Multilevel network item response modelling for discovering differences between innovation and regular school systems in Korea

Ick Hoon Jin, Minjeong Jeon, Michael Schweinberger, Jonghyun Yun, Lizhen Lin

Research output: Contribution to journalArticlepeer-review


The innovation school system in South Korea has been developed in response to the traditional high-pressure school system in South Korea, with a view to cultivate a bottom-up and student-centred educational culture. Despite its ambitious goals, questions have been raised about the success of the innovation school system. Leveraging data from the Gyeonggi Education Panel Study along with advances in the statistical analysis of network data and educational data, we compare the two school systems in more depth. We find that some schools are indeed different from others, and those differences are not detected by conventional multilevel models. Having said that, we do not find much evidence that the innovation school system differs from the regular school system in terms of self-reported mental well-being, although we do detect differences among some schools that appear to be unrelated to the school system.

Original languageEnglish
Pages (from-to)1225-1244
Number of pages20
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Issue number5
Publication statusPublished - 2022 Nov

Bibliographical note

Funding Information:
We are grateful to two anonymous referees, the Associate Editor and the Editor for constructive comments and suggestions that have led to substantial improvements of the manuscript. Ick Hoon Jin was partially supported by the Yonsei University Research Fund of 2019‐22‐0210 and by Basic Science Research Program through the National Research Foundation of Korea (NRF 2020R1A2C1A01009881). Michael Schweinberger was partially supported by NSF awards DMS‐1513644 and DMS‐1812119 and ARO award W911NF‐21‐1‐0237 (75549‐NS). Lizhen Lin would like to acknowledge the general support from NSF grants IIS‐1663870 and DMS‐1654579 and Darpa grant N66001‐17‐1‐4041.

Publisher Copyright:
© 2022 Royal Statistical Society.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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