jeehp Search


J Educ Eval Health Prof > Epub ahead of print
J Educ Eval Health Prof. 2020; 17: 35.
Published online November 17, 2020.
[Epub ahead of print]
Estimation of item parameters and examinees’ mastery probability in each domain of the Korean medical licensing examination using deterministic inputs, noisy and gate(DINA) model.
Younyoung Choi1  , Dong Gi Seo2 
1Department of Adolescent Coaching Counseling, Hanyang Cyber University, Seoul, Korea
2Department of Psychology in College of Social Science & Hallym Applied Psychology Institute, Hallym University, Chuncheon, Korea
Correspondence  Dong Gi Seo ,Email:
Submitted: November 9, 2020  Accepted after revision: November 17, 2020
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.
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.
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
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.
Keywords: Diagnostics Classification Model, DINA model, Large-scale Assessment, Classification and Learning
Share :
Facebook Twitter Linked In Google+
METRICS Graph View
  • 0 Crossref
  • 0 Scopus
  • 232 View
  • 5 Download
We recommend

Editorial Office
Institute of Medical Education, College of Medicine, Hallym Unversity, Hallymdaehak-gil 1, Chuncheon 24252, Korea
TEL: +82-33-248-2652   

Copyright © 2020 by Korea Health Personnel Licensing Examination Institute. All rights reserved.

Developed in M2community

Close layer
prev next