Implications of cognitive studies for teaching physics

The purpose of this paper is to review from an instructional-design (ID) point of view nine teaching programs developed by cognitive psychologists over the last ten years. Among these models, Collins' cognitive apprenticeship model has the most explicit prescriptions for instructional design. The paper analyzes the cognitive apprenticeship model, then uses components of the model as an organizing framework for understanding the remaining models. Differences in approach are noted between traditional ID prescriptions and the cognitive teaching models. Surprisingly, we were unable to identify common design strategies common to all of the model programs. Key differences among programs included: (1) problem solving versus skill orientation, (2) detailed versus broad cognitive task analysis, (3) learner versus system control, and (4) error-restricted versus errordriven instruction. The paper concludes by arguing for the utility of continuing dialogue between cognitive psychologists and instructional designers. The field of instructional design (ID) emerged more than 30 years ago as psychologists and educators searched for effective means of planning and producing instructional systems (Merrill, Kowallis, & Wilson, 1981; Reiser, 1987). Since that time, instructional designers have became more clearly differentiated from instructional psychologists working within a cognitivist tradition (Glaser, 1982; Glaser & Bassok, 1989; Resnick, 1981). ID theorists tend to place priority on developing explicit prescriptions and models for designing instruction while instructional psychologists focus on understanding the learning processes in instructional settings. Of course, the distinction between designers and psychologists is never clear-cut. Over the years, many ID theorists have explored learning processes, just as many psychologists have put considerable energy into the design and implementation of experimental instructional programs. However, because the two fields support different literature and theory bases, communication is often lacking. Cognitive psychologists have tended to overlook contributions from the ID literature, and likewise much design work of psychologists has gone unnoticed by ID theorists. (A happy exception to this trend is the pointed dialogue on constructivism in the May and September issues of Educational Technology magazine.) Collins, Brown, and colleagues (e.g., Collins, 1991; Collins, Brown, & Newman, 1989) have developed an instructional model derived from the metaphor of the apprentice working under the master craftsperson in traditional societies, and from the way people seem to learn in everyday informal environments (Rogoff & Lave, 1984). They have called their model cognitive apprenticeships, and have identified a list of features found in "ideal" learning environments. Instructional strategies, according to the Collins-Brown model, would include modeling, coaching, scaffolding and fading, reflection, and exploration. Additional strategies are offered for representing content, for sequencing, and for maximizing benefits from social interaction. Of course, many of Collins' recommended strategies resemble strategies found in the ID literature (e.g., Reigeluth, 1983a). Clearly both fields could benefit from improved communication concerning research findings and lessons learned from practical tryout. With that goal in mind, the purpose of this paper is to do an ID review of programs and strategies developed by instructional psychologists, using the cognitive apprenticeship model as a conceptual framework. Several teaching systems employing cognitive apprenticeship ideals are described. The resultant review should prove valuable in two ways: cognitive psychologists should be able to make a better correspondence between their models and current ID theory, hopefully seeing areas needing improvement, and ID theorists also should be able to see correspondences and differences, which may lead to revision or expansion of our current models. The Need for Cognitive Apprenticeships The cognitive apprenticeship model rests on a somewhat romantic conception of the "ideal" apprenticeship as a method of becoming a master in a complex domain (Brown, Collins, & Duguid, 1989). In contrast to the classroom context, which tends to remove knowledge from its sphere of use, Collins and colleagues recommend establishing settings where worthwhile problems can be worked with and solved. The need for a problem-solving orientation to education is apparent from the difficulty schools are having in achieving substantial learning outcomes (Resnick, 1989). Another way to think about the concept of apprenticeship is Gott's (1988a) notion of the "lost apprenticeship," a growing problem in industrial and military settings. She noted the effects of the increased complexity and automation of production systems. First, the need is growing for high levels of expertise in supervising and using automated work systems; correspondingly, the need for entry levels of expertise is declining. Workers on the job are more and more expected to be flexible problem solvers; human intervention is often most needed at points of breakdown or malfunction. At these points, the expert is called in. Experts, however narrow the domain, do more than apply canned job aids or troubleshooting algorithms; rather, they have internalized considerable knowledge which they can use to flexibly solve problems in real time (Gott, 1988b). Gott's second observation relates to training opportunities. Now, at a time when more problem-solving expertise is needed due to the complexity of systems, fewer on-the-job training opportunities exist for entry-level workers. There is often little or no chance for beginning workers to acclimatize themselves to the job, and workers very quickly are expected to perform like seasoned professionals. True apprenticeship experiences are becoming relatively rare. Gott calls this dilemmamore complex job requirements with less time on the job to learn-the "lost" apprenticeship, and argues for the critical need for cognitive apprenticeships and simulation-type training to help workers develop greater problem-solving expertise. A Brief Review of ID Models It is assumed that readers will have some prior knowledge of ID models and theories; however, we offer a short overview to allow a clear contrast with certain cognitive approaches. ID models come in two generic varieties: procedural models for systems design (e.g., Andrews and Goodson, 1980) and conceptual models that incorporate specific instructional strategies for teaching defined content (Reigeluth, 1983a, 1987). The procedural models often are represented as flowcharts reflecting a series of project phases, progressing from needs and problems analyses to product implementation and maintenance. Procedural ID models depend less on learning theory and more on systems theory and project management methodologies (Branson & Grow, 1987). Of greater interest for our purposes are the instructional-strategy models. All such models are based on Robert Gagné's conditions-of-learning paradigm (Gagné, 1966), which in its time was a significant departure from the Skinnerian operant conditioning paradigm dominant among American psychologists. The conditions-of-learning paradigm posits that a graded hierarchy of learning outcomes exists, and for each desired outcome, a set of conditions exists that leads to learning. Instructional design is a matter of clarifying intended learning outcomes, then matching up appropriate instructional strategies. The designer writes behaviorally specific learning objectives, classifies those objectives according to a taxonomy of learning types, then arranges the instructional conditions to fit the current instructional prescriptions. In this way, designers can design instruction to successfully teach a rule, a psychomotor skill, an attitude, or piece of verbal information. A related idea within the conditions-of-learning paradigm claims that sequencing of instruction should be based on a hierarchical progression from simple to complex learning outcomes. Gagné developed a technique of constructing learning hierarchies for analyzing skills: A skill is rationally decomposed into parts and sub-parts; then instruction is ordered from simple subskills to the complete skill. Elaboration theory uses content structure (concept, procedure, or principle) as the basis for organizing and sequencing instruction (Reigeluth, Merrill, Wilson, & Spiller, 1980). Both methods depend on task analysis to break down the goals of instruction, then on a method of sequencing proceeding from simple to gradually more complex and complete tasks. Some of the teaching models being offered by cognitive researchers bear strong resemblance to traditional ID models. Larkin and Chabay (1989), for example, offer design guidelines for the teaching of science in the schools (pp. 160-163): 1. Develop a detailed description of the processes the learner needs to acquire. 2. Systematically address all knowledge included in the description of process. 3. Let most instruction occur through active work on tasks. 4. Give feedback on specific tasks as soon as possible after an error is made. 5. Once is not enough. Let students encounter each knowledge unit several times. 6. Limit demands on students' attention. By any standard, these design guidelines are very close to the prescriptions found in component display theory, elaboration theory, and Gagné's instructional-design theory. The strong correspondence can be seen as good news for ID theories: Many current cognitive researchers seem to agree on some fundamentals of design that also form the backbone of ID models. On the other hand, other cognitive teaching models emphasize design elements that traditional ID models historically have under-emphasized, such as learnerinitiated inquiry and exploration, social "scaffolding," cooperative learning methods, and empathi