We describe research on data-drive refinement and evaluation of a probabilistic model of student learning for an educational game on number factorization. The model is to be used by an intelligent pedagogical agent to improve student learning during game play. An initial version of the model was designed based on teachers' advice and subjective parameter settings. Here we illustrate data-driven improvements to the model, and we report results on its accuracy.
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