TIST Journal 2026 Journal Article
Interpretable Structure Learning for Knowledge Components in Education
- Yuang Wei
- Yuan-Hao Jiang
- Changyong Qi
- Wei Zhang
- Bo Jiang
Structural relationships among Knowledge Components (KCs) are essential for adaptive learning systems, as they support accurate cognitive diagnosis, personalized path planning, and targeted resource recommendation. However, existing approaches frequently capture correlations instead of reliable directional dependency signals and tend to converge prematurely or become inefficient as graph dimensionality grows. These limitations weaken the reliable modeling of KC-level structure, which in turn reduces interpretability and limits downstream benefits for diagnosis, planning, and recommendation. To this end, we propose a novel structure learning framework that integrates psychometric modeling with structural search. First, we design the I tem R esponse T heory (IRT)-based I nformation C riterion ( IRIC ), an interpretable scoring function that combines information entropy with causal effect estimation grounded in IRT, jointly capturing statistical associations and directionality-sensitive signals under latent ability control. Second, we develop C o- E volutionary O ptimization for S tructural S earch ( CEO-SS ), a multi-population evolutionary algorithm with a game-inspired co-evolution mechanism that balances exploration and exploitation, avoiding premature convergence and showing robust search behavior as graph dimensionality increases within the evaluated benchmarks. Extensive experiments on three types of datasets—including benchmark causal discovery datasets, the public educational dataset, and real-world classroom data—demonstrate that our framework consistently outperforms strong baselines in accuracy and stability, with especially clear gains in adjacency recovery and more modest improvements in edge-direction recovery. In addition, expert evaluation suggests that the learned structures are more diagnostically useful, more actionable for remediation, and more pedagogically plausible than those produced by alternative scoring methods. Overall, the proposed framework provides an interpretable and practically valuable approach to learning KC structures for adaptive learning.