Designing a Dynamic Bayesian Network for Modeling Students' Learning Styles

Abstract

When using Learning Object Repositories, it is interesting to have mechanisms to select the more adequate objects for each student. For this kind of adaptation, it is important to have sound models to estimate the relevant features. In this paper we present a student model to account for Learning Styles, based on the model defined by Felder and Sylverman and implemented using Dynamic Bayesian Networks. The model is initialized according to the results obtained by the student in the Index of Learning Styles Questionnaire, and then fine-tuned during the course of the interaction using the bayesian model, The model is then used to classify objects in the repository as appropriate or not for a particular student.