Abstract:

Knowledge tracing (KT) aims to infer learners’ dynamic knowledge states from historical interactions and predict their future responses. Despite considerable advances, existing KT methods still suffer from three practical limitations: performance disparities across student groups, sparse interaction data, and insufficient modeling of forgetting dynamics. To address these challenges, this paper proposes HGC-FKT, a heterogeneous counterfactual graph-enhanced fair knowledge tracing model. The model introduces a counterfactual-inspired graph augmentation module, which identifies disadvantaged student groups based on their historical learning records and constructs hypothetical interactions with high-value candidate questions. Potential responses to these unobserved interactions are estimated from the observed behaviors of similar learners and served as augmentation signals to enrich the student-question graph. Meanwhile, a group-disparity regularizer further reduces prediction differences between student groups. To alleviate interaction sparsity, HGC-FKT jointly encodes a student-question graph and a question-knowledge concept graph, thereby integrating behavioral and structural information. Furthermore, a forgetting-aware attention module further incorporates actual time intervals and repeated practice into temporal knowledge-state modeling. Extensive experiments on the Assist09, Assist12, and Junyi datasets demonstrate that HGC-FKT consistently improves predictive performance for disadvantaged students while maintaining strong overall accuracy. The model achieves an AUC of 0.8108 on Assist09, demonstrating the effectiveness of integrating graph augmentation, dual-graph representation learning, and forgetting-aware temporal modeling designs.

Key words: knowledge tracing, counterfactual graph augmentation, predictive fairness, heterogeneous graph convolutional network