Exploiting Semantic Information for Graph-Based Recommendations of Learning Resources

Abstract

Recommender systems in e-learning have different goals as compared to those in other domains. This brings about new requirements such as the need for techniques that recommend learning resources beyond their similarity. It is therefore an ongoing challenge to develop recommender systems considering the particularities of e-learning scenarios like CROKODIL. CROKODIL is a platform supporting the collaborative acquisition and management of learning resources. It supports collaborative semantic tagging thereby forming a folksonomy. Research shows that additional semantic information in extended folksonomies can be used to enhance graph-based recommendations. In this paper, CROKODIL’s folksonomy is analysed, focusing on its hierarchical activity structure. Activities help learners structure their tasks and learning goals. AScore and AInheritScore are proposed approaches for recommending learning resources by exploiting the additional semantic information gained from activity structures. Results show that this additional semantic information is beneficial for recommending learning resources in an application scenario like CROKODIL.