Collins, Brown and Newman proposed the cognitive apprenticeship method of learning in 1989. It is based on a "learning by doing" approach. Central to this approach is the assumption that students can learn cognitive tasks in a similar way to crafts learned from watching an expert in their field, by asking questions and gradually practising the task. Cognitive apprenticeship provides authentic context for learning and encourages deep understanding of the knowledge by engaging students in practice through activity and social interaction.

Advances in cognitive psychology are continuing to influence the design and implementation of teaching technologies. In particular over the last several years, researchers have compared the often remarkable learning accomplishments in natural settings (home, work, and play) with the disappointing learning outcomes accruing from formal school settings (Rogoff & Lave, 1984; Rogoff, 1990). What is needed, according to Resnick (1989), is more "bridging" devices or technologies that bring realistic contexts and problems into the classroom.

The term cognitive apprenticeship was coined by Collins et al. (1989), who proposed that contemporary classroom instructional methods be combined with the concept of apprenticeship. In their landmark study, the age-old apprenticeship learning principles (modeling, coaching, fading) of on-the-job training were combined with the modern pedagogical practice of engaging students with problems in the context of real-world experiences. Their classroom methodology incorporated contextual learning, which is a natural element of apprenticeship that embeds practical application of classroom theory. Other researchers have identified cognitive apprenticeship instruction as a viable means of modernizing technical education (Brandt, Farmer, & Buckmaster, 1993; Raizen, 1989; Wilson & Cole, 1991). Differences between traditional apprenticeship and cognitive apprenticeship have been defined by Collins et al).

"Cognitive apprenticeship, as we envision it, differs from traditional apprenticeship in that the tasks and problems are chosen to illustrate the power of certain techniques or methods, to give students practice in applying these methods in diverse settings, and to increase the complexity of tasks slowly, so that component skills and models can be integrated" (p. 459).

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The Collins-Brown model of cognitive apprenticeship incorporates the following instructional strategies or components.

1. Content: Teach tacit, heuristic knowledge as well as textbook knowledge. Collins et al. (1989) refer to four kinds of knowledge:

--Domain knowledge is the conceptual, factual, and procedural knowledge typically found in textbooks and other instructional materials. This knowledge is important, but often is insufficient to enable students to approach and solve problems independently.

--Heuristic strategies are "tricks of the trade" or "rules of thumb" that help people narrow solution paths while solving a problem. Experts usually pick up heuristic knowledge indirectly through repeated problem-solving practice; slower learners usually fail to acquire this subtle knowledge and never develop competence. There is evidence to believe, however, that at least some heuristic knowledge can be made explicit and represented in a teachable form (Chi, Glaser, & Farr, 1988).

--Control strategies are required for students to monitor and regulate their problem-solving activity. Control strategies have monitoring, diagnostic, and remedial components; this kind of knowledge is often termed metacognition (Flavell, 1979).

--Learning strategies are strategies for learning; they may be domain, heuristic, or control strategies. Inquiry teaching to some extent directly models expert learning strategies (Collins & Stevens, 1983).

2. Situated learning: Teach knowledge and skills in contexts that reflect the way the knowledge will be useful in real life. Brown, Collins, and Duguid (1989) argue for placing all instruction within "authentic" contexts that mirror real-life problem-solving situations. Collins (1991) is less forceful, moving away from real-life requirements and toward problem-solving situations: For teaching math skills, situated learning could encompass settings "ranging from running a bank or shopping in a grocery store to inventing new theorems or finding new proofs. That is, situated learning can incorporate situations from everyday life to the most theoretical endeavors" (Collins, 1991, p. 122).

Collins cites several benefits for placing instruction within problem-solving contexts:

--Learners learn to apply their knowledge under appropriate conditions.

--Problem-solving situations foster invention and creativity.

--Learners come to see the implications of new knowledge. A common problem inherent in classroom learning is the question of relevance: "How does this relate to my life and goals?" When knowledge is acquired in the context of solving a meaningful problem, the question of relevance is at least partly answered.

--Knowledge is stored in ways that make it accessible when solving problems. People tend to retrieve knowledge more easily when they return to the setting of its acquisition. Knowledge learned while solving problems gets encoded in a way that can be accessed again in similar problem-solving situations.

