that were hypothesized to mimic human problem-solving strategies (Newell and Simon, 1972). The results of these studies led to new hypotheses and more fully elaborated theories.

More recently, researchers have used cognitive models to develop intelligent tutors. One of the more long-standing research efforts in this area is the work of John Anderson and his colleagues at Carnegie Mellon University in which the ACT (Adoptive Control of Thought) theory of learning and problem solving was used to build intelligent tutors in algebra, geometry, and LISP programming language. The ACT theory makes a distinction between factual or declarative knowledge and procedural knowledge. Declarative knowledge involves the acquisition of facts (the content of a theorem); procedural knowledge in the form of production rules relates to the development of cognitive skill (the ability to apply a theorem). The early stages of learning are dominated by declarative knowledge; the later stages by procedural knowledge. According to a recent review by Anderson et al. (1993), the 10-year effort has led to further understanding of human cognition as well as to an appreciation for how to implement the system in the classroom. One important finding was that the original conception of tutoring as a process of human emulation changed to the notion of a tutor as a learning environment in which helpful information can be provided and useful problems can be selected.

Another important line of research in cognitive science is modeling the knowledge structures and judgments of experts and novices and comparing the two as a basis for understanding the nature of expertise and for training novices to become experts. For example, Chi et al. (1981) have examined the differences in the knowledge structures and problem approaches of expert and novice physicists as a way to better understand how the acquisition of knowledge and rules changes problem-solving strategies. The schema, algorithms, and heuristics used by experts were explicated by using such methods as cognitive task analysis or think-aloud protocols (Newell and Simon, 1972). According to Glaser et al. (1991), several knowledge models representing various stages in moving from a novice to an expert would be useful in guiding the learning process.

Using cognitive task analysis, Lesgold and his colleagues have described various stages of expertise in electronic troubleshooting as a basis for developing dynamic assessments of learner competence. The resulting computer system, known as Sherlock, uses information on the stages of expertise to track a learner's performance, to diagnose strengths and weaknesses in both knowledge and process, and to provide corrective feedback (Lesgold et al., 1990; Lajoie and Lesgold, 1992).

Although cognitive researchers have made considerable progress in

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