通过建模知识结构状态，提升认知诊断的准确性与可解释性。
Enhancing Cognitive Diagnosis by Modeling Learner Cognitive Structure State
- 用图注意力网络融合知识掌握与概念关联，建模认知结构状态。
- 在真实数据集上，诊断准确率显著优于传统方法。
- 适合教育AI、智能评测系统研究者参考。
认知诊断是智能教育的核心研究方向，旨在衡量个体的认知状态。理论上，个体的认知状态等同于其认知结构状态，包含知识状态（KS）和知识结构状态（KUS）两部分。知识状态反映学习者对单个概念的掌握程度，已有广泛研究；而知识结构状态——即学习者对概念间关系的理解——仍缺乏有效建模。认知结构对促进有意义学习和学业表现至关重要。尽管已有多种方法，但多数仅关注知识状态评估，忽视了知识结构状态。为此，我们提出一种创新框架CSCD（基于认知结构状态的认知诊断），首次在诊断评估中系统建模认知结构状态。具体而言，采用基于边特征的图注意力网络，有效整合知识状态与知识结构状态。在真实数据集上的大量实验表明，该框架在诊断准确性和可解释性方面均表现优异。
原文摘要 · Abstract (English)
Cognitive diagnosis represents a fundamental research area within intelligent education, with the objective of measuring the cognitive status of individuals. Theoretically, an individual's cognitive state is essentially equivalent to their cognitive structure state. Cognitive structure state comprises two key components: knowledge state (KS) and knowledge structure state (KUS). The knowledge state reflects the learner's mastery of individual concepts, a widely studied focus within cognitive diagnosis. In contrast, the knowledge structure state-representing the learner's understanding of the relationships between concepts-remains inadequately modeled. A learner's cognitive structure is essential for promoting meaningful learning and shaping academic performance. Although various methods have been proposed, most focus on assessing KS and fail to assess KUS. To bridge this gap, we propose an innovative and effective framework-CSCD (Cognitive Structure State-based Cognitive Diagnosis)-which introduces a novel framework to modeling learners' cognitive structures in diagnostic assessments, thereby offering new insights into cognitive structure modeling. Specifically, we employ an edge-feature-based graph attention network to represent the learner's cognitive structure state, effectively integrating KS and KUS. Extensive experiments conducted on real datasets demonstrate the superior performance of this framework in terms of diagnostic accuracy and interpretability.
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