Using machine learning to predict UK and Japanese secondary students' life satisfaction in Pisa 2018
Bibliographic Data
| ID | 9615935 |
|---|---|
| Authors | Zexuan Pan (0000-0002-2450-5121, Marsal Family School of Education and Department of Psychology University of Michigan Ann Arbor Michigan USA), Maria Cutumisu (0000-0003-2475-9647, Department of Educational and Counselling Psychology, Faculty of Education McGill University Montreal Quebec Canada, corresponding author) |
| Year | 2024 |
| Volume | 94 |
| Issue | 2 |
| Pages | 474-498 |
| Publication date | 2024-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Journal of Educational Psychology (JOURNAL) |
| Journal identifiers | ISSN: 0007-0998 • E-ISSN: 2044-8279 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bjep.12657 |
| PMID | 38129097 |
| OpenAlex | W4390083045 |
| Language | EN |
| Citations received | 4 |
| References cited | 110 |
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
Advertising · Business · Mathematics education · Advanced Technologies in Various Fields · Grit, Self-Efficacy, and Motivation · Psychological Well-being and Life Satisfaction · Psychology
Predictive insights into U.S. students’ mathematics performance on Pisa 2022 using ensemble tree-based machine learning models
The application of machine learning approaches in international large-scale educational assessments
Predicting the Mathematics Literacy of Resilient Students from High‐performing Economies
Machine-Learning Applications in Predicting Students’ Non-Cognitive Skills
Pisa 2018 Results (Volume I)
Youth Life Satisfaction
Variable Importance Assessment in Regression
Computer-based personality judgments are more accurate than those made by humans
Choosing Prediction Over Explanation in Psychology
Guidelines for National Indicators of Subjective Well-Being and Ill-Being
Subjective Well-Being Around the World
Are Cultures Becoming Individualistic? A Cross-Temporal Comparison of Individualism–Collectivism in the United States and Japan
Relative Levels of Hope and Their Relationship With Academic and Psychological Indicators Among Adolescents
The Relationship Between Life Satisfaction and Health Behavior
A path analysis of basic need support, self-efficacy, achievement goals, life satisfaction and academic achievement level among secondary school students
Cognitive and affective development in adolescence
A review of life satisfaction research with children and adolescents.
Child and adolescent mental disorders
Integrating explanation and prediction in computational social science
Life satisfaction and mental health problems (18 to 35 years)
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
Initial Development of the Student's Life Satisfaction Scale
Self-reported Life Satisfaction and 20-Year Mortality in Healthy Finnish Adults
Life Satisfaction and Academic Performance in Early Adolescents
Bagging predictors
Personality, Culture, and Subjective Well-Being
Culture and systems of thought
Elementary School Students’ Subjective Well-Being Before and During the Covid-19 Pandemic
Systematic review and meta‐analysis
High school students’ tenacity and flexibility in goal pursuit linked to life satisfaction and achievement on competencies tests
Bottom‐up or top‐down? Examining global and domain‐specific evaluations of how one's life is unfolding over time
A Comparison Between Bottom–Up, Top–Down, and Bidirectional Models of Relationships Between Global and Life Facet Satisfaction
The French unhappiness puzzle
Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being
Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being
Predicting Student Well-Being
Culture as situated cognition
A Global Decline in Adolescents’ Subjective Well-Being
The concept of life satisfaction across cultures
The satisfaction with life scale
Life Satisfaction and Student Engagement in Adolescents
Trajectories of Life Satisfaction Across the Transition to Post-Compulsory Education
The shifting basis of life satisfaction judgments across cultures
A theory of human motivation
Culture and the self
Cross-cultural correlates of life satisfaction and self-esteem
Cross-Cultural Organizational Behavior
Significant predictors of mathematical literacy for top-tiered countries/economies, Canada, and the United States on Pisa 2012
Refining the relationship between personality and subjective well-being
Fifty Years since the Coleman Report
Individualism-Collectivism
Will Money Increase Subjective Well-Being
How Disadvantages Shape Life Satisfaction
Avowed happiness as an overall assessment of the quality of life
Top-down versus bottom-up theories of subjective well-being
| Unique citing works | 4 |
|---|---|
| Citations per year | 2 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |