All intelligent tutoring systems (ITSs) are predicated on some form of student modeling to guide tutor behavior. Decisions based on inferences about what a student knows and does not know can affect the presentation and pacing of problems, quality of feedback and instruction, and determination of when a student has completed some set of tutorial objectives. In this paper, we describe a view of student modeling that, in the course of implementing principles of cognitive diagnosis, takes

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... of concepts and tools developed in the areas of probability-based reasoning, educational assessment, and psychometrics in an attempt to develop a generalizable framework for student modeling within intelligent tutoring systems. Student models in an ITS can fulfill at least three functions. First, given a set of instructional options, a student model provides information suggesting which of the available choices is most appropriate for an individual (Ohlsson, 1987) . ITS's,