情感认知诊断的能力残差解耦建模
Ability-Residual Decoupled Modeling for Affective Cognitive Diagnosis
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中文总结 AI 辅助
针对情感认知诊断中认知残差泄漏导致情感污染的问题，提出能力残差解耦框架，先建模未捕获的认知残差，再用情感模块调节猜测/失误效应，在多个数据集上提升预测精度和情感对齐。
中文摘要 AI 辅助
认知诊断从答题记录中推断学生的概念掌握程度。然而，学生的作答并非仅由掌握程度决定：情绪、投入度和疲劳等非认知因素也会影响表现。因此，情感认知诊断通过纳入情感状态来扩展传统认知诊断。现有方法通常假设认知诊断主干已经解释了能力、题目和概念效应，因此剩余误差主要可归因于情感。我们认为，这一假设在真实教育数据中可能不充分：题目校准偏差、系统性概念偏差、个性化学生-概念偏差以及潜在的学生-题目匹配会形成稳定的认知残差。若无显式建模路径，这些残差可能泄漏到情感表示中，导致情感污染。为解决此问题，我们提出一种用于情感认知诊断的能力残差解耦框架。该模型首先通过学生、题目、概念、学生-概念和低秩学生-题目组件捕获未建模的认知残差，然后使用情感模块调节猜测/失误效应。一种受Q矩阵约束的概念残差注意力机制自适应地聚合仅与题目相关的概念残差。在ASSIST2017、ASSIST2012、ASSIST2009和Junyi数据集上，使用六种认知诊断主干进行的实验表明，在报告的比较中均获得作答预测增益，并且在有情感标签时，情感对齐普遍改善。消融研究、泄漏探测、主成分分析可视化、长尾分析和案例研究进一步表明，能力残差吸收了稳定的认知偏差，减少了情感分支中的认知污染，并增强了认知诊断模型的鲁棒性和预测准确性。
英文摘要
Cognitive diagnosis infers students' concept mastery from response logs. However, students' responses are not determined by mastery alone: non-cognitive factors such as emotion, engagement, and fatigue can also affect performance. Affective cognitive diagnosis therefore extends conventional cognitive diagnosis by incorporating affective states. Existing methods often assume that the cognitive diagnosis backbone has already explained ability, item, and concept effects, so the remaining errors can be attributed mainly to affect. We argue that this assumption can be insufficient in real educational data: item calibration bias, systematic concept bias, personalized student-concept deviations, and latent student-item matching can form stable cognitive residuals. Without an explicit modeling pathway, these residuals may leak into affective representations, producing affect contamination. To address this problem, we propose an ability-residual decoupled framework for affective cognitive diagnosis. The model first captures unmodeled cognitive residuals through student, item, concept, student-concept, and low-rank student-item components, and then uses an affective module to modulate guess/slip effects. A Q-matrix-constrained concept residual attention mechanism adaptively aggregates only item-relevant concept residuals. Experiments on ASSIST2017, ASSIST2012, ASSIST2009, and Junyi with six cognitive diagnosis backbones show response-prediction gains across the reported comparisons and generally improved affect alignment when affect labels are available. Ablation studies, leakage probes, principal component analysis visualization, long-tail analysis, and case studies further indicate that ability residuals absorb stable cognitive bias, reduce cognitive contamination in the affective branch, and enhance the robustness and predictive accuracy of cognitive diagnosis models.
发表机构
- Key Laboratory of Modern Teaching Technology, Shaanxi Normal University（陕西师范大学现代教学技术教育部重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
