Towards A New Adaptive E-learning System Based On Learner's Motivation And Machine Learning

With the increase of the internet, E-learning adaptation and recommnadation system has become a new trend in learning. In contrast to traditional learning (all to all sytem), adaptive e-learning systems provides a special environment in which learners are in control of their learning process. Yet finding the most appropriate learning path and content for a given learner according to his or her profil is a very important issue in achieving the learning objectives. In this context several methods have been proposed with different level of performance in terms of precision and quality of adaptation. In this paper, a new adaptation approach based on learner's motivation level is proposed in E-learning environement combining Content-Based Filtering technique, and machine learning algorithms. The proposed approach, is aimed to built a customized learning path while identifying and selectiong the most appropriate learning objects for learners during the learning activity. Additionally, a few experiments were run to gauge how well our contribution worked. The results obtained by the proposed strategy demonstrate that considering the learner's motivation level enhances and reinforces the level of learning among students during the learning activity.