITIES FOR DEVELOPING METACOGNITIVE EXPERTISE In this section, we describe a variety of classroom activities that enable students to develop the aspects of metacognitive expertise that we argue are needed for learning how to learn through inquiry and to get better and better at it. We present a sequence of activities undertaken in a fifth-grade classroom in an urban school. The objective was to develop a progression that motivates and fosters students’ capabilities for collaborative inquiry and reflective learning. The activities are linked to the cognitive models represented by the software advisors of Inquiry Island. They demonstrate how students’ understanding of these models is enhanced by their use as roles to play as they carry out collaborative inquiry activities apart from the software. The activity sequence we present,
along with examples of students’ performance, highlight our fundamental view of the role of technologies in learning: They provide models for understanding human cognition and social practices that are directly useful for doing inquiry, as well as for talking about cognitive and social processes and how to improve them. To support this claim, we will summarize some of the results of our analyses of students’ performance. A more complete account of our analyses and statistical results can be found in White and Frederiksen (2005). Doing Research Projects With Guidance From Software Advisors The fifth graders began by using the Inquiry Island software to guide them as they undertook a research project, which was about their own cognitive processes and was of great interest to them. For their projects, they worked together in groups of three to develop and test alternative theories about the effects that different types of music would have on their performance as they did math problems in class. This research topic was chosen as one for which students would have a lot of experience and knowledge, which they could use to develop research questions, motivate their alternative hypotheses, and guide their analyses. This pedagogical activity introduced students to the Inquiry Cycle, as they worked on their projects under the guidance of successive inquiry task advisors found on Inquiry Island. While doing their projects, students learned what an advisor’s expertise is like, in terms of the goals and strategies that the advisor recommends, and they learned to formatively assess their inquiry products using the Goal Sliders, such as those shown at the left of Figure 1. We evaluated students’ research projects based on a series of questions that assess particular features of their work that are desirable, such as “Do they include a second hypothesis?” “Is it an alternative to the first?” and “Do they explain why someone might believe it?” (Frederiksen & White, 1997). Scores were then calculated for sections of the research notebook based on the percentage of desirable features that were shown. This enabled us to see how well students were understanding the goal structure of inquiry and what is required to meet those goals. Overall, students’ research on music and schoolwork received high scores, with an average total score of 76%. Averages for each of the six inquiry tasks are Question (70%), Hypothesize (98%), Investigate (88%), Analyze (65%), Model (79%), and Evaluate (78%). Nearly all of the student groups developed clear research questions, and also provided background and rationales motivating their questions. An example of a rationale that one group wrote is Music can be distracting when having lyrics for it will draw the student in and get them more interested in the song. It shows that the song can be distracting but, depending on the thing your doing, it may effect the outcome.
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All of the groups developed two or more theoretically distinct hypotheses. In carrying out their investigations, they all developed research designs that enabled them to test their hypotheses. The greatest difficulty the six groups had was that two thirds of them overlooked results that could have helped them develop a better model or conclusion. Nonetheless, all of the groups developed a clearly stated model. An example is, “We found that music that you like affects your score. It is because you get distracted from your work because you like the music.” The students also exhibited metacognition in evaluating the usefulness and limitations of their model, and included responses such as This research evaluation could help other teachers if they wanted to know if listening to music during math time would help or hinder their grade. It would probably be best if only fifth-grade teachers used our data because different math subjects can change our percentages. Taken together, these findings support our position that working in domains for which students already have many theoretical ideas provides a good initial venue for learning about inquiry. Taking on the Roles of the Advisors In the next activity, students learned more about the roles of the general-purpose cognitive, social, and metacognitive advisors, while they engaged in a collaborative activity. Their primary task, as they worked in reading groups of four or five, was to come up with and discuss a series of below-the-surface questions related to a novel they were reading. (A below-the-surface question is one that does not have a simple yes-or-no answer. For example, What motivated a character in the story to do a certain action?) For each question, they wrote a report documenting their initial ideas about the question, the evidence for and weaknesses of each idea, their subsequent theories, and the possible relationships of their theories to their own lives. This was a reading inquiry task we created to have commonalities with scientific inquiry. As students worked on investigating their questions, they played different managerial roles in which they enacted the roles of different software advisors. They started by playing cognitive roles (theory manager, evidence manager, synthesis manager, application manager), moved on to social roles (collaboration manager, equity manager, communication manager, mediation manager), and finally they played metacognitive roles (planning manager, productivity manager, revision manager, reflection manager). For the metacognitive roles, the emphasis is on planning, monitoring, revising, and reflecting. The planning manager’s goals, for example, are to get the group to decide on its goals, develop a plan for achieving them, and figure out who will do what. The students were told that playing these
