A literature review of implemented recommendation techniques used in Massive Open online Courses

Massive Open Online Courses (MOOCs) are receiving attention from learners because MOOCs enable them to satisfy their learning needs through an open, participatory, and distributed way. With the increased interest from learners, the number of MOOCs available is increasing which has increased options for learners. This as a result has created the need for recommendation systems that help learners select suitable MOOCs. This literature review covers analysis of recommender systems (RSs) that have been implemented in MOOCs with the goal of providing insights on the trends reported in the academic literature on recommender systems in MOOCs. The review discusses different recommendation techniques, recommendation types and evaluation techniques that have been used and reported on. This review includes research work over eight years, i.e. from 1st January 2012 to 17th November 2020. After the filtering process, 67 papers were selected from journals and conferences from four academic databases (i.e., IEEE, ACM, Science Direct, and Springer). A framework is designed that classifies literature on the basis of both design and evaluation aspects of RS in MOOCs. This review concludes by highlighting gaps found in the literature.