Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners' current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric-based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large-scale learning data. While the deep learning-based learning diagnosis methods have improved the accuracy of learning performance prediction, their inherent black-box properties lead to a lack of interpretability, making their results untrustworthy for educational applications. To settle the above problem, the proposed unified interpretable intelligent learning diagnosis (UIILD) framework, which benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics, achieves a better performance of learning prediction and provides interpretability from three aspects: cognitive parameters, learner-resource response network, and weights of self-attention mechanism. Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI. Experiments on two real-world datasets and a simulation dataset show that our method has higher accuracy in predicting learners' performances compared with the state-of-the-art models, and can provide valuable educational interpretability for applications such as precise learning resource recommendation and personalized learning tutoring in intelligent tutoring systems.

翻译：智能学习诊断是智能辅导系统的关键引擎，旨在评估学习者当前知识掌握状态并预测其未来学习表现。传统学习方法面临的核心挑战在于难以兼顾诊断精度与可解释性。尽管现有基于心理测量的学习方法通过认知参数提供了部分领域解释，但其浅层结构对大规模学习数据的建模能力不足。基于深度学习的学习诊断方法虽提升了学习表现预测精度，但其固有的黑箱特性导致可解释性缺失，使得教育应用中的诊断结果缺乏可信度。为解决上述问题，本文提出的统一可解释智能学习诊断（UIILD）框架，融合了深度学习的强大表征学习能力与心理测量的可解释性，在实现更优学习预测性能的同时，从认知参数、学习者-资源响应网络及自注意力机制权重三个维度提供可解释性。在该框架下，本文进一步提出双通道学习诊断机制LDM-ID与三通道学习诊断机制LDM-HMI。在两个真实数据集与一个仿真数据集上的实验表明，相比现有最优模型，本方法在预测学习者表现方面具有更高精度，并能针对智能辅导系统中的精准学习资源推荐与个性化学习辅导等应用提供具有教育价值的可解释性。