Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students' proficiency on knowledge concepts from their response logs. Current works assume each knowledge concept will certainly be tested and covered by multiple exercises. However, whether online or offline courses, it's hardly feasible to completely cover all knowledge concepts in several exercises. Restricted tests lead to undiscovered knowledge deficits, especially untested knowledge concepts(UKCs). In this paper, we propose a novel \underline{Dis}entangling Heterogeneous \underline{K}nowledge \underline{C}ognitive \underline{D}iagnosis framework on untested knowledge(DisKCD). Specifically, we leverage course grades, exercise questions, and resources to learn the potential representations of students, exercises, and knowledge concepts. In particular, knowledge concepts are disentangled into tested and untested based on the limiting actual exercises. We construct a heterogeneous relation graph network via students, exercises, tested knowledge concepts(TKCs), and UKCs. Then, through a hierarchical heterogeneous message-passing mechanism, the fine-grained relations are incorporated into the embeddings of the entities. Finally, the embeddings will be applied to multiple existing cognitive diagnosis models to infer students' proficiency on UKCs. Experimental results on real-world datasets show that the proposed model can effectively improve the performance of the task of diagnosing students' proficiency on UKCs. Our anonymous code is available at https://anonymous.4open.science/r/DisKCD.

翻译：认知诊断是学习评估中一项基础且关键的任务，旨在从学生的答题记录中推断其对知识概念的掌握程度。现有研究通常假设每个知识概念都必然会被测试，并由多个习题覆盖。然而，无论是在线还是线下课程，都很难通过有限习题完全覆盖所有知识概念。受限的测试会导致未被发现的知识缺陷，尤其是那些未被测试的知识概念。本文提出一种新颖的面向未测试知识的解耦异构知识认知诊断框架。具体而言，我们利用课程成绩、习题题目和资源来学习学生、习题和知识概念的潜在表示。特别地，根据实际有限的习题，知识概念被解耦为已测试和未测试两类。我们通过学生、习题、已测试知识概念和未测试知识概念构建了一个异构关系图网络。随后，通过一种分层的异构消息传递机制，细粒度关系被整合到各实体的嵌入表示中。最后，这些嵌入表示将被应用于多个现有的认知诊断模型，以推断学生对未测试知识概念的掌握程度。在真实数据集上的实验结果表明，所提模型能有效提升诊断学生对未测试知识概念掌握程度这一任务的性能。我们的匿名代码可在 https://anonymous.4open.science/r/DisKCD 获取。