{"title":"利用机器学习预测 2018 年国际学生评估项目(PISA)中英国和日本中学生的生活满意度。","authors":"Zexuan Pan, Maria Cutumisu","doi":"10.1111/bjep.12657","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches.</p>\n </section>\n \n <section>\n \n <h3> Objective</h3>\n \n <p>Using ML algorithms, the current study predicts secondary students' life satisfaction from individual-level variables.</p>\n </section>\n \n <section>\n \n <h3> Method</h3>\n \n <p>Two supervised ML models, random forest (RF) and k-nearest neighbours (KNN), were developed based on the UK data and the Japan data in PISA 2018.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Findings show that (1) both models yielded better performance on the UK data than on the Japanese data; (2) the RF model outperformed the KNN model in predicting students' life satisfaction; (3) meaning in life, student competition, teacher support, exposure to bullying and ICT resources at home and at school played important roles in predicting students' life satisfaction.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Theoretically, this study highlights the multi-dimensional nature of life satisfaction and identifies several key predictors. Methodologically, this study is the first to use ML to explore the predictors of life satisfaction. Practically, it serves as a reference for improving secondary students' life satisfaction.</p>\n </section>\n </div>","PeriodicalId":51367,"journal":{"name":"British Journal of Educational Psychology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjep.12657","citationCount":"0","resultStr":"{\"title\":\"Using machine learning to predict UK and Japanese secondary students' life satisfaction in PISA 2018\",\"authors\":\"Zexuan Pan, Maria Cutumisu\",\"doi\":\"10.1111/bjep.12657\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. 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引用次数: 0
摘要
背景:生活满意度是学生主观幸福感的重要组成部分,因为它对学业成绩和终身健康都有影响。尽管以往的研究通过不同的视角对生活满意度进行了调查,但很少有研究采用机器学习(ML)方法:本研究利用 ML 算法,从个体层面的变量预测中学生的生活满意度:基于 PISA 2018 中的英国数据和日本数据,开发了随机森林(RF)和 k-近邻(KNN)两种有监督的 ML 模型:研究结果表明:(1)两种模型在英国数据上的表现均优于日本数据;(2)RF 模型在预测学生生活满意度方面优于 KNN 模型;(3)生活意义、学生竞争、教师支持、遭受欺凌以及家庭和学校的 ICT 资源在预测学生生活满意度方面发挥了重要作用:从理论上讲,本研究强调了生活满意度的多维性,并确定了几个关键的预测因素。从方法论上讲,本研究首次使用 ML 方法来探索生活满意度的预测因素。在实践中,它为提高中学生的生活满意度提供了参考。
Using machine learning to predict UK and Japanese secondary students' life satisfaction in PISA 2018
Background
Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches.
Objective
Using ML algorithms, the current study predicts secondary students' life satisfaction from individual-level variables.
Method
Two supervised ML models, random forest (RF) and k-nearest neighbours (KNN), were developed based on the UK data and the Japan data in PISA 2018.
Results
Findings show that (1) both models yielded better performance on the UK data than on the Japanese data; (2) the RF model outperformed the KNN model in predicting students' life satisfaction; (3) meaning in life, student competition, teacher support, exposure to bullying and ICT resources at home and at school played important roles in predicting students' life satisfaction.
Conclusions
Theoretically, this study highlights the multi-dimensional nature of life satisfaction and identifies several key predictors. Methodologically, this study is the first to use ML to explore the predictors of life satisfaction. Practically, it serves as a reference for improving secondary students' life satisfaction.
期刊介绍:
The British Journal of Educational Psychology publishes original psychological research pertaining to education across all ages and educational levels including: - cognition - learning - motivation - literacy - numeracy and language - behaviour - social-emotional development - developmental difficulties linked to educational psychology or the psychology of education