3. Modeling and explaining: Show how a process unfolds and tell reasons why it happens that way. Collins (1991) cites two kinds of modeling: modeling of processes observed in the world and modeling of expert performance, including covert cognitive processes. Computers can be used to aid in the modeling of these processes. Collins stresses the importance of integrating both the demonstration and the explanation during instruction. Learners need access to explanations as they observe details of the modeled performance. Computers are particularly good at modeling covert processes that otherwise would be difficult to observe. Collins suggests that truly modeling competent performance, including the false starts, dead ends, and backup strategies, can help learners more quickly adopt the tacit forms of knowledge alluded to above in the section on content. Teachers in this way are seen as "intelligent novices" (Bransford et al., 1988). By seeing both process modeling and accompanying explanations, students can develop "conditionalized" knowledge, that is, knowledge about when and where knowledge should be used to solve a variety of problems.

4. Coaching: Observe students as they try to complete tasks and provide hints and helps when needed. Intelligent tutoring systems sometimes embody sophisticated coaching systems that model the learner's progress and provide hints and support as practice activities increase in difficulty. The same principles of coaching can be implemented in a variety of settings. Bransford and Vye (1989) identify several characteristics of effective coaches:

--Coaches need to monitor learners' performance to prevent their getting too far off base, but leaving enough room to allow for a real sense of exploration and problem solving.

--Coaches help learners reflect on their performance and compare it to others'.

--Coaches use problem-solving exercises to assess learners' knowledge states. Misconceptions and buggy strategies can be identified in the context of solving problems.

--Coaches use problem-solving exercises to create the "teachable moment."

5. Articulation: Have students think about their actions and give reasons for their decisions and strategies, thus making their tacit knowledge more explicit. Think-aloud protocols are one example of articulation. Collins (1991) cites the benefits of added insight and the ability to compare knowledge across contexts. If learners' tacit knowledge is brought to light, that knowledge can be recruited to solve other problems.

6. Reflection: Have students look back over their efforts to complete a task and analyze their own performance. Reflection is like articulation, except it is pointed backwards to past tasks. Analyzing past performance efforts can also influence strategic goal-setting and intentional learning (Bereiter & Scardamalia, 1989). Collins and Brown (1988) suggest four kinds or levels of reflection:

--Imitation occurs when a batting coach demonstrates a proper swing, contrasting it with your swing;

--Replay occurs when the coach videotapes your swing and plays it back, critiquing and comparing it to the swing of an expert;

--Abstracted replay might occur by tracing an expert's movements of key body parts such as elbows, wrists, hips, and knees, and comparing those movement to your movements;

--Spatial reification would take the tracings of body parts and plot them moving through space.

The latter forms of reflection seem to rely on technologies--video or computer-- for feasible implementation.

7. Exploration: Encourage students to try out different strategies and hypotheses and observe their effects. Collins (1991) claims that through exploration, students learn how to set achievable goals and to manage the pursuit of those goals. They learn to set and try out hypotheses, and to seek knowledge independently. Real-world exploration is always an attractive option; however, constraints of cost, time, and safety sometimes prohibit instruction in realistic settings. Simulations are one way to allow exploration; hypermedia structures also allow exploration of information.

8. Sequence: Present instruction in an ordering from simple to complex, with increasing diversity, and global before local skills.

--Increasing complexity. Collins et al. (1989) point to two methods for helping learners deal with increasing complexity. First, instruction should take steps to control the complexity of assigned tasks. They cite Lave's study of tailoring apprenticeships: apprentices first learn to sew drawers, which have straight lines, few pieces of material, and no special features like zippers or pockets. They progress to more complex garments over a period of time. The second method for controlling complexity is through scaffolding. Here the cases or content remains complex, but the instructor provides the needed scaffolding for initial performances and gradually fades that support.

--Increasing diversity refers to the variety in examples and practice contexts.

--Global before local skills refers to helping learners acquire a mental model of the problem space at very early stages of learning. Even though learners are not engaged in full problem solving, through modeling and helping on parts of the task (scaffolding), they can understand the goals of the activity and the way various strategies relate to the problem's solution. Once they have a clear "conceptual map" of the activity, they can proceed to developing specific skills.

