Cross-economy stability of predictor rankings for adolescent school belonging and mathematics anxiety: an explainable machine-learning analysis of PISA 2022
Yuan Liao, Qin Peng, Runlin Li · 2026
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Description
What makes teenagers feel they belong at school, and what makes them fear mathematics? Yuan Liao and co-authors ask whether the answers travel across borders. They mine survey data from more than six hundred thousand students in the PISA 2022 study, spanning dozens of economies. Machine learning models rank the predictors in each economy. Explanation tools then test whether the rankings hold steady.
Two stable patterns emerged. Belonging tracked relationships: feeling safe, escaping bullying, and getting along with teachers. Math anxiety tracked confidence and achievement: self-belief, growth mindset, and persistence. Most top predictors kept their rank across economies. The findings give schools a short, sturdy list of levers that seem to work in many cultures at once.
Catalog record
| CREATOR | Yuan Liao, Qin Peng, Runlin Li |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 87 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |