A Learner Model Based on Bayesian Networks in Adaptive Educational Hypermedia Systems

Abstract

The work presented in this chapter lies within learner modeling in an adaptive educational system construed as a computational modeling of the learner. All actions of the learner in a learning situation on an adaptive hypermedia system are not limited to valid or invalid actions (true and false), but they are a set of actions that characterize the learning path of formation. Thus, one cannot represent the information from the system of each learner using relative data. It requires putting the work in a probabilistic context due to the changes in the learner model information during formation. In this chapter, the authors propose to use Bayesian networks as a probabilistic framework to resolve the issue of dynamic management and update of the learner model. The experiments and results presented in this work are arguments in favor of the hypothesis and can also promote reusing the modeling obtained through different systems and similar modeling situations.