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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.
- PaperComputers and Education: Artificial Intelligence11 Jul 2026
Fair and explainable educational recommendations with a hybrid Graph-GRU framework
Edmund Evangelista, Syed M. Salman Bukhari
Introduces the Hybrid Heterogeneous Knowledge Graph-Gated Recurrent Unit (Hybrid HKG-GRU) framework to improve fairness, robustness, diversity, and explainability in educational recommender systems. Combines heterogeneous graph embeddings with sequential modeling, and integrates group distributionally robust optimization, MMR reranking, and path-based counterfactual explanations. Empirical evaluation on Moodle logs shows strong predictive performance (HR@10=0.68, MRR=0.41) and improved diversity, though popularity bias persists.
Original abstract
Artificial Intelligence (AI) recommender systems are increasingly used in education to personalize learning and help students navigate large collections of digital learning resources. However, many existing approaches emphasize predictive accuracy over fairness, robustness, diversity, and transparency. This creates an important educational challenge. The students with limited participation histories may receive less reliable support, while highly popular resources may dominate recommendation lists and limit access to other useful learning materials. To address this challenge, this study aims to develop and evaluate a responsible educational recommender framework that supports personalized learning resource navigation while making recommendation behaviour more fair, stable, diverse, and explainable. This study introduces the Hybrid Heterogeneous Knowledge Graph-Gated Recurrent Unit (Hybrid HKG-GRU) framework, which combines heterogeneous graph embeddings with sequential modelling to capture both the relational structure of course materials and the temporal dynamics of learner interactions. The framework integrates three contributions: (i) multi-objective training with Group Distributionally Robust Optimization (GroupDRO), (ii) Maximum Marginal Relevance (MMR) reranking to reshape exposure patterns, and (iii) built-in, model-centric explainability through path-based and counterfactual analyses. The empirical evaluation shows strong predictive performance with HR@10 = 0.68 and MRR = 0.41 on Moodle LMS logs dataset that comprises of 152 students, 59 resources, and approximately 150k interactions. The model also achieves high intra-list diversity and moderate catalogue coverage, while showing moderate counterfactual stability for many learners (median CR@10 = 1.0), although catalogue-level popularity bias remains evident. The framework provides model-centric interpretability and verification intended to support more transparent educational recommendation. The study positions the framework as a technically auditable approach for improving how learning resources are recommended, inspected, and monitored in educational settings. By integrating fairness, robustness, and explainability as coequal design objectives, the Hybrid HKG-GRU provides a methodological foundation for more responsible and accountable recommender systems in education and related high-stakes contexts.