新框架通过层级约束提升学生能力评估精度，更符合真实教育场景。
Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts
- 引入层级映射与卷积注意力机制，分析同层级学生知识表现差异。
- 跨层级采样注意力捕捉不同层次间的能力差距，提升诊断全面性。
- 适合教育智能系统开发、个性化学习诊断场景使用。
认知诊断（CD）旨在揭示学生在特定知识概念上的掌握程度。随着智能教育应用的普及，准确评估学生知识掌握情况成为迫切挑战。现有认知诊断框架虽通过分析学生显式作答记录提升诊断准确性，但主要关注个体知识状态，未能充分反映学生在层级结构中的相对能力表现。为此，我们提出层级约束感知的认知诊断框架（HCD），以更准确地刻画真实教育情境中学生的能力建构。该框架首先通过层级映射层识别学生所处层级；随后利用层级卷积增强注意力层深入分析同层级学生对知识概念的表现差异；再通过层级间采样注意力层捕捉跨层级的能力差异，全面理解学生知识状态间的关联。最后，通过个性化诊断增强，将层级约束感知特征融合至现有模型，提升对个体与群体特征的表征能力。实验证明，该方法不仅合理约束学生知识状态变化以契合真实教育环境，还提升了教育评估的科学性与公平性，推动认知诊断领域发展。
原文摘要 · Abstract (English)
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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