摘要： 认知诊断通过学生作答记录估计知识概念掌握状态，为个性化教学提供依据。现有方法多关注学生、习题与知识概念之间的静态关系，对时间上下文与潜在情感因素的联合建模不足，容易将短期作答波动混入认知状态估计；情感信息通常作为附加特征参与预测，缺少面向具体交互的动态调制机制，且部分数据集缺乏情感标签。针对上述问题，提出情感调制与时间感知的神经张量环认知诊断模型（AT-NTRCD），并构建适用于无情感标签场景的迁移变体T-AT-NTRCD。 AT-NTRCD将学生、习题与时间映射至低秩张量环空间，利用Q矩阵约束保持习题表征与知识概念的语义一致性；采用门控循环单元编码历史时间嵌入序列，通过聚合相邻时间片信息，生成时间上下文表征；随后，根据学生知识掌握程度与习题概念难度的差异构建认知挑战表征，结合学生情感倾向与时间上下文推断交互级多维情感状态；模型将情感状态参数化为情感调制核切片，与学生核、习题核和时间核构成闭合张量环交互结构，再通过编码—解码神经网络提取非线性交互特征，最终实现对学生作答表现的精准预测。为提高情感表征的可靠性并扩展模型适用范围，本文针对不同情感标签条件设计训练策略：在具有情感伪标签的数据集上，引入辅助监督约束情感表征学习；在无情感标签的数据集上，构建迁移变体T-AT-NTRCD，复用并固定源域情感生成网络，在目标域学习学生情感倾向映射、认知挑战投影及适配目标张量环秩的情感基矩阵，实现无情感监督条件下的动态情感调制。 在多个真实教育数据集上的实验表明，相较最优基线，AT-NTRCD在ASSIST12上的AUC和ACC分别提高0.44和0.40个百分点，RMSE相对降低0.55%；在ASSIST17上分别提高1.90和1.23个百分点，RMSE相对降低11.87%。在无情感标签的Junyi上，T-AT-NTRCD的AUC和ACC分别提高3.53和2.39个百分点，RMSE相对降低19.69%。消融实验进一步验证了时间上下文编码、情感调制、Q矩阵约束和神经张量环交互的有效性。综上，所提模型通过低秩高阶交互与交互级情感调制改善作答预测，并将情感建模扩展至无显式情感标签场景。

Abstract: Cognitive diagnosis estimates students’ mastery of knowledge concepts from their response records, providing a basis for personalized instruction. Existing methods primarily focus on static relationships among students, exercises, and knowledge concepts, with limited joint modeling of temporal context and latent affective factors. Consequently, short-term response fluctuations may be incorporated into estimates of cognitive states. Affective information is typically used as an additional predictive feature, without a dynamic modulation mechanism tailored to individual interactions, and some datasets lack affective labels. To address these issues, we propose the Affective-Temporal Neural Tensor Ring Cognitive Diagnosis model (AT-NTRCD) and develop a transfer variant, T-AT-NTRCD, for settings without affective labels. AT-NTRCD maps students, exercises, and time into a low-rank tensor ring space and uses Q-matrix constraints to maintain semantic consistency between exercise representations and knowledge concepts. A gated recurrent unit encodes historical time embedding sequences and aggregates information from adjacent time slices to generate temporal context representations. Cognitive challenge representations are then constructed from the discrepancy between students’ knowledge mastery and exercise difficulty at the concept level. These representations are combined with students’ affective tendencies and temporal context to infer multidimensional affective states for individual interactions. The model parameterizes these affective states as affective modulation core slices, which form a closed tensor ring interaction structure together with the student, exercise, and temporal cores. An encoder–decoder neural network then extracts nonlinear interaction features to accurately predict student response outcomes. To improve the reliability of affective representations and extend the model’s applicability, training strategies are designed for different affective label conditions. For datasets with affective pseudo-labels, auxiliary supervision constrains affective representation learning. For datasets without affective labels, T-AT-NTRCD reuses and freezes the source-domain affective generation network while learning student affective tendency mappings, cognitive challenge projections, and affective basis matrices compatible with the target tensor ring rank in the target domain. This enables dynamic affective modulation without affective supervision in the target domain. Experiments on multiple real-world educational datasets show that, compared with the best-performing baselines, AT-NTRCD improves AUC and ACC by 0.44 and 0.40 percentage points, respectively, and achieves a relative RMSE reduction of 0.55% on ASSIST12. On ASSIST17, the corresponding improvements in AUC and ACC are 1.90 and 1.23 percentage points, with a relative RMSE reduction of 11.87%. On Junyi, which lacks affective labels, T-AT-NTRCD improves AUC and ACC by 3.53 and 2.39 percentage points, respectively, and achieves a relative RMSE reduction of 19.69%. Ablation studies further confirm the effectiveness of temporal context encoding, affective modulation, Q-matrix constraints, and neural tensor ring interactions. In summary, the proposed models improve response prediction through low-rank high-order interactions and interaction-level affective modulation, and extend affective modeling to settings without explicit affective labels.