Predicting the Outcomes of the Korean National Accreditation System for Higher Education Institutions: A Method Using Disclosure Data for Outsiders

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aut.relation.issue4en_NZ
aut.relation.journalAsia Pacifc Education Reviewen_NZ
aut.relation.volume22en_NZ
aut.researcherLee, Dong
dc.contributor.authorLee, DD-Hen_NZ
dc.contributor.authorCho, SJ
dc.date.accessioned2021-10-28T02:24:10Z
dc.date.available2021-10-28T02:24:10Z
dc.date.copyright2021-10-26en_NZ
dc.date.issued2021-10-26en_NZ
dc.description.abstractFor outsiders to higher education institutions (HEIs) in South Korea, predicting the outcomes of the International Education Quality Assurance System (IEQAS)—a Korean institutional accreditation system for HEIs—is challenging. The annual IEQAS accreditation has been conducted behind closed doors; the assessment process is confidential, and there is little access to the data for the public and individuals. However, many stakeholders, such as overseas applicants to Korean HEIs, may want to predict whether particular universities will pass the upcoming IEQAS. Hence, we sought an alternative method for the outsiders to predict a binary result of the IEQAS accreditation by utilizing disclosure data that the Korean government has published. To best predict the outcomes, we mapped out a threefold discrete model combining logistic regression, discriminant analysis, and neural network. We collected the information disclosed by the Ministry of Education in 2019 on 138 Korean private HEIs and then analyzed the secondary public dataset in line with the discrete method that ensures generalizability. Results showed (i) three education investment factors, and one school operations factor appeared as key predictors among the tested indices; (ii) education cost per student within education investment proved to be the most crucial element; and (iii) while leveraging the disclosed data turned out to be reliable, neural network’s predictive accuracy was higher than those reported using logistic regression and discriminant analysis. By processing the publicly available disclosure data, our self-study model may effectively assist in predicting IEQAS outcomes, and it can also be used as a diagnostic, prior to accreditation, by local HEIs in other nations to check their preparedness and likelihood of success within similar contexts.
dc.identifier.citationAsia Pacifc Education Review (2021). https://doi.org/10.1007/s12564-021-09710-z
dc.identifier.doi10.1007/s12564-021-09710-zen_NZ
dc.identifier.issn1598-1037en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14601
dc.publisherSpringer
dc.relation.urihttps://link.springer.com/article/10.1007%2Fs12564-021-09710-z
dc.rightsThis article is licensed under a Creative Commons Attri bution 4.0 International License, which permits use, sharing, adapta tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectNational accreditation systems; The International Education Quality Assurance System (IEQAS); Korean higher education; Discrete analysis model; Disclosure data
dc.titlePredicting the Outcomes of the Korean National Accreditation System for Higher Education Institutions: A Method Using Disclosure Data for Outsidersen_NZ
dc.typeJournal Article
pubs.elements-id442418
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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