Augmenting knowledge tracing through modeling dynamic higher-order concept interactions: A temporal hypergraph memory network

Knowledge Tracing (KT) aims to estimate students’ evolving knowledge states from their question–answer interactions, with performance prediction as the evaluation proxy. Memory-augmented neural networks have transformed KT by automatically extracting latent concepts and maintaining dynamic, interpretable representations of student understanding. Despite these advances, such models treat concepts as independent entities. They fail to capture how higher-order interactions among concepts shape learning trajectories - particularly when concepts co-occur across varying question compositions. To address these gaps, we propose the Temporal Hypergraph Memory Network (THyMeN), a hybrid model that augments memory-based tracing with temporal hypergraph reasoning. THyMeN captures dynamic higher-order concept interactions by representing students’ learning histories as evolving hypergraphs, where each hyperedge reflects the multi-concept structure of a question. A bidirectional message-passing mechanism enables mutual refinement between concept nodes and question hyperedges, modeling how concept dynamics shift across questions. An attention-based fusion mechanism then integrates this composition-aware information with concept-level knowledge states, enabling predictions that reflect both contextual variability and persistent learning. Additionally, an adaptive scaling strategy moderates mastery updates using the diversity of concept co-occurrences across questions, aiming to model stable trajectories consistent with learning from varied practice. Experiments on four benchmark datasets show that THyMeN outperforms seven baselines and state-of-the-art models in predictive accuracy, and generates smoother, pedagogically plausible knowledge trajectories. Ablation and structural comparison studies further validate its design innovations, underscoring the importance of modeling concept co-occurrence and practice diversity. These results establish THyMeN as an interpretable framework that advances KT toward explainable and pedagogically grounded learning support.