Marsal Family School of Education and Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA.
Department of Educational and Counselling Psychology, Faculty of Education, McGill University, Montreal, Quebec, Canada.
Br J Educ Psychol. 2024 Jun;94(2):474-498. doi: 10.1111/bjep.12657. Epub 2023 Dec 21.
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.
Using ML algorithms, the current study predicts secondary students' life satisfaction from individual-level variables.
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.
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.
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.
生活满意度是学生主观幸福感的一个关键组成部分，因为它会影响到学业成绩和终身健康。尽管之前的研究已经从不同的角度探讨了生活满意度，但很少有研究采用机器学习 (ML) 方法。
本研究使用 ML 算法从个体层面的变量预测中学生的生活满意度。
基于 UK Data 和 PISA 2018 中的日本数据，开发了两种有监督的 ML 模型，随机森林 (RF) 和 K 最近邻 (KNN)。
研究结果表明：(1) 两个模型在 UK Data 上的表现均优于在日本数据上的表现；(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、遭受欺凌的经历以及家庭和学校的 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，并确定了一些关键的预测因素。从方法论上讲，本研究首次使用 ML 来探索生活满意度的预测因素。从实践的角度来看，它为提高中学生的生活满意度提供了参考。
