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HetGNN-KGAT : Enhancing Personalized Course Recommendation in MOOCs With Knowledge Graph Attention Networks
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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24
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IEEE Access, Volume 13, pp. 192045-192068
Abstract
Personalized course recommendations are essential for enhancing learning in Massive Open Online Courses (MOOCs). However, MOOCs data are typically sparse and incomplete, which limits the effectiveness of graph-based recommendation models. To address this challenge, we propose HetGNN-KGAT, a two-stage framework that integrates Heterogeneous Graph Neural Networks (HetGNN) for imputing missing links and attributes with Knowledge Graph Attention Networks (KGAT) for capturing high-order relations. The contributions of this work are threefold. First, we present a graph-based imputation strategy that mitigates sparsity by enriching MOOCs data through HetGNN. Second, we demonstrate that enriching the graph allows KGAT to operate more effectively, highlighting that the novelty lies not in simply combining two models but in strategically enabling KGAT to exploit sparse MOOCs data. Third, we establish a systematic knowledge discovery pipeline that explicitly evaluates input data quality (completeness and consistency) alongside downstream recommendation performance (MAP, NDCG, Precision, Recall), providing a comprehensive view of how data enrichment improves recommendations. Extensive experiments on the MOOCCubeX dataset show that HetGNN-KGAT consistently outperforms strong baselines in both simulated and real-world scenarios. The framework achieves up to 23.55% relative improvement in MAP, 11.37% in F1-score, and 27.70% in NDCG under simulated conditions, with corresponding gains of 7.82%, 3.73%, and 9.24% in real-world settings. These results highlight that the proposed framework not only strengthens the accuracy of course ranking but also enhances the overall relevance and reliability of personalized recommendations. This confirms the value of coupling data-quality enhancement with graph-based recommendation and offers practical insights for advancing adaptive educational technologies.
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Nguyen, T, Do, D, Vi, H, Vo, K T, Ta, T T, Nguyen, P, Nguyen, H T & Nguyen-Hoang, T A 2025, 'HetGNN-KGAT : Enhancing Personalized Course Recommendation in MOOCs With Knowledge Graph Attention Networks', IEEE Access, vol. 13, pp. 192045-192068. https://doi.org/10.1109/ACCESS.2025.3630894