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

arXiv cs.AIAugust 5, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2608.03006v1 Announce Type: new Abstract: 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 t

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