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

Abstract

BackgroundLife 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.ObjectiveUsing ML algorithms, the current study predicts secondary students' life satisfaction from individual‐level variables.MethodTwo 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.ResultsFindings 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.ConclusionsTheoretically, 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.

Determinants of the happiness of adolescents: A leisure perspective

“…After hyper parameter tuning, the model's performance is shown in Table 6 and Fig ). The performance of these models is somewhat low compared to previous studies that used machine learning to predict happiness [97][98][99].…”