A Student Model Building Method based on Formalization of Nonmonotonicity

Abstract

An intelligent tutoring system should have a student model which describes the student's understanding in order to realize adaptive tutoring. We have developed a basic architecture of HSMIS, which is an assumption-basd inductive student model inference engine. Although the algorithm of HSMIS is domain independent and is based on a complete logical foundation, the behavior of the system lacks flexibility and educational appropriateness. In this paper, we investigate which mechanisms and what knowledge is required to make HSMIS flexible and powerful. To this end, we developed the following two mechanisms : 1. Flexible decision making on the usage of information obtained from student's problem solving process. 2. Sophisticated model inference mechanism for coping with various assumptions. The control mechanism and knowledge for controlling student model inference is described in detail.