Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model
Purpose The deterministic inputs, noisy “and” gate (DINA) model is a promising statistical method for providing useful diagnostic information about students’ level of achievement, as educators often want to receive diagnostic information on how examinees did on each content strand, which is referred...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Korea Health Personnel Licensing Examination Institute
2020-11-01
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| Series: | Journal of Educational Evaluation for Health Professions |
| Subjects: | |
| Online Access: | http://www.jeehp.org/upload/jeehp-17-35.pdf |
| _version_ | 1797760569733808128 |
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| author | Younyoung Choi Dong Gi Seo |
| author_facet | Younyoung Choi Dong Gi Seo |
| author_sort | Younyoung Choi |
| collection | DOAJ |
| description | Purpose The deterministic inputs, noisy “and” gate (DINA) model is a promising statistical method for providing useful diagnostic information about students’ level of achievement, as educators often want to receive diagnostic information on how examinees did on each content strand, which is referred to as a diagnostic profile. The purpose of this paper was to classify examinees of the Korean Medical Licensing Examination (KMLE) in different content domains using the DINA model. Methods This paper analyzed data from the KMLE, with 360 items and 3,259 examinees. An application study was conducted to estimate examinees’ parameters and item characteristics. The guessing and slipping parameters of each item were estimated, and statistical analysis was conducted using the DINA model. Results The output table shows examples of some items that can be used to check item quality. The probabilities of mastery of each content domain were also estimated, indicating the mastery profile of each examinee. The classification accuracy and consistency for 8 content domains ranged from 0.849 to 0.972 and from 0.839 to 0.994, respectively. As a result, the classification reliability of the diagnostic classification model was very high for the 8 content domains of the KMLE. Conclusion This mastery profile can provide useful diagnostic information for each examinee in terms of each content domain of the KMLE. Individual mastery profiles allow educators and examinees to understand which domain(s) should be improved in order to master all domains in the KMLE. In addition, all items showed reasonable results in terms of item parameters. |
| first_indexed | 2024-03-12T19:00:29Z |
| format | Article |
| id | doaj.art-92177f2198224a0ba2c6336480bd4042 |
| institution | Directory Open Access Journal |
| issn | 1975-5937 |
| language | English |
| last_indexed | 2024-03-12T19:00:29Z |
| publishDate | 2020-11-01 |
| publisher | Korea Health Personnel Licensing Examination Institute |
| record_format | Article |
| series | Journal of Educational Evaluation for Health Professions |
| spelling | doaj.art-92177f2198224a0ba2c6336480bd40422023-08-02T06:41:30ZengKorea Health Personnel Licensing Examination InstituteJournal of Educational Evaluation for Health Professions1975-59372020-11-011710.3352/jeehp.2020.17.35383Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) modelYounyoung Choi0Dong Gi Seo1Department of Adolescent Coaching Counseling, Hanyang Cyber University, Seoul, KoreaDepartment of Psychology in College of Social Science & Hallym Applied Psychology Institute, Hallym University, Chuncheon, KoreaPurpose The deterministic inputs, noisy “and” gate (DINA) model is a promising statistical method for providing useful diagnostic information about students’ level of achievement, as educators often want to receive diagnostic information on how examinees did on each content strand, which is referred to as a diagnostic profile. The purpose of this paper was to classify examinees of the Korean Medical Licensing Examination (KMLE) in different content domains using the DINA model. Methods This paper analyzed data from the KMLE, with 360 items and 3,259 examinees. An application study was conducted to estimate examinees’ parameters and item characteristics. The guessing and slipping parameters of each item were estimated, and statistical analysis was conducted using the DINA model. Results The output table shows examples of some items that can be used to check item quality. The probabilities of mastery of each content domain were also estimated, indicating the mastery profile of each examinee. The classification accuracy and consistency for 8 content domains ranged from 0.849 to 0.972 and from 0.839 to 0.994, respectively. As a result, the classification reliability of the diagnostic classification model was very high for the 8 content domains of the KMLE. Conclusion This mastery profile can provide useful diagnostic information for each examinee in terms of each content domain of the KMLE. Individual mastery profiles allow educators and examinees to understand which domain(s) should be improved in order to master all domains in the KMLE. In addition, all items showed reasonable results in terms of item parameters.http://www.jeehp.org/upload/jeehp-17-35.pdfdiagnostics classification modeldina modellarge-scale assessmentclassification and learning |
| spellingShingle | Younyoung Choi Dong Gi Seo Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model Journal of Educational Evaluation for Health Professions diagnostics classification model dina model large-scale assessment classification and learning |
| title | Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model |
| title_full | Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model |
| title_fullStr | Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model |
| title_full_unstemmed | Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model |
| title_short | Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model |
| title_sort | estimation of item parameters and examinees mastery probability in each domain of the korean medical licensing examination using a deterministic inputs noisy and gate dina model |
| topic | diagnostics classification model dina model large-scale assessment classification and learning |
| url | http://www.jeehp.org/upload/jeehp-17-35.pdf |
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