多层次网络项目反应模型用于发现韩国创新与普通学校系统之间的差异

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-06-13 DOI:10.1111/rssc.12569
Ick Hoon Jin, Minjeong Jeon, Michael Schweinberger, Jonghyun Yun, Lizhen Lin
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引用次数: 1

摘要

韩国的创新学校制度是针对韩国传统的高压学校制度而发展起来的,旨在培养一种自下而上、以学生为中心的教育文化。尽管有雄心勃勃的目标,人们还是对创新学校体系的成功提出了质疑。利用京畿道教育小组研究的数据,以及网络数据和教育数据的统计分析的进展,我们更深入地比较了两种学校系统。我们发现一些学校确实与其他学校不同,而这些差异是传统的多层模型所无法检测到的。话虽如此,我们没有发现太多证据表明创新学校系统在自我报告的心理健康方面与普通学校系统不同,尽管我们确实发现了一些学校之间的差异,这些差异似乎与学校系统无关。
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Multilevel network item response modelling for discovering differences between innovation and regular school systems in Korea

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.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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