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metacognitive roles will slow their work process down so that the group can think more about how to create better work products and how to improve their work processes. For instance, the productivity manager should stop the group from time to time and get the group to assess its progress. If there are problems, the revision manager should step in and get the group to fix them. Playing these roles turns metacognition into a fundamentally social process, both in terms of its development through playing roles, and in terms of its execution as the groups collaboratively plan, monitor, revise, and reflect. Our objective is for metacognition, and cognition itself, to remain as social processes even when students are working alone: We want them to think in terms of putting on different hats and adopting different voices to achieve different functions, like planning or theorizing, which correspond to the different roles that have been played in their groups. To facilitate students’ taking on each role, we provided them with a “role guide” for each of the roles, which contains a subset of the advice that is found in the software advisors. For each of a role’s goals, the guide provides a statement of the goal (for instance, one of the reflector’s goals is “think about how to improve the way the group works”). It also describes problems that occur if the goal isn’t achieved. Then the role guide provides one or two strategies for achieving the goal (such as, “point out weaknesses” and “suggest improvements”), along with examples of things you could say to your group to implement that strategy (such as, “some weaknesses of how we worked are …” and “maybe next time it would be better if we …”). Thus these role guides, like the software advisors, provide students with a model of expertise for that role in terms of goals to pursue, strategies for achieving each of those goals, and things you could say to implement each strategy. The students used the role guides and engaged in various types of conversations as they learned to play the roles. For example, in one type of conversation, role managers get their group to apply one of their role’s strategies, as in the following excerpt from the transcript of a group (transcripts throughout the article use pseudonyms and are edited for the sake of brevity and readability): Reflection manager (looking at role guide): “You guys, I think we need to reflect on what we did. Nicole, how did you judge our work?” Nicole: “We did okay, but we could have concentrated more.” Rachel: “And we could have given more complex answers.” Reflection manager: “Matt, how did you judge our work?” Matt: “I judge that we did well. But I also judge that people need to be more serious.” Based on videotapes of two groups, we carried out a taxonomic analysis of their conversations while they were engaged
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in the reading discussion task and played the metacognitive roles. We analyzed conversations in which they were either playing or talking about the roles. This analysis allowed us to determine how they felt about the different roles and how aptly they played them. For the metacognitive roles, the planning manager was clearly seen as a desirable role in that students argued about who got to play it. This role also promoted good planning behavior in the groups. In contrast, the revision role received the most frequent complaints, such as that it is redundant with the reflection role. Further, when students played this role, they often engaged in actions like correcting grammar or spelling in their reports rather than finding and fixing deeper problems, such as revising the plan of action for the day. Analyses such as these enable us to rethink the set of metacognitive roles to make them seem more worthwhile and accessible to the students and promote better self-regulation. Planning, Monitoring, and Improving an Advisor’s Role In keeping with the basic premise that self-regulation is an important tool for learning, the students were asked to reflect on how they played each role by following the metacognitive cycle shown in Figure 3. A reflective journal was the vehicle for facilitating this process: It asks students to plan, then monitor, and then reflect on how they achieved their role’s goals. This is similar to using the research notebook in Inquiry Island. The planning page in the reflective journal is completed at the start of each task. Its purpose is to get students to focus on their role’s goals and strategies, to decide which of their goals has the highest priority for that day, and to choose which strategy they will use to achieve it. The monitoring page of the journal is completed while students are doing the reading discussion task. It asks them to keep track of whether or not they are succeeding at getting their group to achieve their role’s goals and to note any problems that occur. The reflection page of the journal is done after the reading discussion task is completed. It asks students to rate how well
