Adaptive E-learning: Adaptation of Content According to the Continuous Evolution of the Learner During his Training

Abstract

Higher education has always been based on a traditional education system. All learners have to attend all training courses without taking into consideration human and logistical constraints. This situation leads to major problems of massification, which unfortunately subsequently lead to problems of demotivation or even abandonment of a large number of students. A solution to these problems resides in introduction of distance learning. The implementation of online courses, such as MOOC (Massive Open Online Courses), and the emergence of educational platforms such as LMS (Learning Management System) or LCMS (Learning Content Management System), have made it possible to introduce the notion of e-learning into Higher education. Nevertheless, if e-learning has left the Stone Age elsewhere, it is still an emerging field in some countries as Morocco where it is far from having reached maturity. Like any new system or proposal, e-learning has its own detractors who need to be more reassured on certain aspects. In this article, we deal with some major issues related to e-learning platforms, which offer pre-established pedagogical content without really taking into account the particularity or evolution of each learner during the training path. We will therefore talk about a customised or adaptive e-learning. By combining UBA (User Behavior Analytics) and AI (Artificial Intelligence), we will propose during this article an LBA (Learner Behavior Analytics) model based on an a system called SBAN (Score and Behavior ANalytics).