Learners sharing similar implicit cognitive states often display comparable observable problem-solving performances. Leveraging collaborative connections among such similar learners proves valuable in comprehending human learning. Motivated by the success of collaborative modeling in various domains, such as recommender systems, we aim to investigate how collaborative signals among learners contribute to the diagnosis of human cognitive states (i.e., knowledge proficiency) in the context of intelligent education. The primary challenges lie in identifying implicit collaborative connections and disentangling the entangled cognitive factors of learners for improved explainability and controllability in learner Cognitive Diagnosis (CD). However, there has been no work on CD capable of simultaneously modeling collaborative and disentangled cognitive states. To address this gap, we present Coral, a Collaborative cognitive diagnosis model with disentangled representation learning. Specifically, Coral first introduces a disentangled state encoder to achieve the initial disentanglement of learners' states. Subsequently, a meticulously designed collaborative representation learning procedure captures collaborative signals. It dynamically constructs a collaborative graph of learners by iteratively searching for optimal neighbors in a context-aware manner. Using the constructed graph, collaborative information is extracted through node representation learning. Finally, a decoding process aligns the initial cognitive states and collaborative states, achieving co-disentanglement with practice performance reconstructions. Extensive experiments demonstrate the superior performance of Coral, showcasing significant improvements over state-of-the-art methods across several real-world datasets. Our code is available at https://github.com/bigdata-ustc/Coral.

翻译：具有相似隐性认知状态的学习者往往表现出可比较的可观察问题解决表现。利用这些相似学习者之间的协作关系对于理解人类学习过程具有重要价值。受推荐系统等多个领域中协作建模成功的启发，本研究旨在探究在智能教育背景下，学习者之间的协作信号如何促进人类认知状态（即知识熟练度）的诊断。主要挑战在于识别隐性的协作关联，并解耦学习者纠缠的认知因素，以提升学习者认知诊断（CD）的可解释性和可控性。然而，目前尚无能够同时建模协作关系与解耦认知状态的CD方法。为填补这一空白，我们提出了Coral——一种基于解耦表征学习的协作式认知诊断模型。具体而言，Coral首先引入解耦状态编码器，实现学习者认知状态的初步解耦。随后，通过精心设计的协作表征学习过程捕获协作信号：该方法以情境感知的方式迭代搜索最优邻近节点，动态构建学习者协作图。基于所构建的图结构，通过节点表征学习提取协作信息。最后，通过解码过程将初始认知状态与协作状态进行对齐，结合练习表现重建实现协同解耦。大量实验证明Coral具有卓越性能，在多个真实数据集上相比现有最先进方法取得显著提升。我们的代码公开于https://github.com/bigdata-ustc/Coral。