the group did at achieving each of their role’s goals. Then it asks them to identify the weakest goal and to suggest how they could get their group to do better at achieving that particular goal. The students then typically try out their “improved” strategies for achieving their role’s goals in the next reading group meeting. Thus, the reflection page of the journal feeds into the plan for the next meeting, and students go round and round this metacognitive cycle as they continue to play and improve their roles. By analyzing the students’ reflective journals, we developed evidence of how students were learning the cognitive models that are embedded in the roles. For instance, on the reflection page, they were asked, “Which of your role’s goals was your group weakest at?” and “How could you get your group to improve at achieving that goal?” In coding students’ responses to the latter question, four characteristics of a good “how-to” response emerged. The first is that it be strategic, that is, something you strive to achieve. An example of a strategic response is, “I would try get my group to be more focused.” The second characteristic is whether or not they presented a specific strategy, meaning a response that is close to a plan for how you would get your group to do better. An example is, “I would give everyone something to do and then complain if they didn’t do it.” The third characteristic is whether their response is relevant to the particular role they were playing. And the fourth is whether what they wrote was related to the specific goal that they claimed their group was weakest at. For the goal of “producing high-quality work,” an example of a good answer is, “by asking, while we are going along, if this is our best work.” We compared students’ responses the first time they wrote in their reflective journal, when they had just begun playing the roles, to their responses last time they wrote in their journals, having played the roles for approximately twelve 1-hr sessions. We created a scale by adding a point for each of the four characteristics of a good response. We found that there was a significant improvement from their first journal entries to their last entries, t(14) = 3.12, p = .004. For their first entry, the mean score was 2.3, whereas for the last entry, the mean score was 3.4. This provides evidence that the metacognitive process carried out in using the reflective journal was helping students learn how to generate more specific strategies, which could help them and their groups achieve a role’s goals. Creating and Investigating Theories About the Utility of the Advisors
FIGURE 3 A metacognitive cycle.
In this and the remaining activities, the four student groups that constituted the original reading groups, who had been reflecting on and improving their own role playing in their group, now became research groups whose task was to study other reading groups’ use of roles. In keeping with our view that formulating and testing explicit theories about sociocognitive processes is key to developing metacognitive expertise, as well as to learning how to learn, in their next ac-
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tivity we had the students develop and test theories about the utility of the roles. In doing this, they were guided by the Inquiry Island software, as they were in their music and schoolwork research projects. They investigated questions such as, “Which roles are the easiest and most important to start with, and why?” Having played and reflected on the roles provided a foundation from which they could generate different theoretical ideas, which they used in developing their hypotheses. Some are “The cognitive roles are easiest, because they are the most simple and they introduce what we are going to do”; “The social roles help because they help us interact with our group more”; and “Start with all the roles because they teach all of the things we are supposed to know.” The groups then tested their competing hypotheses by conducting research on reading groups of their peers, who were just starting to play the various roles. For example, one group decided, “We will observe all the groups with roles and groups without roles. We will make rubrics for evaluating their participation and the quality of their work. We will compare roles with no roles.” This research group then created several rubrics and used them to make ratings for the individual students in the reading groups during the 2nd and 3rd days they used the roles. They presented their data and concluded (correctly) that “The people with all the roles got higher scores for participation, productivity, and the quality of their work than people with no roles.” Their discussion of How Your Model Could be Generalized revealed the depth of thought the students put into their work: We think that these roles could be helpful in all subjects, science fair projects, partnership projects (for the social roles). If you have an argument at work, or something, you can use the social roles to help you solve the disagreement. You might not be aware that you are using the social roles, but you may have learned it earlier. The roles could help at some jobs, like being a lawyer ’cause you use the evidence, and come up with a theory, and you put it all together, and ask different opinions or argue. We coded the groups’ research projects in the same way that we coded the music and schoolwork projects. Their performance was very similar to that for the earlier project, but there were improvements in performance for the analyze step (from a score of 65% in the earlier project to 73% in the roles research project) and for the model step (from 79% in the earlier project to 93%). Both of these increases appear to be due to improvements in the students’ application of their knowledge and theories of the domain they are investigating, in this case, their knowledge of the nature of roles and how they improve a group’s functioning. For instance, in carrying out an analysis of their data, 67% of groups in the earlier project were coded as having overlooked results that were relevant to their hypotheses, whereas only 50% received such a code for the research on roles. The largest difference was in explain-
