The accuracy and consistency of mastery for each content domain using the Rasch and deterministic inputs, noisy “and” gate diagnostic classification models: a simulation study and a real-world analysis using data from the Korean Medical Licensing Examination

This paper compared the accuracy of high-level attribute mastery between deterministic inputs, noisy “and” gate (DINA) and Rasch models, along with sub-scores based on CTT. First, a simulation study explored the effects of attribute length (number of items per attribute) and the correlations among attributes with respect to the accuracy of mastery. Second, a real-data study examined model and item fit and investigated the consistency of mastery for each attribute among the 3 models using the 2017 Korean Medical Licensing Examination with 360 items.本研究对比了确定性输入噪声"与"门模型（deterministic inputs, noisy "and" gate, DINA）、拉希模型（Rasch models）以及基于经典测验理论（Classical Test Theory, CTT）的子得分在高阶属性掌握度判断上的准确率。首先，模拟研究探究了属性长度（单属性对应项目数量）以及属性间相关性对属性掌握度判断准确率的影响。其次，本实证研究采用包含360个项目的2017年韩国医师资格考试数据集，考察了模型与项目拟合优度，并分析了3种模型下各属性掌握情况的一致性。