Recommendation Systems on E-Learning and Social Learning: A Systematic Review

E-learning is renowned as one of the highly effective modalities of learning. Social learning, in turn, is considered to be of major importance as it promotes collaboration between learners. For properly managing learning resources, recommender systems have been implemented in e-learning to enhance learners' experience. Whilst recommender systems are of widespread concern in online learning, it is still unclear to educators how recommender systems can improve the learning process and have a positive impact on learning. This paper seeks to provide an overview of the recommender systems proposed in e-learning between 2007 and the first part of 2021. Out of 100 initially identified publications for the period between 2007 and the first part of 2021, 51 articles were included for final synthesis, according to specific criteria. The descriptive results show that most of the disciplines involved in educational recommender systems papers have approached e-learning in a general way without putting as much emphasis on social learning, and that recommender systems based on explicit feedbacks and ratings were the most frequently used in empirical studies. The synthesis of results presents several recommender systems types in e-learning: (1) content-based recommender systems; (2) collaborative-filtering recommender systems; (3) hybrid recommender systems; and (4) recommender systems based on supervised and unsupervised algorithms. The conclusions reflect on the almost lack of critical reflection on the importance of addressing recommender systems in social learning and social educational networks in particular, especially as social learning has particular requirements, the weak databases size used in some research work, the importance of acknowledging the strengths and weaknesses of each type of recommender system in an educational context and the need for further exploration of implicit feedbacks more than explicit learners' feedbacks for more accurate recommendations.