A model to solve sparsity challenge in cognitive diagnosis

Cognitive Diagnosis (CD) plays a crucial role in personalized learning by evaluating students' mastery of various concepts. However, current CD models face a significant challenge—the "student-concept sparsity barrier." This occurs when students have limited interactions with certain concepts.

To solve the problems, a research team led by Kun Zhang published their new research on 15 November 2025 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

The team proposed developed a new ESR-CD model. This model considers two key elements: concept mastery (how well students grasp concepts) and application ability (their capacity to apply knowledge in problem-solving). ESR-CD uses a unique sparsity-based mask module, along with matrix factorization and exercise-concept relationship refinement layers, enabling more accurate assessments even with limited data.

Extensive experiments demonstrated significant improvements of ESR-CD, with AUC gains of 1.5% on the ASSIST dataset and over 6% on the MOOC-Radar dataset. ESR-CD provides a more effective way to assess students' mastery and application skills in real-world learning environments. Accurate assessment can help educators create more personalized learning experiences. Future work can focus on exploring concept dependencies for better diagnostic performance by prompting large language models or preventing the amplification of unwanted biases in CD.