Learning How Students Learn with Bayes Nets

Abstract

This extended abstract summarizes an exploration of how computational techniques may help educational experts identify fine-grained student models. In particular, we look for methods that help us learn how students learn composite concepts. We employ Bayesian networks for the representation of student models, and cast the problem as an instance of learning the hidden substructures of Bayesian networks. The problem is challenging because we do not have direct access to students’ competence in concepts, though we can observe students’ responses to test items that have only indirect and probabilistic relationships with the competence levels. We apply mutual information and backpropagation neural networks for this learning problem, and experimental results indicate that computational techniques can be helpful in guessing the hidden knowledge structures under some circumstances.