HSEKT-GS: Hypergraph structure-enhanced for knowledge tracing with gumbel-softmax sampling

Knowledge tracing is a core task in intelligent education, aiming to model students’ knowledge states based on their learning behaviors and dynamically predict their mastery of specific concepts. In practical applications, online learning platforms typically provide a large number of questions, but each student can only interact with a small subset. This limited interaction results in extreme data sparsity for certain questions, hindering the model’s ability to build effective representations and make accurate predictions for them. Although recent hypergraph neural network-based knowledge tracing methods can model high-order heterogeneous relationships between questions, improving question representation to some extent, the hypergraph structure often overlooks latent global structural information. This limitation weakens the comprehensive semantic representation of questions, thereby affecting prediction performance. To address these challenges, we propose a Hypergraph Structure-Enhanced for Knowledge Tracing with Gumbel-Softmax sampling (HSEKT-GS). First, we construct a question–concept hypergraph and its dual graph, and incorporate a structural embedding mechanism to capture local high-order relational information between questions and between concepts. Second, to further enhance question representation, we introduce a hypergraph star expansion and use Gumbel-Softmax sampling to generate multiple perturbed embeddings per node to explore structural uncertainty. Finally, the updated representations of all sampled paths are averaged to reveal latent structural links and mitigate the over-smoothing issue in fully connected graphs. In addition, we incorporate a hypergraph structure regularization term as structural supervision to improve the robustness and interpretability of the framework. Experimental results on four publicly available datasets demonstrate that HSEKT-GS outperforms existing baseline methods.