Predicting School Happiness Among Primary School Students Using Explainable Machine Learning: Evidence from a Nationally Representative Sample in Türkiye

This study examines the factors predicting school happiness among primary school students in Türkiye using an explainable machine learning approach. Data were collected from a nationally representative sample of 4,134 primary school students. School happiness was modeled as a continuous outcome variable, and sociodemographic, familial, behavioral, and school-contextual factors were analyzed using supervised regression techniques. Linear models were compared with tree-based ensemble models to capture nonlinear relationships and complex interactions among predictors. Results indicated that tree-based ensemble models outperformed linear models in out-of-sample prediction. The Gradient Boosting Regressor achieved the highest predictive performance, explaining approximately 23% of the variance in school happiness. Model diagnostics and calibration analyses supported the generalizability of the findings. Explainability analyses revealed that peer bullying was the strongest negative predictor of school happiness, whereas reading frequency and teacher intervention emerged as protective factors. Excessive mobile device use was associated with lower predicted happiness levels, particularly at higher usage durations. Overall, the findings demonstrate that school happiness can be reliably predicted using explainable machine learning methods. While the results are predictive rather than causal, they highlight bullying prevention, teacher support, and balanced digital media use as critical leverage points for school-based interventions and educational policy.