HRKT: Hierarchical Recurrent Knowledge Tracing for Efficient Transformer-Based Long-Sequence Modeling

Abstract

Knowledge Tracing (KT) is the task of modeling students' knowledge states from past learning interaction data to predict future responses. Attention-based KT models such as SAKT and AKT have demonstrated excellent performance by effectively capturing inter-problem relationships through Attention mechanisms. However, these attention-based models face efficiency limitations due to quadratically increasing computational costs as interaction sequences lengthen. Moreover, existing models struggle to effectively capture overall knowledge evolution trends across long-term learning trajectories. To address these issues, this paper proposes Hierarchical Recurrent Knowledge Tracing (HRKT), a novel knowledge tracing framework that introduces a new hierarchical recurrent design for Transformer-based KT models. By separately modeling local interaction patterns and global knowledge evolution, HRKT enables efficient long-sequence modeling and improves prediction performance. Extensive experiments on real-world educational datasets confirm that Transformer-based models augmented with HRKT significantly reduce computational cost and memory usage compared to conventional Transformer-based KT models, while achieving improved performance on long sequences.