Evaluating Simplicial Mixtures of Markov Chains for Modeling Student Metacognitive Strategies
Modeling and discovery of the strategies that students use, both cognitive and metacognitive, is important for building accurate models of student knowledge and learning. We present a simulation study to examine whether simplicial mixtures of Markov chains (SM-MC) can be used to model student metacognitive strategies. We find that SM-MC models cannot be estimated on the moderately sized data sets common in education, and must be adapted to be useful for strategy modeling.
KeywordsTransition Matrice Markov Chain Model Intelligent Tutoring System Metacognitive Strategy Cognitive Tutor
This research has been supported in part by a postdoctoral award from the US Department of Education, Office of Education, Institute of Education Sciences to Ilya Goldin, award #R305B110003.
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