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ing their models, where 33% of the groups in the earlier projects were coded at the highest level, whereas 100% received the highest level code for research on roles. These improvements are consistent with the idea that, when students have greater depth of understanding of the domain they are investigating, this enables them to better relate their data analyses and explanations to their competing theories about the topic. Choosing Roles Needed for a Particular Task Each day that the students engaged in the research on roles, the research groups had to decide which roles were needed for the task that they were undertaking that day. Their choices were written on a role selection sheet, which asked them to say which roles they selected and why, as well as who will play each role, as in Name of Role 1: Mediation Manager. Who will play this role: Maria. Why did the group think this role would be useful today? We need to get along. This activity was aimed at (a) helping students see how the various roles could be relevant in different contexts and (b) providing them with experience at applying the roles in the context of scientific inquiry, having previously used them for the reading discussion tasks. These groups, which consisted of four or five students, chose, on average, 5.4 roles for each research session. Across all five sessions, the social roles were chosen most often (an average of 11.25 times per role), followed by the metacognitive roles (averaging 9.75 times per role), followed by the cognitive roles (averaging 6 times per role). The three roles chosen most often were equity, mediation, and productivity managers. The three roles chosen least often were the synthesis, application, and evidence managers. It is interesting that these fifth graders selected the social roles as being necessary most often, but this was not because they were the most recent roles they had played in their reading groups (which were the metacognitive) or the ones they spent the most time playing (which were the cognitive). This finding is possibly indicative of group self-awareness about the importance of social processes, such as equity and mediation, in enabling a group to function effectively. Reflecting on the Purpose and Utility of the Different Roles Near the end of this sequence of curricular activities, we conducted interviews with the students and administered a test, which asked them to write about the purposes of the three types of roles—the cognitive, social, and metacognitive roles. Over the course of the curriculum, the groups had played all of the different types of roles. Our concern was that if they could not keep these three types of roles distinct and
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understand their different purposes, then students probably could not internalize the different roles or think about how to improve them, which would invalidate our theory.
Goals for each role. To test students’ knowledge of the purposes of the social, cognitive, and metacognitive roles, we asked students to write down the goals for each of the 12 roles. One might predict that, if you asked a fifth grader (or anyone else) about the goals of a role such as a planner, they would simply say, “to develop a plan.” However, most of the students went beyond that type of response and included several of the role’s goals. For example, for the planner, some students included “deciding on goals” and “figuring out who will do what.” The role guides typically gave three goals for each role. For each student, we calculated the average number of goals that were mentioned for each role type. The averages were as follows: 1.48 for cognitive roles, 1.65 for social roles, and 1.51 for metacognitive roles. Only one student had an average below 1 goal per role. Purposes of roles. Another type of question on this test asks about the general purposes of each type of role. For instance, the students were asked, “How do the metacognitive roles help a group to function better?” Nearly all of their responses to this particular question fit into one of two broad categories. In the first type, which encompasses 71% of the responses, the students said that the metacognitive roles helped you to think about something, such as how to improve your thinking processes or your work. The most frequent was thinking about thinking itself, which accounted for half of the responses in this category. In the second type of response, which encompasses 29% of the responses, the students said that the metacognitive roles helped you to improve something, such as your planning, your work, or your thinking. Both types of responses suggest an understanding of the purpose of metacognition, but particularly the latter type, because a primary purpose of metacognition is to enable you to improve. Interviews. In the interviews, one of the questions that students were asked was, “Could you ever imagine finding the metacognitive roles, or something that you learned from playing them, useful later in school or work?” The following is an example of a high-rated response to this question. Maybe, after you finished a project, you could be like the Revision Manager and look back to see if there’s any things, like making sure that everything makes sense, that nothing seems like weird, and you can reflect back at your work and see if it was the best work that you could have done, and that you could do better. Another example of a high-rated response is,
They would help you get better work done, and they help you get moving faster and get better products. And that would be very useful if, you know, like if there is a report due. It would help you get your work done better and faster—sort of a way of checking yourself and making sure you are still doing what you are supposed to and doing it well. Nearly all of the students generated reasonable responses to this question, with a third producing such high-rated responses.
