A WildCAT Based Observable Bayesian Student Model
S. BOULEHOUACHE Selma Ouareth Ramdane Maamri
AbstractThe Student Model is dedicated to personalize and to adapt the learning. With pedagogical strategy self-switching, the monitoring of the student model is the cornerstone of pedagogical strategy adapting. To efficiently achieve the monitoring operation, we propose a fine grained WildCAT based Observable Bayesian Student Model. On one side, it represents how the user relates to the concepts of the knowledge structure using the pedagogical component. On the other side, it integrates concept level sensors that results in an Observable Networks’ Sensors. This permits to ensure the collect of the instant student knowledge level. In addition, it uses a publish/subscribe communication model to notify the Student Cognitive changes to the monitoring component. On this side, the Monitoring Component subscribe as a receiver of appropriate cognitive changes. To experiment the likelihood and the usefulness of this model, a framework is constructed using WildCAT on a Student Cognitive Level.