Recommendation System for Smart LMS Using Machine Learning: A Literature Review

Abstract

This paper presents the result of Systematic Literature Review (SLR) on Recommender System (RS) topic as a preliminary toward a further study on designing a smart Learning Management System (LMS) for online learning which adopts Natural Language Processing techniques. As a foundation to a broader study on smart LMS, this study focused on analyzing prominent study reports on recommender systems in general and online learning in particular. The SLR method analyzed papers published in the range of 2013-2018. Out of the 109 papers this study analyzed indepth 42 papers. The study findings confirmed that most of RS studies still focused on e-commerce, movies, tourists, and more whose most popular RS methods were collaborative filtering and content base. Some studies in RS for online education were mostly focused on scheduling, recommendations for courses, books, prospective students and others. The results of this study found that there are still much opportunities to develop methods and approaches for RS in online learning. This study findings gives foundation of our future research to develop a model of conscious contextual recommendation system using Machine Learning based on smart LMS for online learning.