Predicting the outcomes of the Korean national accreditation system for higher education institutions: a method using disclosure data for outsiders

Abstract For outsiders to higher education institutions (HEIs) in South Korea, predicting the outcomes of International Education Quality Assurance System (IEQAS)—a Korean institutional accreditation system for HEIs—is challenging. The annual IEQAS has been conducted behind closed doors; assessment process is confidential, and there little access data public individuals. However, many stakeholders, such as overseas applicants HEIs, may want predict whether particular universities will pass upcoming IEQAS. Hence, we sought an alternative method a binary result by utilizing disclosure that government published. To best outcomes, mapped out threefold discrete model combining logistic regression, discriminant analysis, neural network. We collected information disclosed Ministry 2019 on 138 private HEIs then analyzed secondary dataset line with ensures generalizability. Results showed (i) three investment factors, one school operations factor appeared key predictors among tested indices; (ii) cost per student within proved be most crucial element; (iii) while leveraging turned reliable, network’s predictive accuracy was than those reported using regression analysis. By processing publicly available data, our self-study effectively assist it can also used diagnostic, prior accreditation, local other nations check their preparedness likelihood success similar contexts.