Using machine learning to predict UK and Japanese secondary students' life satisfaction in PISA 2018.

Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 0370636 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2044-8279 (Electronic) Linking ISSN: 00070998 NLM ISO Abbreviation: Br J Educ Psychol Subsets: MEDLINE

Publication: <2012-> : Chichester : Wiley-Blackwell
Original Publication: Edinburgh : Scottish Academic Press

Personal Satisfaction* ; Students*/psychology ; Machine Learning*; Humans ; Japan ; Female ; Male ; Adolescent ; United Kingdom ; Schools ; East Asian People

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.
(© 2023 The Authors. British Journal of Educational Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

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Alberta Innovates; RES0059331 Government of Canada CanCode; RES0043209 NSERC; RES0062310 Social Sciences and Humanities Research Council of Canada; RES0048110 Social Sciences and Humanities Research Council of Canada

Keywords: PISA; k‐nearest neighbours; life satisfaction; machine learning; random forest

Date Created: 20231221 Date Completed: 20240425 Latest Revision: 20240426

20240427

10.1111/bjep.12657

38129097