Journal of Educational Evaluation for Health Professions

Software report

Introduction to the LIVECAT web-based computerized adaptive testing platform: Dong Gi Seo, Jeongwook Choi; J Educ Eval Health Prof. 2020;17:27. Published online September 29, 2020; DOI: https://doi.org/10.3352/jeehp.2020.17.27

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This study introduces LIVECAT, a web-based computerized adaptive testing platform. This platform provides many functions, including writing item content, managing an item bank, creating and administering a test, reporting test results, and providing information about a test and examinees. The LIVECAT provides examination administrators with an easy and flexible environment for composing and managing examinations. It is available at http://www.thecatkorea.com/. Several tools were used to program LIVECAT, as follows: operating system, Amazon Linux; web server, nginx 1.18; WAS, Apache Tomcat 8.5; database, Amazon RDMS—Maria DB; and languages, JAVA8, HTML5/CSS, Javascript, and jQuery. The LIVECAT platform can be used to implement several item response theory (IRT) models such as the Rasch and 1-, 2-, 3-parameter logistic models. The administrator can choose a specific model of test construction in LIVECAT. Multimedia data such as images, audio files, and movies can be uploaded to items in LIVECAT. Two scoring methods (maximum likelihood estimation and expected a posteriori) are available in LIVECAT and the maximum Fisher information item selection method is applied to every IRT model in LIVECAT. The LIVECAT platform showed equal or better performance compared with a conventional test platform. The LIVECAT platform enables users without psychometric expertise to easily implement and perform computerized adaptive testing at their institutions. The most recent LIVECAT version only provides a dichotomous item response model and the basic components of CAT. Shortly, LIVECAT will include advanced functions, such as polytomous item response models, weighted likelihood estimation method, and content balancing method.

Citations

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Corrigendum

Funding information of the article entitled “Post-hoc simulation study of computerized adaptive testing for the Korean Medical Licensing Examination”: Dong Gi Seo, Jeongwook Choi; J Educ Eval Health Prof. 2018;15:27. Published online December 4, 2018; DOI: https://doi.org/10.3352/jeehp.2018.15.27 [Epub ahead of print]; Corrects: J Educ Eval Health Prof 2018;15(0):14

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Research article

Post-hoc simulation study of computerized adaptive testing for the Korean Medical Licensing Examination: Dong Gi Seo, Jeongwook Choi; J Educ Eval Health Prof. 2018;15:14. Published online May 17, 2018; DOI: https://doi.org/10.3352/jeehp.2018.15.14; Correction in: J Educ Eval Health Prof 2018;15(0):27

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Abstract PDF Supplementary Material

Purpose
Computerized adaptive testing (CAT) has been adopted in licensing examinations because it improves the efficiency and accuracy of the tests, as shown in many studies. This simulation study investigated CAT scoring and item selection methods for the Korean Medical Licensing Examination (KMLE).
Methods
This study used a post-hoc (real data) simulation design. The item bank used in this study included all items from the January 2017 KMLE. All CAT algorithms for this study were implemented using the ‘catR’ package in the R program.
Results
In terms of accuracy, the Rasch and 2-parametric logistic (PL) models performed better than the 3PL model. The ‘modal a posteriori’ and ‘expected a posterior’ methods provided more accurate estimates than maximum likelihood estimation or weighted likelihood estimation. Furthermore, maximum posterior weighted information and minimum expected posterior variance performed better than other item selection methods. In terms of efficiency, the Rasch model is recommended to reduce test length.
Conclusion
Before implementing live CAT, a simulation study should be performed under varied test conditions. Based on a simulation study, and based on the results, specific scoring and item selection methods should be predetermined.

Citations

Citations to this article as recorded by

Presidential address: improving item validity and adopting computer-based testing, clinical skills assessments, artificial intelligence, and virtual reality in health professions licensing examinations in Korea
Hyunjoo Pai
Journal of Educational Evaluation for Health Professions.2023; 20: 8. CrossRef
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Lingling Xu, Zhehan Jiang, Yuting Han, Haiying Liang, Jinying Ouyang
Perspectives on Medical Education.2023;[Epub] CrossRef
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T. Petra Rausch-Koster, Michiel A. J. Luijten, Frank D. Verbraak, Ger H. M. B. van Rens, Ruth M. A. van Nispen
Translational Vision Science & Technology.2022; 11(11): 14. CrossRef
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 Examinat
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Journal of Educational Evaluation for Health Professions.2021; 18: 15. CrossRef
Linear programming method to construct equated item sets for the implementation of periodical computer-based testing for the Korean Medical Licensing Examination
Dong Gi Seo, Myeong Gi Kim, Na Hui Kim, Hye Sook Shin, Hyun Jung Kim
Journal of Educational Evaluation for Health Professions.2018; 15: 26. CrossRef
Funding information of the article entitled “Post-hoc simulation study of computerized adaptive testing for the Korean Medical Licensing Examination”
Dong Gi Seo, Jeongwook Choi
Journal of Educational Evaluation for Health Professions.2018; 15: 27. CrossRef
Updates from 2018: Being indexed in Embase, becoming an affiliated journal of the World Federation for Medical Education, implementing an optional open data policy, adopting principles of transparency and best practice in scholarly publishing, and appreci
Sun Huh
Journal of Educational Evaluation for Health Professions.2018; 15: 36. CrossRef

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