A Pragmatic Review on Different Approaches Used in E-Learning Recommender Systems

Abstract

In recent years there has been a noteworthy development in the utilization of online learning resources by the learners. Due to information overload, many learners are experiencing challenges in retrieving relevant and useful learning resources that meet their needs. The recommender system in an e-learning context tries to intelligently recommend learning resources to a learner based on the task already done by the learner. The high diversity of learners on the internet presents new difficulties to the existing e-learning system. So we need to incorporate the learning characteristics of the learners in to the recommendation process. The existing techniques suffer from different challenges like cold-start problem, sparsity problem etc. In-order to overcome the limitations of the existing system we need to integrate some intelligent methods in to the recommendation approach. This paper reviews the various challenges as well as techniques used in an e-learning recommender system.