Date of Original Version
Abstract or Description
In well-defined domains there exist well-accepted criteria for detecting good and bad student solutions. Many ITS implement these criteria characterize solutions and to give immediate feedback. While this has been shown to promote learning, it is not always possible in ill-defined domains that typically lack well-accepted criteria. In this paper we report on the induction of classification rules for student solutions in an ill-defined domain. 1 We compare the viability of classifications using statistical measures with classification trees induced via C4.5 and Genetic Programming.