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

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 of discrepancy sets and discovery of error classes from discrepancies of buggy PROLOG programs demonstrate the potential of the approach.