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

ion of Implicit Domain Concepts The fact that EUREKA indexes the enhanced problem representation means that its expert-level schemas are not limited to the problem’s initial descriptors. Instead, physics concepts and other inferences about object relations and features are eligible to become norms and indices. This is important for abstracting useful domain-specific concepts that are defined as a collection of features and relations in problem representations. For example, EURJXA’s enhanced knowledge about objects, their features, and their relation to other objects bears some resemblance to Novak’s (1977) notion of canonical physics objects, such as “pivots.” In Novak’s ISAAC system, the definition of a canonical object specified particular object features, the object’s relation to other objects, and perhaps features and relations of those other objects as well. In EUREKA, this kind of related information collects as a set of P-MOP norms and inference rules. Consider the information about particular object variables in P-MOPIS, listed in Table 1. As a package, this knowledge may approximate a canonical definition of a “pulley system.” Although the problem descriptions used as input to EUREKA already included “pulley system” as a relation, that simply reflects the level of granularity of knowledge in the textbook knowledge network. A more fine-grained representation of the objects, features, and relations involved with a pulley system could have been used in the initial problem description; the critical defining co-occurring object features and