将认知诊断结构嵌入神经网络，实现可解释的智能学业评估
Multilayer Q-Matrix-Embedded Neural Network for Cognitive Diagnosis (M-QCDNet): Structure-Aware Deep Learning Architecture for Psychometric Interpretability
- 用Q矩阵作为结构先验，约束模型学习技能关联
- 新损失函数提升预测准确率同时保持理论一致性
- 适合教育AI、智能评测系统开发者使用
本文提出多层嵌入Q矩阵的认知诊断神经网络（M-QCDNet），将认知诊断模型（CDMs）的结构可解释性与深度神经网络（NN）结合。M-QCDNet以Q矩阵定义题目-技能关系作为结构先验，确保潜在掌握状态符合认知理论；设计含L2正则项的损失函数，惩罚与Q矩阵不一致的技能激活，平衡预测性能与结构对齐。进一步构建可解释对齐度量，量化预测技能激活与题目层面技能的匹配程度。该模型可支持教学中早期发现学习困难，推动基于掌握度的干预。通过在模型设计中嵌入诊断有效性，M-QCDNet实现了心理测量透明性与神经网络灵活性的融合，为可解释、公平且可操作的认知诊断AI提供新范式。
原文摘要 · Abstract (English)
The research proposes a multilayer Q-matrix-embedded neural network for cognitive diagnosis (M-QCDNet), which integrates the structural interpretability of cognitive diagnostic models (CDMs) with the deep learning neural network (NN). M-QCDNet structures the item-skill relationship using the Q-matrix as a structural prior, ensuring latent mastery profiles remain interpretable and consistent with cognitive theory, followed by the proposed loss function with an L2 penalty to penalize skills not aligned with the Q-matrix and to balance predictive performance and structural alignment. Corresponding evaluation matrices, the interpretable alignment-based metrics that quantify the degree to which predicted skill activations correspond to item-level skills, were further developed. M-QCDNet offers practical benefits for classroom practice, enabling early detection of learning difficulties and supporting mastery-based interventions. By embedding diagnostic validity into model design, M-QCDNet bridges psychometric transparency and neural flexibility, advancing interpretable, fair, and actionable AI for cognitive diagnostics.
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