With the advancement of network technologies, intelligent tutoring systems (ITS) have emerged to deliver increasingly precise and tailored personalized learning services. Cognitive diagnosis (CD) has emerged as a core research task in ITS, aiming to infer learners' mastery of specific knowledge concepts by modeling the mapping between learning behavior data and knowledge states. However, existing research prioritizes model performance enhancement while neglecting the pervasive noise contamination in observed response data, significantly hindering practical deployment. Furthermore, current cognitive diagnosis models (CDMs) rely heavily on researchers' domain expertise for structural design, which fails to exhaustively explore architectural possibilities, thus leaving model architectures' full potential untapped. To address this issue, we propose OSCD, an evolutionary multi-objective One-Shot neural architecture search method for Cognitive Diagnosis, designed to efficiently and robustly improve the model's capability in assessing learner proficiency. Specifically, OSCD operates through two distinct stages: training and searching. During the training stage, we construct a search space encompassing diverse architectural combinations and train a weight-sharing supernet represented via the complete binary tree topology, enabling comprehensive exploration of potential architectures beyond manual design priors. In the searching stage, we formulate the optimal architecture search under heterogeneous noise scenarios as a multi-objective optimization problem (MOP), and develop an optimization framework integrating a Pareto-optimal solution search strategy with cross-scenario performance evaluation for resolution. Extensive experiments on real-world educational datasets validate the effectiveness and robustness of the optimal architectures discovered by our OSCD model for CD tasks.

翻译：随着网络技术的进步，智能导学系统（ITS）已能够提供日益精确和个性化的学习服务。认知诊断（CD）作为ITS中的核心研究任务，旨在通过建模学习行为数据与知识状态之间的映射关系，推断学习者对特定知识概念的掌握程度。然而，现有研究优先考虑模型性能的提升，却忽视了观测响应数据中普遍存在的噪声污染问题，这严重阻碍了其实际部署。此外，当前的认知诊断模型（CDMs）严重依赖研究者的领域专业知识进行结构设计，未能充分探索架构的可能性，从而未能充分挖掘模型架构的潜力。为解决这一问题，我们提出了OSCD，一种用于认知诊断的进化多目标单次神经架构搜索方法，旨在高效且鲁棒地提升模型评估学习者能力水平的能力。具体而言，OSCD通过两个不同的阶段运行：训练阶段和搜索阶段。在训练阶段，我们构建了一个包含多样化架构组合的搜索空间，并训练了一个通过完全二叉树拓扑表示的权重共享超网络，从而能够全面探索超越人工设计先验的潜在架构。在搜索阶段，我们将异构噪声场景下的最优架构搜索问题表述为一个多目标优化问题（MOP），并开发了一个集成帕累托最优解搜索策略与跨场景性能评估的优化框架进行求解。在真实世界教育数据集上的大量实验验证了我们的OSCD模型为CD任务所发现的最优架构的有效性和鲁棒性。