Learning materials recommendation using good learners’ ratings and content-based filtering

The enormity of the amount of learning materials in e-learning has led to the difficulty of locating suitable learning materials for a particular learning topic, creating the need for recommendation tools within a learning context. In this paper, we aim to address this need by proposing a novel e-learning recommender system framework that is based on two conceptual foundations—peer learning and social learning theories that encourage students to cooperate and learn among themselves. Our proposed framework works on the idea of recommending learning materials with a similar content and indicating the quality of learning materials based on good learners’ ratings. A comprehensive set of experiments were conducted to measure the system accuracy and its impact on learner’s performance. The obtained results show that the proposed e-learning recommender system has a significant improvement in the post-test of about 12.16% with the effect size of 0.6 and 13.11% with the effect size of 0.53 when compared to the e-learning with a content-based recommender system and the e-learning without a recommender system, respectively. Furthermore, the proposed recommender system performed better in terms of having a small rating deviation and a higher precision as compared to e-learning with a contentbased recommender system.