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Abstract: Probabilistic and learned approaches to student modeling are attractive because of the uncertainty surrounding the student skills assessment and because of the need to automatize the process. Item to item structures readily lend themselves to probabilistic and fully learned models because they are solely composed of observable nodes, like answers to test questions. Their structure is also well grounded in the cognitive theory of knowledge spaces. We study the e#ectiveness of two Bayesian... (Update)
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BibTeX entry: (Update)
@misc{ michel-learned,
author = "Michel Desmarais Michel",
title = "Learned Student Models with Item to Item Knowledge Structures",
url = "citeseer.ist.psu.edu/764414.html" }
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