HCD: A Hierarchy Constraint-Aware Neural Cognitive Diagnosis Framework.

Abstract

Cognitive diagnosis (CD) aims to reveal students' proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students' knowledge mastery has become an urgent challenge. Although existing cognitive diagnosis frameworks enhance diagnostic accuracy by analyzing students' explicit response records, they primarily focus on individual knowledge state, failing to adequately reflect the relative ability performance of students within hierarchies. To address this, we propose the Hierarchy Constraint-Aware Neural Cognitive Diagnosis Framework (HCD), designed to more accurately represent student ability performance within real educational contexts. Specifically, the framework introduces a hierarchy mapping layer to identify students' levels. It then employs a hierarchy convolution-enhanced attention layer for in-depth analysis of knowledge concepts performance among students at the same level, uncovering nuanced differences. A hierarchy inter-sampling attention layer captures performance differences across hierarchies, offering a comprehensive understanding of the relationships among students' knowledge state. Finally, through personalized diagnostic enhancement, the framework seamlessly integrates hierarchy constraint-aware features with existing typical diagnostic methods, significantly improving the precision of student knowledge state representation and enhancing the adaptability and diagnostic performance of existing frameworks. Research shows that this framework not only reasonably constrains changes in students' knowledge state to align with real educational contexts, but also supports the scientific rigor and fairness of educational assessments, thereby advancing the field of cognitive diagnosis. To support reproducible research, we have published the data and code at https://github.com/xinjiesun-ustc/HCD, encouraging further innovation in this field.