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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, takesdoi:10.21236/ada280554 fatcat:2hwft63okjcl3bhcnx3pzbbyfy