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Combining the Overlay Model and Bayesian Networks to Determine Learning Styles in AHES
Abstract
First of all, it is important to note that the work presented here lies within the modeling part of the learner in an adaptive educational system construed as computational modeling of the learner. Modeling the learner in adaptive systems involves different information such as knowledge of the domain, the performance of the learning goals, background, learning styles, etc. Although there are several methods to manage the learner model, like the stereotype model or learner profiles, they do not handle the uncertainty in the dynamic modeling of the learner. The main hypothesis of this work is to show the link between the structure of the learner model and especially the characteristics of a learning profile and the learning style of a learning situation. This chapter shows how the combination of these two approaches to learner modeling can address the dynamic aspect of the problem in the modeling of the learner.
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