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运用机器学习和传统统计学方法识别跨文化学生幸福感的主要预测因素。

Identifying the top predictors of student well-being across cultures using machine learning and conventional statistics.

机构信息

Department of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong Kong, Hong Kong, China.

Faculty of Education, University of Macau, Taipa, Macau SAR, China.

出版信息

Sci Rep. 2024 Apr 10;14(1):8376. doi: 10.1038/s41598-024-55461-3.

DOI:10.1038/s41598-024-55461-3
PMID:38600124
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11006657/
Abstract

Alongside academic learning, there is increasing recognition that educational systems must also cater to students' well-being. This study examines the key factors that predict adolescent students' subjective well-being, indexed by life satisfaction, positive affect, and negative affect. Data from 522,836 secondary school students from 71 countries/regions across eight different cultural contexts were analyzed. Underpinned by Bronfenbrenner's bioecological theory, both machine learning (i.e., light gradient-boosting machine) and conventional statistics (i.e., hierarchical linear modeling) were used to examine the roles of person, process, and context factors. Among the multiple predictors examined, school belonging and sense of meaning emerged as the common predictors of the various well-being dimensions. Different well-being dimensions also had distinct predictors. Life satisfaction was best predicted by a sense of meaning, school belonging, parental support, fear of failure, and GDP per capita. Positive affect was most strongly predicted by resilience, sense of meaning, school belonging, parental support, and GDP per capita. Negative affect was most strongly predicted by fear of failure, gender, being bullied, school belonging, and sense of meaning. There was a remarkable level of cross-cultural similarity in terms of the top predictors of well-being across the globe. Theoretical and practical implications are discussed.

摘要

除了学术学习,人们越来越认识到,教育系统还必须顾及学生的福祉。本研究考察了预测青少年学生主观幸福感(以生活满意度、积极情绪和消极情绪为指标)的关键因素。分析了来自 71 个国家/地区的 522,836 名中学生的数据,这些国家/地区来自 8 个不同的文化背景。本研究以 Bronfenbrenner 的生物生态理论为基础,同时使用机器学习(即轻梯度提升机)和常规统计学(即层次线性模型)来考察人与环境因素的作用。在众多被考察的预测因素中,学校归属感和意义感是各种幸福感维度的共同预测因素。不同的幸福感维度也有不同的预测因素。生活满意度最好由意义感、学校归属感、父母支持、对失败的恐惧和人均国内生产总值来预测。积极情绪主要由韧性、意义感、学校归属感、父母支持和人均国内生产总值来预测。消极情绪主要由对失败的恐惧、性别、被欺负、学校归属感和意义感来预测。在全球范围内,幸福感的最佳预测因素在文化上具有显著的相似性。讨论了理论和实践意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8642/11006657/b9a85c4871f2/41598_2024_55461_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8642/11006657/5f12f22ed955/41598_2024_55461_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8642/11006657/e8c2d06086cb/41598_2024_55461_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8642/11006657/b9a85c4871f2/41598_2024_55461_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8642/11006657/5f12f22ed955/41598_2024_55461_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8642/11006657/e8c2d06086cb/41598_2024_55461_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8642/11006657/b9a85c4871f2/41598_2024_55461_Fig3_HTML.jpg

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