Identifying and tracking learning styles in MOOCs: A neural networks approach

Learning styles identification using learners’ behavior and the actions they perform on a MOOC environment constitute in our opinion not just an interesting research issue but also an important solution to improve MOOC effectiveness. Indeed, providing learners with learning resources and activities that suit to their preferences and learning styles increases their satisfaction improve learning performances and save time (efficiency). In this paper, we propose an approach that uses neural networks to identify and track learners learning styles, then to provide them the appropriate resources, activities, etc. through adaptive recommendation system. The purpose of this paper is to examine the point of view of literature on MOOCs, learning styles and their use in MOOCs environment and our proposed solution to integrate an adaptive recommendation system with MOOC taking into accounts the plurality of participants’ learning styles.