lee_2025_nursing_exam

5,850 responses from 117 respondents to 50 items.

About this table

DescriptionItem-level binary (correct/incorrect) responses on a 50-item women's health nursing examination in Korea; 117 examinees including 111 nursing students and 6 generative AI platforms
ReferenceLee, T.K., Jeong, G.H. (2025). Comparing generative artificial intelligence platforms and nursing student performance on a women's health nursing examination in Korea: a Rasch model approach. Journal of Educational Evaluation for Health Professions, 22, 23. https://doi.org/10.3352/jeehp.2025.22.23
DOI10.3352/jeehp.2025.22.23
LicenceCC0 1.0
Source datahttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PWV6H2

Size and shape

Responses5,850
Respondents117
Items50
Response categories2
Responses per respondent50
Responses per item117
Density1
LongitudinalFALSE

Classification

sampleEducational
measurement toolTest
item formatLikert Scale/selected response
primary language(s)kor

Item text

This table has item text in the IRW: the wording administered to respondents, not just the response codes.

Instrument2023 Women's Health Nursing final examination, 4th-year first semester, Korean baccalaureate nursing programme
Mean words per item39.320
Mean characters per item276.720
Mean characters per response8
Flesch-Kincaid grade level11.301

Codebook

This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, lee_2025_nursing_exam.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.

ColumnWhat it isSource column
IRW standard columns
idIRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task).ID
itemIRW standard Item identifier.made in the build script
respIRW standard Response value.made in the build script

What item codes and covariate values stand for is in the codebook released with the source data.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("lee_2025_nursing_exam")
# Python
pip install irw

import irw
df = irw.fetch("lee_2025_nursing_exam")

How to cite

@article{Ko_2025, title={Comparing generative artificial intelligence platforms and nursing student performance on a women’s health nursing examination in Korea: a Rasch model approach}, volume={22}, ISSN={1975-5937}, url={http://dx.doi.org/10.3352/jeehp.2025.22.23}, DOI={10.3352/jeehp.2025.22.23}, journal={Journal of Educational Evaluation for Health Professions}, publisher={Korea Health Personnel Licensing Examination Institute}, author={Ko, Eun Jeong and Lee, Tae Kyung and Jeong, Geum Hee}, year={2025}, month=Sept, pages={23} }

Version and provenance

IRW versionv566
Redivis datasetitem_response_warehouse_3 v13.0
Redivis dataset DOI10.57761/gpg7-sg87
Manifest pin for this IRW versionv13.0
Metadata sourceirw_meta v43.0