Epistemological remediation in intelligent tutoring systems

This paper presents an approach to student's errors diagnosis in intelligent tutoring systems and to the remedial instruction to overcome those errors. Our contribution arises from two key components. Firstly, a diagnosis model, which based on the nature of the learner's input, as well as its exercise knowledge model., uses Bayesian induction (a posteriori maximization) to find the most probable causes of a failure Secondly a remedial instruction model which will be the focus of this paper. This model will use the epistemological nature of the faulty skill that was diagnosed.

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