The three teaching models presented below illustrate various features of the cognitive apprenticeship model. The first two are computer-based environments: Sherlock and goal-based scenarios. The third model is the problem-based learning environment developed by medical educators at the University of Illinois. All three models build instruction around problems or cases that are faithful to real-life situations, within which learners learn the details of a subject matter.

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Scardamalia, Bereiter, McLean, Swallow, and Woodruff (1989) observe:

There has been a history of attempts in computer-assisted instruction to give students more autonomy or more control over the course of instruction. Usually these attempts presupposed a well-developed repertoire of learning strategies, skills, and goals, without providing means to foster them. (p. 51)

Scardamalia and Bereiter envision a computer-based learning environment wherein students can learn and exercise these metacognitive skills, giving the name computer-supported intentional learning environments to "environments that foster rather than presuppose the ability of students to exert intentional control over their own learning..." (Scardamalia et al., 1989, p. 52). In a series of studies, Scardamalia & Bereiter (1992) found that children were capable of generating impressive higher-order questions about a new subject, based upon their interest and background knowledge. These questions could then be used to guide students' research and exploration of the topic. Intentional learning environments are designed to support the high-level, knowledge-generating activity resulting from this question-asking process.

The technology allows students to apply theories to real life situations through microworlds, networks, databases, graphic packages, and text editors. Benefits: 1. Students learn situations to which theories can apply. 2. Students learn how to apply the knowledge they learn. 3. When students apply theories a situation, how to use the theory in other situations becomes more obvious. 4. Theories stored in context of situations are more useful than mere memorized words of the theory.

The computer lets the student see things, which were not possible through human instruction or pictured/described in text. Also, the computer offers the advantage of letting the student "see" what is being explained while the computer "talks". In other words, the student does not have to read text and then look at a picture to put the two together, but through the technology available - the student can do both simultaneously. Benefits: 1. The student can pose a problem for the expert to solve. 2. Combining experimentation and theory. 3. Making the entire process visible.

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In the Final Analysis: How Effective is Cognitive Apprenticeship? We do not yet know, especially if the question is whether cognitive apprenticeship is effective in routine, as opposed to hothouse, learning situations. However, the ideas are unusually well grounded. Cognitive apprenticeship strategies build on traditional apprenticeships, a tested, cross-cultural strategy for effectively acquiring visually observable skills. They also build on and incorporate the ideas and findings of a community of serious thinkers and researchers, from John Dewey to today's cognitive scientists. However, there are very few learning situations that reflect cognitive apprenticeship principles. Extending the ones that exist and creating new ones requires dealing with regulatory, institutional, curricular, pedagogic, assessment, and professional training issues. The model itself will change as we gain experience with it in the bruising real world of teaching and learning.

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European Congress of the Internet in Medicine

A Cognitive Apprenticeship Approach to Teaching Medicine on the World Wide Web

http://www.pavilion.co.uk/mednet/me5.htm

Math Forum: Learning and Mathematics: Cognitive Apprenticeship – Colins, et al.

http://forum.swarthmore.edu/~sarah/Discussion.Sessions/Collins.html

Cognitive Teaching Models

Brent G. Wilson (University of Colorado at Denver), Peggy Cole (Arapahoe Community College)
http://www.cudenver.edu/~bwilson/hndbkch.html

Effectiveness of Cognitive Apprenticeship Instructional Methods in College Automotive Technology Classrooms

Joseph R. Cash, Michael B. Behrmann, Ronald W. Stadt (Southern Illinois University at Carbondale)

http://borg.lib.vt.edu/ejournals/JITE/v34n2/Cash.html

Collins, A. (1991): Cognitive Apprenticeship and Instructional Technology

Summary by Laconya Ruby (Educational Technology, Winter 1994)

http://www.cc.gatech.edu/aimosaic/OLD/education/courses/cs8113R/presentations/cog-app.html

Designing Effective Learning Environments: Cognitive Apprenticeship Models

Sue E. Berryman

http://www.cudenver.edu/~mryder/itc_data/idmodels.html

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