Modeling Novice‐to‐Expert Shifts in Problem‐Solving Strategy and Knowledge Organization

Abstract

This research presents a computer model called EUREKA that begins with novice‐like strategies and knowledge organizations for solving physics word problems and acquires features of knowledge organizations and basic approaches that characterize experts in this domain. EUREKA learns a highly interrelated network of problem‐type schemas with associated solution methodologies. Initially, superficial features of the problem statement form the basis for both the problem‐type schemas and the discriminating features that organize them in the P‐MOP (Problem Memory Organization Packet) network. As EUREKA solves more problems, the content of the schemas and the discriminating features change to reflect more fundamental physics principles. This changing network allows EUREKA to shift from a novicelike means‐ends strategy to a more expertlike “knowledge development” strategy in which the presence of abstract concepts are triggered by problem features. In this model, the strategy shift emerges as a natural consequence of the evolving expertlike organization of problem‐type schemos. EUREKA captures many of the descriptive models of novice expert differences, and also suggests a number of empirically testable assumptions regarding problem‐solving strategies and the representation of problem‐solving knowledge.

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