Models of Competence in Solving Physics Problems*

Larkin, Jill H.; McDermott, John; Simon, Dorothea P.; Simon, Herbert A.

doi:10.1207/s15516709cog0404_1

Cited by 411 publications

(138 citation statements)

References 5 publications

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“…Research in domains such as mathematics (Schoenfeld & Herrmann, 1982) and physics (Chi, Feltovich, & Glaser, 1981) indicates that experts focus on the deep structures of problems (e.g., principles and concepts that could be used to solve the problems) to decide whether or not two problems would be solved similarly. These findings suggest that when attempting to solve a problem, experts begin by considering what principle or principles apply most appropriately to the situation, and then decide on a strategy or procedure that will be used to instantiate that principle, or those principles (Larkin, 1981(Larkin, , 1983Larkin, McDermott, D. P. Simon, & H. A. Simon, 1980; D. P. Simon & H. A. Simon, 1978).…”

mentioning

confidence: 99%

The relation between problem categorization and problem solving among experts and novices

1989

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These investigations were conducted to examine the relationship between problem-solving ability and the criteria used to decide that two classical mechanics problems would be solved similarly. We began by comparing experts and novices on a similarity judgment task and found that the experts predominantly relied on the problems' deep structures in deciding on similarity of solution, although the presence of surface-feature similarity had a clear adverse effect on performance. The novices relied predominantly on surface features, but were capable of using the problems' deep structures under certain conditions. In a second experiment, we compared groups of novices, at the same level of experience, who tended to employ different types of reasoning in making similarity judgments. Compared to novices who relied predominantly on surface features, novices who made greater use of principles tended to categorize problems similarly to how experts categorized them, as well as score higher in problem solving. These results suggest that principles play a fundamental role in the organization of conceptual and procedural knowledge for good problem solvers at all levels.

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mentioning

confidence: 99%

The relation between problem categorization and problem solving among experts and novices

1989

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“…For example, physics experts initiate the problem-solving process by representing a situation on the basis of physics principles and relevant available data (Larkin, McDermott, Simon, & Simon (1980a);Larkin, McDermott, Simon, & Simon (1980b). They use theoretically-driven strategies and conceptual schemas to integrate both the provided relevant information and the abstract relationships between problem elements (Dhillon, 1998;Larkin, 1985).…”

Section: The Role Of Strategy In Expert Cognitionmentioning

confidence: 99%

“…They classify problems on the basis of surface-level details that are not relevant to the operational principles of the task (Larkin, McDermott, Simon, & Simon, 1980a); Larkin, McDermott, Simon, & Simon (1980b). Novices then determine which equations will yield an answer that responds appropriately to the presented prompt.…”

Section: The Role Of Strategy In Expert Cognitionmentioning

confidence: 99%

The Implications of Research on Expertise for Curriculum and Pedagogy

Feldon

2006

Educ Psychol Rev

123

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Instruction on problem solving in particular domains typically relies on explanations from experts about their strategies. However, research indicates that such self-reports often are incomplete or inaccurate (e.g., Chao & Salvendy, 1994;Cooke & Breedin, 1994). This article evaluates research on experts' cognition, the accuracy of experts' self-reports, and the efficacy of instruction based on experts' self-reports. Analysis of this evidence indicates that experts' free recall of strategies introduces errors and omissions into instructional materials that hinder student success. In contrast, when experts engage in structured knowledge elicitation techniques (e.g., cognitive task analysis), the resultant instruction is more effective. Based on these findings, the article provides a theoretical explanation of experts' self-report errors and discusses implications for the continued improvement of instructional design processes.

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“…Future work on this topic will focus on the organization of knowledge in memory as this is one difference between experts and novices in many domains including chess (Chase and Simon 1973), physics (Larkin et al 1980), game playing (Reitman 1976) and electrical diagrams (Egan and Schwartz 1979). Organization of information in memory is likely to be a difference between experts and novices in the domain of design as well.…”

Section: Iteration Evaluationmentioning

confidence: 99%

“…This learning process is gradual and has been documented in a number of other domains (Chase and Simon 1973, Larkin et al 1980, Reitman 1976, Richman et al 1995. The reason for this improvement in performance with experience is due in part to the ability of the designer to transfer knowledge learned in one problem to the problem that is currently being pursued.…”

Section: Introductionmentioning

confidence: 99%

Learning from design experience in an agent-based design system

2004

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Abstract.A simple learning mechanism was added to an agent-based computational design system to see if it could then transfer knowledge across problems. An existing system, A-Design, was enhanced by giving it the ability to store useful design knowledge in a memory store so that this knowledge could be used in new design problems. Three electromechanical design problems were used to evaluate this new learning mechanism, and results indicate that this simple learning mechanism is successful at transferring design knowledge to new problems with some limitations.

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Models of Competence in Solving Physics Problems*

Cited by 411 publications

References 5 publications

The relation between problem categorization and problem solving among experts and novices

The relation between problem categorization and problem solving among experts and novices

The Implications of Research on Expertise for Curriculum and Pedagogy

Learning from design experience in an agent-based design system

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