Recommending system for digital educational resources based on learning analysis

In this paper, we propose an online course recommendation system applicable to a distance-learning platform. (LMS) which assists and supports learners in knowledge tests. Most commercial LMS or open source as well as open source software do not include recommendation tools to guide learners during their learning process. Current online training devices, such as MOOCs, enlist thousands of learners, so tutors often feel powerless to build a synthetic representation of learner activity, to send recommendations, remarks to each learner and intervene in appropriate time. In an attempt to meet these needs, we propose an approach that aims to automatically analyze the learners' responses, which determine their level of knowledge. Basis on their answers, our system automatically offers personalized recommendations for the learners. We will then present the results of the implementation of the system developed as part of an experiment we conducted with 10,000 students enrolled on our distance-learning platform. The paper presents the results obtained and the perspectives for more research.