Discovering Error Classes from Discrepancies in Novice Behaviors Via Multistrategy Conceptual Clustering

Authors:
Raymund Sison;Masayuki Numao;Masamichi Shimura
Affiliations:
Department of Computer Science, Tokyo Institute of Technology, Japan;Department of Computer Science, Tokyo Institute of Technology, Japan;Department of Industrial Administration, Science University of Tokyo, Japan
Venue:
User Modeling and User-Adapted Interaction
Year:
1998

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Abstract

The automatic discovery of classes of errors that represent misconceptions and other knowledge errors underlying discrepancies in novice behavior is not a trivial task. A novel approach to this problem is described, in which relationships among behavioral discrepancies are analyzed and inductively generalized via an unsupervised, incremental, relational multistrategy conceptual clustering method that takes into account similarities as well as causalities in the data. Performance results on the classification ofdiscrepancy sets and discovery of error classes from discrepancies of buggy PROLOG programs demonstrate the potential of the approach.