Evaluating the Feasibility of Learning Student Models from Data

Anders Jonnson, Jeff Johns, Hasmik Mehranian, Ivon Arroyo, Beverly Woolf, Andrew Barto, Donald Fisher, and Sridhar Mahadevan

Recent work on intelligent tutoring systems has used Bayesian networks to model students’ acquisition of skills. In many cases, researchers have hand-coded the parameters of the networks, arguing that the conditional probabilities of models containing hidden variables are too difficult to learn from data. We present a machine learning approach that uses Expectation-Maximization to learn the parameters of a dynamic Bayesian network with hidden variables. We test our methodology on data that was simulated using a state-based model of skill acquisition. Results indicate that it is possible to learn the parameters of hidden variables given enough sequential data of training sessions on similar problems.

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