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Using multiple Dirichlet distributions to improve parameter plausibility
2010
Educational Data Mining
Predictive accuracy and parameter plausibility are two major desired aspects for a student modeling approach. Knowledge tracing, the most commonly used approach, suffers from local maxima and multiple global maxima. Prior work has shown that using Dirichlet priors improves model parameter plausibility. However, the assumption that all knowledge components are from a single Dirichlet distribution is questionable. To address this problem, this paper presents an approach to integrate multiple
dblp:conf/edm/GongBH10
fatcat:5bcdmdvgmvhonh6tysglfapoua