Cognitive diagnosis is a crucial task in computational education, aimed at evaluating students' proficiency levels across various knowledge concepts through exercises. Current models, however, primarily rely on students' answered exercises, neglecting the complex and rich information contained in un-interacted exercises. While recent research has attempted to leverage the data within un-interacted exercises linked to interacted knowledge concepts, aiming to address the long-tail issue, these studies fail to fully explore the informative, un-interacted exercises related to broader knowledge concepts. This oversight results in diminished performance when these models are applied to comprehensive datasets. In response to this gap, we present the Collaborative-aware Mixed Exercise Sampling (CMES) framework, which can effectively exploit the information present in un-interacted exercises linked to un-interacted knowledge concepts. Specifically, we introduce a novel universal sampling module where the training samples comprise not merely raw data slices, but enhanced samples generated by combining weight-enhanced attention mixture techniques. Given the necessity of real response labels in cognitive diagnosis, we also propose a ranking-based pseudo feedback module to regulate students' responses on generated exercises. The versatility of the CMES framework bolsters existing models and improves their adaptability. Finally, we demonstrate the effectiveness and interpretability of our framework through comprehensive experiments on real-world datasets.

翻译：认知诊断是计算教育中的关键任务，旨在通过习题评估学生在各知识概念上的掌握水平。然而，现有模型主要依赖学生已交互的习题，忽略了未交互习题中蕴含的复杂而丰富的信息。尽管近期研究尝试利用与已交互知识概念相关的未交互习题数据以解决长尾问题，但这些方法未能充分探索与更广泛知识概念相关的信息性未交互习题，导致模型在综合数据集上的性能下降。为弥补这一不足，我们提出了协作感知混合习题采样（CMES）框架，该框架能有效利用与未交互知识概念相关的未交互习题中的信息。具体而言，我们引入了一种新型通用采样模块，其中训练样本不再是原始数据片段，而是通过结合权重增强注意力混合技术生成的增强样本。鉴于认知诊断需要真实响应标签，我们还提出了一种基于排名的伪反馈模块，用于调节学生在生成习题上的响应。CMES框架的通用性增强了现有模型并提升了其适应性。最后，通过在实际数据集上的综合实验，我们展示了所提框架的有效性与可解释性。