J* E* C* N* U* N* S* ›› 2025, Vol. 2025 ›› Issue (5): 76-86.doi: 10.3969/j.issn.1000-5641.2025.05.008

• Innovative Practices of Open Source and AI in Education • Previous Articles     Next Articles

Static cognitive diagnosis model enhanced by knowledge point relations

Henggui LIANG1, Yihui ZHU2, Xiaowen TANG2, Mingdong ZHU3,*()   

  1. 1. School of Management, Guizhou University, Guiyang 550025, China
    2. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
    3. School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan 453003, China
  • Received:2025-06-27 Online:2025-09-25 Published:2025-09-25
  • Contact: Mingdong ZHU E-mail:hackdong@126.com

Abstract:

Cognitive diagnosis, a core task in personalized education, aims to evaluate students’ mastery of knowledge points using historical response records. Existing static cognitive diagnosis models are typically based on manually annotated key knowledge points, ignoring potential correlations between knowledge points within items as well as differences in how items emphasize specific knowledge points. To address these limitations, this study proposes a static cognitive diagnosis model improved by knowledge point relations (Q-matrix Enhanced Neural Cognitive Diagnosis, QENCD) model. The model optimizes the item-knowledge point association vector by constructing knowledge point dependency relationships and item emphasis information, then integrating these features through residual connections. The experimental results showed that QENCD model significantly outperforms existing models on the ASSIST09, ASSIST17, and Junyi datasets significantly outperforming state-of-the-art baselines. This study provides a more precise knowledge modeling method for static cognitive diagnosis.

Key words: cognitive diagnosis, knowledge point correlation, attention mechanism, residual connection, static model

CLC Number: