Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts

• URL: http://arxiv.org/abs/2412.04488v1
• Date: Thu, 21 Nov 2024 11:37:36 GMT
• Title: Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts
• Authors: Xinjie Sun, Qi Liu, Kai Zhang, Shuanghong Shen, Fei Wang, Yan Zhuang, Zheng Zhang, Weiyin Gong, Shijin Wang, Lina Yang, Xingying Huo,
• Abstract summary: We propose the Hierarchy Constraint-Aware Cognitive Diagnosis Framework (HCD) HCD aims to more accurately represent student ability performance within real educational contexts. It integrates hierarchy constraint perception features with existing models, improving the representation of both individual and group characteristics.
• Score: 26.287186220848763
• License:
• 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 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 integrates hierarchy constraint perception features with existing models, improving the representation of both individual and group characteristics. This approach enables precise inference of students' knowledge state. Research shows that this framework not only reasonably constrains changes in students' knowledge states to align with real educational settings, but also supports the scientific rigor and fairness of educational assessments, thereby advancing the field of cognitive diagnosis.

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