Knowledge tracing aims to dynamically track and assess learners’ mastery of specific knowledge concepts (e.g., item taxonomies, skill hierarchies). Integrating it into recommender systems greatly enhances model explainability, yet current models either overemphasize temporal dynamics or overlook inter-concept spatial correlations, resulting in suboptimal performance in integrated spatiotemporal modeling. Such disjointed designs also lead to limited adaptability in long learning sequences, poor compatibility with real-world dynamic scenarios, and restricted model interpretability. To solve these problems, we propose an A-SGNN framework fusing higher-order path spiking and graph neural networks, which leverages Graph Convolutional Network (GCN) and Spiking Neural Network (SNN) modules to make up for existing shortcomings. Specifically, a Bidirectional GCN module fully captures bidirectional high-order spatial graph structures between concepts, accurately modeling multi-knowledge relationships and reducing information loss. An SNN module with adaptive path-finding strategy dynamically optimizes individual learning trajectories, overcoming traditional fixed-path rigidity, while its membrane potential decay simulates human forgetting. A time decay factor is integrated to better capture memory effects in learning. Experiments demonstrate that our method outperforms state-of-the-art approaches in predicting learners’ future academic performance, with this advantage deriving from the combined synergistic benefits of the GCN and SNN modules. Our implementation is publicly available at: https://github.com/jianruichen/A-SGNN .
Hu et al. (Mon,) studied this question.