ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs

Computer Science > Artificial Intelligence [Submitted on 4 Aug 2026] ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs Xinghe Cheng, Jiapu Wang, Chaobo He, Ruihai Dong, Quanlong Guan Prerequisite relation learning is central to adaptive instruction, yet existing methods often formulate it as conventional link prediction, limiting their ability to adaptively integrate complementary educational evidence for individual candidate pairs and to discourage contradictory reverse predictions. We propose ProPRL, a Property-aware Prerequisite Relation Learning framework. ProPRL first learns complementary concept representations from a concept-resource hypergraph and a directed learning-behavior graph, where direction-preserving personalized propagation aggregates multi-hop behavioral evidence. It then employs a Pair-conditioned Gate to adaptively weight and fuse the two views for each candidate ordered concept pair. Finally, an \textit{Irreversibility Constraint} introduces an anti-symmetry regularizer that penalizes simultaneously high confidence in both directions of the same concept pair. Experiments on multiple real-world educational datasets show that ProPRL achieves state-of-the-art performance on prerequisite relation learning. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.03006 [cs.AI]   (or arXiv:2608.03006v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2608.03006 Focus to learn more Submission history From: Jiapu Wang [view email] [v1] Tue, 4 Aug 2026 01:41:01 UTC (2,610 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)