Cross-Temporal Relational Alignment for Unified Knowledge Tracing

Online learning platforms accumulate student-question records for learning analytics, early warning, and institutional decision support. Mining these records requires accurate next-response prediction and stable modeling of temporal-relational patterns among items and knowledge components. Existing knowledge tracing methods use static structures or separate temporal and relational modeling, limiting their ability to capture evolving dependencies and causing relational drift. To address this issue, this paper proposes the Cross-Temporal Relational Alignment Network (CTRA-Net), a framework for joint sequential and dynamic structural modeling. CTRA-Net uses time-conditioned graph attention to reconstruct knowledge relations under the current learner state and a memory bank to preserve historical prototypes for cross-stage alignment. Student-level five-fold cross-validation on six public datasets shows that CTRA-Net outperforms sequential, graph-based, spatiotemporal, memory-based, and semantic-enhanced baselines. Drift, cold-start, and efficiency analyses show better stability with deployable cost.