This study aims to develop a recommender system for social learning platforms that combine traditional learning management systems with commercial social networks like Facebook. We therefore take into account social interactions of users to make recommendations on learning resources. We propose to make use of graph-walking methods for improving performance of the well-known baseline algorithms. We evaluate the proposed graph-based approach in terms of their F1 score, which is an effective combination of precision and recall as two fundamental metrics used in recommender systems area. The results show that the graph-based approach can help to improve performance of the baseline recommenders; particularly for rather sparse educational datasets used in this study.
This article explores the feasibility of using student promotions of content, in a blogosphere, to identify quality content, and implications for students and instructors. It shows that students actively and voluntarily promote content, identify quality material with considerable accuracy, and use promotion data to select what to read. Application of the peer promotions tool provides the desired results — the promoted content is of significantly higher quality than content that is not promoted, and content that is repeatedly promoted is of higher quality than content that has fewer promotions. These results have been verified by two different case studies. Other results show that good and poor promoters can be identified. Both classifications of promoters have value: by focusing on good promoters, the reliability of quality assessment can be improved; by focusing on poor promoters, the instructor is in a better position to identify students who may be struggling.