A Systematic Review: Deep Learning based E-Learning Recommendation System

摘要

Recently, there is notable development in usage of online learning resources by the learners. Increasing offerings of online materials to student creates complexity to locate particular data from data pools. Likewise, overloaded information in online makes the learner feel difficult to access needed information. The complexity is reduced with help of e-learning Recommendation System (RS). E-learning based RS try to suggest perfect learning resources to the learner depending on previous tasks done by him. High usage of internet by the learner includes more complexity to current E-Learning system. Nowadays, e-learning RS depends on Deep learning technique for their progression. But still issues and challenges remains in form of accuracy, time consumption, and scalability, cold-start and data scarcity. So, in this survey, e-learning RS based on various DL approaches are reviewed. A taxonomy is created in which accounted for components needed to create efficient RS. Survey creates prominent contribution to filed e-learning RS by performing overview on current research and existing challenges.