Towards a Social Learning Analysis Using Learning Management System and Learning Experience to Predict Learners’ Success

Abstract

Online learning is an alternative to the traditional (face-to-face) educational system. Contrary to traditional teaching (classroom), distance learning is characterized by the lack of physical contact between instructors and students and also between students within a classroom. Unfortunately, some learners may fail or even stop learning online quite quickly. In addition, learners find it more difficult to learn, not because there is not enough content, but because there is too much and they cannot find what is useful and up-to-date. This study aims to prove the importance of using social and adaptive learning to overcome the problems that learners face in online learning platforms, we propose a model to determine the effects of using the two types of platforms in online learning, namely Learning Management System (LMS) and Learning Experience Platform (LXP) for two groups of learners in Moroccan higher education applying social learning analysis (SLA). Specifically, in this proposal we attempt to analyze data from the LMS and LXP platforms to build knowledge models about students and determine their learning styles based on their interactions on these platforms, improve their learning experience and predict the conditions that favor their progress and success in order to recreate the conditions for quality social learning and help learners find possible paths to success in the online learning platforms in Morocco.