Unveiling the Impact of Hierarchical Knowledge Dependencies on Knowledge Tracing: A Spatial Structure Perspective
TL;DR
This study investigates how multilevel spatial relationships among KCs affect the performance of KT models and infer causal links among KCs and incorporate the resulting spatial structures into both deep learning and traditional machine learning KT models.
Abstract
Knowledge tracing (KT) predicts learners’ evolving knowledge states by tracking their performance over time. While temporal dynamics have been the focus of most KT studies, spatial structures among knowledge components (KCs) remain underexplored, despite containing rich latent information. Prior work suggested that inter-KCs relationships can enhance KT performance, yet the impact of hierarchical spatial structures remains unclear. This study investigates how multilevel spatial relationships among KCs affect the performance of KT models. Using causal structure learning, we infer causal links among KCs and incorporate the resulting spatial structures into both deep learning and traditional machine learning KT models. Experimental results showed that incorporating second-order spatial structures yielded consistent performance gains. These findings underscore the value of spatial structural information in KT. Furthermore, interpretable feature analyses illustrated how spatial features shape diagnostic predictions, providing insight into factors underlying students’ learning challenges. This spatial perspective not only improves KT models’ performance but also has the potential to inform more targeted and effective instructional strategies.