A FORMALIZATION OF STUDENT MODELING

Abstract

In this paper, we focus on student modeling within an Intelligent Tutoring System (ITS). Elaborating a student modeling system entails determining the formalism in which student knowledge will be represented, and the processes that will dynamically acquire and synthesize this knowledge. We describe three domain‐independent properties that this formalism and these processes must possess to build sound and accurate student models. First, because the student's knowledge evolves in time, the modeling system must be able to represent knowledge that issues from defeasible reasoning. Second, since students may have contradictions in mind, it also must deal with paraconsistent reasoning. Third, because the diagnosis of the student's cognitive state is not certain, the results of the cognitive diagnosis must be considered as hypotheses rather than certain facts. These hypotheses may have to be withdrawn if contradictory information is subsequently acquired. Therefore, the system must also be able to follow hypothetical reasoning. We show how an implementation based upon probabilistic logic can take into account both defeasible and paraconsistent student reasoning. We also point out where, when and how hypothetical reasoning mechanisms must intervene. We exemplify our results within the framework of Compounds, an ITS devoted to English compounding processes.