A machine learning approach to identify and track learning styles in MOOCs

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 during a MOOC then to provide them with personalized recommendations based on their learning styles. Massive Open Online Courses are attracting a debate in the research community about their influence in online education. Indeed, with their advent, we are assisting to a substantial expansion of online learning with new advantages such as: massiveness, openness, democratization of learning, etc. However, it raises particular issues related to the dropout rates and the heterogeneity of massive learner's cohorts. 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, the dropout issue and solution to integrate an adaptive recommendation system with MOOC.