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：

---

使用确定性输入，噪声和门（DINA）模型估算韩国医学许可考试各个领域中的项目参数和应试者的掌握概率。

目的确定性输入，噪声和门（DINA）模型是一种有前途的统计手段之一，可提供有关学生成绩水平的有用诊断信息。诊断信息是改善学习而非选择的核心要素。教育人员通常希望获得诊断信息，即给定考生在每个内容链上的表现方式，称为诊断配置文件。本文的目的是使用DINA模型对不同内容领域中的应试者进行分类。方法本文分析了来自韩国医学许可考试（KMLE）的数据，其中包括360项和3259名考生。应用研究估计考生参数以及项目特征。估计每个项目的猜测和滑移参数。进行DINA模型作为统计分析。结果输出表显示了某些项目的示例，可用于检查项目质量。另外，估计了在每个内容域上掌握的概率，这表明了每个应试者的掌握状况。8个内容的分类准确性介于.849至.972之间，8个内容的分类一致性介于.839至.994之间。结果，对于KMLE中的8个内容，CDM中的分类可靠性非常高。结论就KMLE的内容域而言，该掌握概况对于每个应试者都是有用的诊断信息。KMLE的主资料提供了每个内容域中每个考生的掌握资料。个体的掌握概况可让教育者和考生了解为掌握KMLE中的所有领域而应改进的领域。此外，结果发现，所有项目相对于项目参数特征都是合理的水平。