Automatic detection of learning styles based on dynamic Bayesian network in adaptive e-learning system

Abstract

A large number of studies attest that learning is facilitated if the teaching strategies are in accordance with the students learning styles (LS), making the learning process more effective and considerably improving student's performances. But, traditional approaches for detection of LS are inefficient. This work determines the current preferences through dynamic Bayesian network that represent the matches between LS and teaching strategies in order to determine how much a given strategy is interesting to a student. The LS theory that supports this approach is the LS model proposed by Felder-Silverman's learning styles model (FSLSM). Our approach gradually and constantly adjusts the student model, taking into account students' performances, student's effort, student's intensity, student's resistance and student's attention. Promising results were obtained from experiments, and some of them are discussed in this paper.