Assessing Metacognitive and Inquiry Expertise Fifth-grade students in this school were given an assessment of metacognition at the beginning and end of our curriculum (except ELL, learning disabled, and absent students). The students who undertook this sequence of pedagogical activities, including role playing and the research on roles, gained significantly on our assessment of metacognition, t(13) = 3.82, p = .002; whereas fifth graders in this school who did not participate did not show a significant gain, t(10) = .68, p = .51. In this assessment of metacognitive expertise, students watch a movie and then complete a written test. The movie shows two students doing a research project and, from time to time, one of them turns to the camera and expresses a metacognitive thought or indicates that she has a thought (which is not expressed). In the written assessment, the students are presented with excerpts from the movie’s script and are asked to respond to either a justified multiple-choice question or a free-response question. The questions ask students to generate or select an appropriate metacognitive thought for the actor in the movie, or to characterize the type of metacognitive thought (planning, checking understanding, and so forth) expressed by the actor, or to explain why that type of metacognition is important at that point in the actors’ dialogue. Regarding their inquiry skills, students in the fifth-grade class who used Inquiry Island to do a research project showed significant gains on an inquiry test, t(20) = 1.78, p = .04. Their posttest scores were also significantly higher than those of a comparison group of students, t(37) = 3.46, p = .001; which consisted of fifth graders in the same school who did not use Inquiry Island. This inquiry test asks students to engage in a thought experiment (White & Frederiksen, 1998). It provides them with a research question (which is randomly chosen from one of five possible questions) and asks them to generate and justify two alternative hypotheses about the answer, design an experiment to test these hypotheses, make up data that are consistent with their design, analyze their made-up data, reach a conclusion, and explain which hypothesis, if any, their research supports. Our prior research indicates that simply taking this test twice does not lead to significantly higher scores (White & Frederiksen, 2004). Thus these findings provide fur-
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ther support that our software and curricular activities lead to increased capabilities for inquiry. Developing and Testing New Tools and Expertise for an Advisor In the preceding school year, students in the same teacher’s fifth-grade class worked on another kind of activity for improving the roles. These students had played the social roles while doing science projects (see Borge, White, & Miller, 2005). Then they were asked to create an artifact that could be added to an advisor’s toolkit to improve it. They were told that the purpose of the artifact should be to help someone understand and play that particular role. One example of the artifacts created by students is a “problems and solutions chart” that students created for their advisor, “Matt Mediator,” which is shown in Table 1. Other examples of students’ artifacts are (a) a story about “A Day in the Life of a Collaboration Manager,” in which Chippy Collaborator tells about problems he encountered and how he solved them; (b) a reward system, using popsickle sticks, for the collaborator to use to encourage groups to share ideas; (c) a story with the title “An Island Torn” illustrating how a superhero, named Communication Man, uses democratic strategies to heal Inquiry Island; and (d) an interview with Mongoose Mediator with questions about how to be a mediator and answers giving strategies to use in different circumstances.
Analysis of artifacts students created. We analyzed the nature and content of the 14 artifacts students’ created for evidence of their understanding of the nature of the roles they had been playing. We found that students created a wide variety of artifacts. All of them differed from those that were provided in their toolkit; 57% were forms suggested by their teacher (these were stories, rubrics, and videotaped interTABLE 1 An Artifact Developed by Students to Improve the Advice for the Mediator No. 1 2 3
4 5 6 7
Problem People accept things without thinking about them. People aren’t saying their ideas. People are criticizing other people, not ideas. People are not working and are goofing off. The critiques are too negative. People feel they weren’t treated fairly. Whoever speaks loudest or last gets their way.
Solution Discuss ideas with the group. Encourage everyone to discuss their ideas with the rest of the group. Discuss this with group and come up with punishments for people who are just criticizing people. Talk to your group about this and come up with rules for your group. Give good reasons for why something needs to be changed. Find a solution that gives everyone some of what they want. Vote on what to do.
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views), whereas 43% were completely novel genres (including a productivity chart, a problem-solution chart, and an integrative poster that links tools in the kit with their purpose, situations where you’d use them, and what you’d say when you use them). Half of the artifacts provide some type of tool or information resource that a manager can use when they are actually playing their role. These include a problem-solution chart and a productivity chart for keeping a daily record of rubric scores and comments on progress. In addition, 64% of the artifacts illustrate how the role is played, such as in a story or in interview in which the role manager talks about how he or she carries out the role. Only one artifact did not address the way a role is played in actual situations or provide a tool that could be actually used while playing a role. Thus the majority of the students’ artifacts were clearly concerned with helping someone who is learning to play or carrying out a role. Regarding the quality of the artifacts that student produced, 79% showed “a deep and accurate understanding of several aspects of the role,” 14% showed “a more limited understanding of the role by focusing on only one particular aspect or on several aspects in a limited way,” and 7% of the artifacts “show little or an inaccurate understanding of the role.” We also scored the comprehensiveness of the artifacts, that is, whether or not they directly addressed all of the goals for the role. All of the artifacts directly addressed at least one of the role’s goals. Of these, 36% addressed all of the goals directly, and 64% of them chose to focus on certain goals and not cover all of the goals for that role. After creating the artifacts, the students then went on to conduct research, using the Inquiry Island software, on the utility of the artifacts they had created by surveying other students who used them to play their particular role (see White & Frederiksen, 2005).
