Cognitive diagnosis aims to diagnose students' knowledge proficiencies based on their response scores on exam questions, which is the basis of many domains such as computerized adaptive testing. Existing cognitive diagnosis models (CDMs) follow a proficiency-response paradigm, which views diagnostic results as learnable embeddings that are the cause of students' responses and learns the diagnostic results through optimization. However, such a paradigm can easily lead to unidentifiable diagnostic results and the explainability overfitting problem, which is harmful to the quantification of students' learning performance. To address these problems, we propose a novel identifiable cognitive diagnosis framework. Specifically, we first propose a flexible diagnostic module which directly diagnose identifiable and explainable examinee traits and question features from response logs. Next, we leverage a general predictive module to reconstruct response logs from the diagnostic results to ensure the preciseness of the latter. We furthermore propose an implementation of the framework, i.e., ID-CDM, to demonstrate the availability of the former. Finally, we demonstrate the identifiability, explainability and preciseness of diagnostic results of ID-CDM through experiments on four public real-world datasets.

翻译：认知诊断旨在根据学生在试题上的作答得分来诊断其知识掌握程度，这是计算机自适应测试等多个领域的基础。现有认知诊断模型（CDMs）遵循能力-作答范式，将诊断结果视为可学习的嵌入向量，将其视为学生作答的原因，并通过优化来学习诊断结果。然而，这种范式容易导致诊断结果不可辨识以及可解释性过拟合问题，这对量化学生学习表现有害。为解决这些问题，我们提出了一种新颖的可辨识认知诊断框架。具体而言，我们首先提出一个灵活的诊断模块，该模块可直接从作答日志中辨识出可辨识且可解释的考生特质和试题特征。其次，我们利用一个通用预测模块从诊断结果中重构作答日志，以确保后者的精确性。此外，我们提出该框架的一个具体实现，即ID-CDM，以验证其可行性。最后，通过在四个公开真实数据集上的实验，我们证明了ID-CDM诊断结果的可辨识性、可解释性和精确性。