Learning Student Knowledge States from Multi-View Question–Skill Networks

Accurately modeling students’ evolving knowledge states is a core challenge in intelligent education systems. Existing knowledge tracing methods often rely on single-view representations, limiting their ability to capture high-order dependencies among questions and underlying skills. In this paper, we propose a Multi-View Question–Skill Network (MVQSN), which constructs complementary relational views from readily available data to model student knowledge states comprehensively. Specifically, we first build a question–skill bipartite graph capturing direct question–skill associations, a skill complementarity view where edges represent synergistic relationships between skills derived from co-occurrence patterns, and a question co-response view that captures hierarchical question–question associations emerging from the hierarchical patterns of aggregated student responses. Each view is independently encoded using graph neural networks, producing view-specific student knowledge embeddings. To enhance representation consistency across views while preserving unique view information, we employ a multi-view contrastive learning module that aligns embeddings from different views. The aligned embeddings are then fused through an attention-guided mechanism, generating a unified and expressive student knowledge state representation. Finally, the unified embeddings are fed into a prediction layer to estimate students’ future performance. We conduct extensive experiments on three publicly available datasets, demonstrating that MVQSN significantly outperforms state-of-the-art baselines in prediction performance.