The study presents an exploratory graphical modeling approach for evaluating local item dependency within cognitively diagnostic classification models (DCMs). Current approaches to modeling local dependence require known item structure and have limited utility when such information is not available. In this study, we propose an exploratory approach to modeling local dependence so that items' own interactions can be revealed without dependency specification. The new framework is developed by integrating a Markov network into a generalized DCM. The framework unveils item interactions while performing regular cognitive diagnosis within a unified scheme. The inference on the model parameters is made on the regularized pseudo-likelihood and is implemented by an EM algorithm. Numerical experimentation from Monte Carlo simulation suggests that the proposed framework adequately recovers generating parameters and yields reliable standard error estimates. Compared with the regular DCM, the new model produced more accurate item parameter estimates as items exhibit local dependence. The study demonstrates application of the model using two real assessment data and discusses practical benefits of modeling local dependence.

翻译：本研究提出了一种探索性图建模方法，用于评估认知诊断分类模型中的局部项目依赖性。当前建模局部依赖的方法要求已知项目结构，在缺乏此类信息时应用有限。本研究中，我们提出了一种探索性方法，以在不依赖预先指定的依赖关系的情况下，揭示项目间的相互作用。通过将马尔可夫网络整合到广义认知诊断模型中，开发了这一新框架。该框架在统一的体系内进行常规认知诊断的同时，揭示项目间的相互作用。模型参数的推断基于正则化伪似然，并通过期望最大化算法实现。蒙特卡罗模拟的数值实验表明，所提出的框架能有效恢复生成参数，并提供可靠的标准误差估计。与常规认知诊断模型相比，当项目表现出局部依赖时，新模型能更准确地估计项目参数。研究通过两个真实评估数据集展示了模型的应用，并讨论了建模局部依赖的实际益处。