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
Younyoung Choi, Dong Gi Seo · Harvard Dataverse · 2020
The purpose of this paper was to classify examinees of the Korean Medical Licensing Examination (KMLE) in different content domains using the DINA model. 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.