THE UTILITY OF OUR APPROACH TO FOSTERING METACOGNITIVE DEVELOPMENT Our theory of the metacognitive expertise needed to enhance inquiry learning, combined with our pedagogical theory that emphasizes cognitive modeling and role playing, led to the sequence of instructional activities described in this article. Our research findings indicate that this sequence is effective in increasing students’ inquiry skills and in developing their metacognitive theories and capabilities (White & Frederiksen, 2005). Our observations of classroom video and interview data, along with comments from students and the teacher, indicate that this sequence of activities is highly engaging for students. They enjoy playing the roles of the advisors, creating artifacts to improve their advice, and doing research on themselves, such as the research they did on the utility of the different types of roles. We thus argue that there are important motivational ramifications of our approach to fostering young learners’ metacognitive development.
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A central aspect of our approach is that we provide students with a distributed model of expertise (Salomon, 1993), both in terms of its conception and its execution as a community of advisors, who each serve a different function. Every member of a group has an important role to play, which increases equity and participation (Borge, White, & Miller, 2005). In so doing, our approach provides students with models of how complex tasks can be accomplished: They require a variety of capabilities that must be employed in an organized and coordinated fashion with constant monitoring and reflection. Other features of our approach include enabling students to develop a theory of cognitive and metacognitive processes, internalize those processes, and learn how to use them in a variety of contexts. These features should have implications for transfer as well as for building feelings of self-efficacy. In our view, for students to develop confidence in their capabilities, they need to have a language for talking about them, evaluating them, and improving them as well as developing explicit cognitive models of how to employthese capabilities as theywork. Here we draw attention to the need for transparency of processes by developing a type of cognitive model that can be enacted. Self-efficacy is supported by knowing you have the knowledge and strategies for accomplishing different kinds of goals, and for how to apply them in a variety of contexts. Further, by introducing students to the idea that such capabilities can be improved and refined, and teaching them how to do this, students develop the important idea that anyone can learn and improve. You don’t have to be “born smart.” On the contrary, students see that their capabilities are subject to reflection and improvement (Dweck & Leggett, 1988). In our own research, this view is supported by the finding that reflective activities, such as self-assessment, significantly decrease the performance gap between low- and high-achieving students (White & Frederiksen, 1998, 2005). However, determining the impact of such learning environments and instructional activities on students’feelings of self-efficacy remains as a question for future research.
A VISION FOR THE FUTURE We think our approach to fostering students’ inquiry learning and metacognitive development, which is oriented around cognitive modeling, will have even more utility in the future. As technology advances, society will reach a point where humans work with software advisors and assistants throughout their school, work, and personal lives. If so, it will become even more valuable for students to learn how to select, learn from, improve, and utilize advisors. Furthermore, enabling advisors and students to speak a common language of metacognition will become even more crucial. We argue that the idea of the mind as a community is more than just a metaphor. Distributed models of expertise, such as that embodied by our communities of advisors of various
types, will become increasingly accurate and useful. Students will need such a theory of mind as their capabilities are enhanced by adding teams of software advisors and assistants. Furthermore, these advisors and assistants should not be mysterious black boxes. For the same reasons that we want students to develop metacognitive awareness and theories about their own minds, they need to be able to develop such knowledge about their extended minds. Thus, it is important for psychologists, educators, and computer scientists to work to create software advisors that not only reason in transparent ways that humans can understand, but that also serve to help humans develop the types of metalevel expertise that we have described. Then humans will be able to conceptualize and work with their technology-enhanced environments in new and productive ways.
ACKNOWLEDGMENTS This research was funded by the National Science Foundation (NSF Grant REC–0087583). The views expressed in this article are those of the authors and do not necessarily reflect those of the NSF. We thank the members of the ThinkerTools Research Group, past and present; particularly Marcela Borge, Eric Eslinger, Suzy Loper, and Todd Shimoda, who made major contributions to this research. We also thank our participating teachers, especially Tatiana Miller, who conducted this research in her classroom and contributed significantly to the ideas. Finally, we thank Allan Collins and Linda Shimoda for their numerous contributions to this research.
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