learner-modeling
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- PaperarXiv — AI in Education (cs.CY)21 Jul 2026
Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks
Rawaa Alatrash, Mohamed Amine Chatti, Hong Yang, Yumeng Wang
This study proposes MR-ConceptGCN, an unsupervised approach that combines Personal Knowledge Graphs, multi-relational Graph Convolutional Networks, and a pre-trained language model to model learners' sequential interactions. An online user study with 31 participants showed that the method improves accuracy, usefulness, diversity, and satisfaction in an educational recommender system.
Original abstract
User modeling is a critical task in a variety of personalized systems. Recognizing their effectiveness in learning from graph-structured data, Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), are increasingly employed for user modeling. However, existing approaches typically treat different relation types in a graph as homogeneous, limiting their ability to capture richer semantics and construct more informative user models. While multi-relational GNNs (MR-GNNs) have been adopted for representation learning and recommendation, their application for user modeling remains unexplored. Moreover, existing GNN-based user modeling approaches ignore the user interaction sequence. To address these research gaps, in this work we propose MR-ConceptGCN, a novel fully unsupervised approach focused on concept-based sequential learner modeling using multi-relational GCNs (MR-GCNs). MR-ConceptGCN effecively combines Personal Knowledge Graphs (PKGs), MR-GCNs, and the pre-trained language model SBERT to obtain enhanced relation- and semantic-aware representations of the PKG items. The enriched embeddings of the knowledge concepts that a learner did not understand when interacting with learning materials in CourseMapper are then used to construct a sequential learner model that combines long-term and short-term learner interactions. We report the results of an online user study (n = 31), demonstrating the benefits of MR-ConceptGCN in terms of several important user-centric aspects including accuracy, usefulness, diversity, and satisfaction with an educational recommender system.