Multi-Perspective Consolidation Enhanced Cognitive Diagnosis via Conditional Diffusion Model

Guanhao Zhao, Zhenya Huang, Cheng Cheng, Yan Zhuang, Qingyang Mao, Xin Li, Shijin Wang, Enhong Chen

Abstract: Cognitive diagnosis, which assesses the learners' competence from learners' interaction logs, plays a vital role in education. It provides a crucial reference for gauging learners' proficiency levels and tailoring future learning activities accordingly. Researchers have proposed numerous cognitive diagnosis models to address this task. Despite their success, these models continue to face the ill-posed problem because of the information loss caused by under-expressive interaction function and incomplete observations. In this paper, we address these challenges by proposing a novel cognitive diagnosis model, DMC-CDM, based on the theoretical premise that cognitive states can be captured with minimal information loss by maximizing the mutual information between observed and potential observations. Specifically, DMC-CDM incorporates a semantic extractor to provide a comprehensive semantic understanding of learners' interaction logs, thereby enhancing current collaborative-based cognitive state representations. It then consolidates multi-perspective observations to capture precise cognitive states by maximizing mutual information between these observations. We conducted extensive experiments on three datasets, and the experimental results demonstrate that our proposed model is both effective and beneficial for downstream applications in education.