Abstract
This paper describes a framework to infer a student's misconception from observed errors during problem-solving processes. A human teacher can generate hypotheses about reasons for an error by observing a student's problem-solving process. The teacher is also able to identify a student's misconception during the process of verifying these hypotheses. Furthermore, by using these hypotheses, the teacher can generate new tasks to evaluate the student's understanding level. In this way, appropriate instructions based on the student's knowledge structure can be provided. To accomplish such a behavior within an intelligent tutoring system (ITS), the authors have defined a domain model and applied hypothesis-based reasoning to diagnose the student model. When the system finds an error in a student's problem-solving process, it attempts to generate hypotheses which explain that error in terms of the domain model.
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