Estimating Prerequisite Structure From Noisy Data
نویسنده
چکیده
There has been a long-standing interest in how to estimate the prerequisite structure among a set of skills. This prerequisite structure is important for intelligent tutoring systems and the educational data mining community, because it has important implications for student modeling, and for automatically constructing teaching policies. Here we present preliminary work towards inferring skill prerequisite structure given a set of noisy observations of student knowledge. We compare models using likelihood calculations and provide experimental results on a set of Algebra skills.
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