Towards Analyzing the Online Learner's Behavior: An Expedition to Recommender System

Abstract

E-learning courses are gaining significance nowadays and are also an active area for research. COVID-19 pandemic has challenged education system across the globe and many institutions have switched to the online platforms for survival. Due to this, a lot many online courses are available and it is very difficult for a novice user to select suitable course as per one’s preference. Despite the great motivation for MOOC courses and rapid growth of online platforms, there has been tremendous drop out for MOOC courses. This paper focuses on analyzing user activities or user’s learning behavior in order to recommend suitable and unsuitable courses according to user preference. One of the key issues is nowadays is the increasing dropout rate of MOOC learners. The completion rate of most of the online courses is very less as compared to the number of enrolled participants. After creating and analyzing the user learners’ profile, the recommender system recommends suitable courses to the learners so that dropout cases will decrease and the user will be able to choose appropriate courses as per their preferences. Sometimes under social influence or lack of awareness, learners opt those courses that are not appropriate for them. As each learner knowledge level is diverse so customization is required in order to improve online learning experience. In this paper, authors have described various challenges of the online learners, suggested how a recommender system should be advanced and different types of recommender systems.