An approach for the identification and tracking of learning styles in MOOCs

Abstract

This paper is devoted to describe a preliminary draft of our approach that aims to identify and track learners’ learning styles based on their behavior and actions they perform in a MOOC environment. Adaptation arises with intensity in MOOCs. Indeed, it has been proved that MOOCs can benefit from the advantages of learning styles as a way to provide an adaptive navigational guidance to learners. In this approach, we use neural networks for the identification and tracking of learner’s learning styles in MOOCs so as to increase learners’ engagement and satisfaction. The purpose of this paper is to examine the point of view of literature and solution to integrate an adaptive system in MOOC.