View article

Probabilistic and learned approaches to student modeling are attractive because of the uncertainty surrounding the student skills assessment and because of the need to automatize the process. Item to item structures readily lend themselves to probabilistic and fully learned models because they are solely composed of observable nodes, like answers to test questions. Their structure is also well grounded in the cognitive theory of knowledge spaces. We study the effectiveness of two Bayesian frameworks to learn item to item structures and to use the induced structures to predict item outcome from a subset of evidence. One approach, Partial Order Knowledge Structures (POKS), relies on a naive Bayes framework whereas the other is based on the Bayesian network (BN) learning and inference framework. Both approaches are assessed over their predictive ability and their computational efficiency in …