Learner Modeling Based on Bayesian Networks

The work presented in this chapter lies within Learner modeling in an adaptive ed‐ ucational system construed as a computational modeling of the learner. All actions of the learner in a learning situation on an adaptive hypermedia systems are not limited to valid or invalid actions (true and false), but they are a set of actions that characterize the learning path of his formation. Thus, we cannot represent the infor‐ mation from the system of each learner using relative data. It requires putting our work in a probabilistic context due to the changes in the learner model information during formation. We propose in this work to use Bayesian networks as a probabil‐ istic 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 our hypothesis, and can also promote reusing the modeling obtained through different systems and similar modeling situations.