ConceptGCN: Knowledge concept recommendation in MOOCs based on knowledge graph convolutional networks and SBERT

Abstract

Massive Open Online Courses (MOOCs) have gained popularity in the technology-enhanced learning (TEL) domain. To enhance the learning experience in MOOCs, educational recommender systems (ERSs) can play a crucial role by suggesting courses or learning materials that align with students' knowledge states. Thereby, understanding a student's learning needs and predicting knowledge concepts that the student might be interested in are important to provide effective recommendations. Inspired by the superior ability of knowledge graphs (KGs) in modeling the heterogeneous data in MOOCs and Graph Neural Networks (GNNs) in learning on graph-structured data, few works focusing on GNN-based recommendation of knowledge concepts in MOOCs have emerged recently. However, existing approaches in this domain have limitations mainly related to complexity, semantics, and transparency. To address these limitations, in this paper we propose ConceptGCN, an end-to-end framework that combines KGs, Graph Convolutional Networks (GCNs), and pre-trained transformer language model encoders (SBERT) to provide personalized and transparent recommendations of knowledge concepts in the MOOC platform CourseMapper. We conducted extensive offline experiments and an online user study (N=31), demonstrating the benefits of the ConceptGCN-based recommendation approach, in terms of several important user-centric aspects including accuracy, novelty, diversity, usefulness, overall satisfaction, use intentions, and reading intention. In particular, our results indicate that, if SBERT is used for the initial embeddings of items in the KG, a self-connection operation and a semantic similarity-based score function in the aggregation operation of GCN are not necessarily needed.