PURPOSE Deterministic inputs, noisy and gate (DINA) model is one of the promising statistical means for providing useful diagnostic information about a student' level of achievement. Diagnostics information is core element for improving learning instead of selection. Educators often want to be provided with diagnostic information which how a given examinees did on each content strand, called diagnostic profiles. The purpose of this paper is to classify examinees in different content domains using the DINA model. METHODS This paper analyzed data from the Korean medical licensing examination (KMLE) with 360 items and 3259 examinees. The application study estimate examinees parameters as well as item characteristics. The guessing and slipping parameters of each item were estimated. DINA model was conducted as a statistical analysis. RESULTS The output table shows the examples of some items, which can be used for the check of item quality. In addition, the probabilities of being mastery at each content domain were estimated, which indicates the mastery profile of each examinee. Classifications accuracy for 8 contents ranged from .849 to .972 and classification consistency for 8 contents ranged from .839 to .994. As a result, classification reliability in a CDM was very high for 8 contents in KMLE. CONCLUSION This mastery profile can be useful diagnostic information for each examinee in terms of the content domains of KMLE. The master profile from KMLE provides each examinee's mastery profile in terms of each content domain. The individual mastery profile allows educators and examinees to understand that which domain(s) should be improved for mastering all domains in KMLE. In addition, the results found that all items are reasonable level with respect to item parameters character.

中文翻译：

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使用确定性输入、噪声和门 (DINA) 模型估计韩国医疗许可考试每个领域的项目参数和考生掌握概率。

目的 确定性输入、噪声和门 (DINA) 模型是一种很有前途的统计方法，可提供有关学生成绩水平的有用诊断信息。诊断信息是改进学习而不是选择的核心要素。教育工作者通常希望获得给定考生在每个内容链上的表现的诊断信息，称为诊断资料。本文的目的是使用 DINA 模型对不同内容领域的考生进行分类。方法本文分析了来自韩国医学执照考试 (KMLE) 的 360 个项目和 3259 名考生的数据。应用研究估计检查参数以及项目特征。估计每个项目的猜测和滑动参数。DINA 模型作为统计分析进行。结果 输出表显示了一些项目的示例，可用于项目质量的检查。此外，还估计了每个内容域的掌握概率，这表明了每个考生的掌握情况。8 项内容的分类准确度范围为 0.849 至 .972，8 项内容的分类一致性范围为 0.839 至 .994。因此，对于 KMLE 中的 8 个内容，CDM 中的分类可靠性非常高。结论 根据 KMLE 的内容域，此掌握概况可为每位考生提供有用的诊断信息。KMLE 的主配置文件提供了每个考生在每个内容域方面的掌握配置文件。个人掌握概况允许教育者和考生了解哪些域应该改进以掌握 KMLE 中的所有域。此外，结果发现所有项目在项目参数特征方面均处于合理水平。