Data-Driven Investigation of Socio-Emotional Support Patterns and Their Influence on Learning Resilience Among Youths in Rural Compulsory Education

Rural compulsory education systems often operate under persistent resource constraints that intensify students exposure to academic setbacks and socio-emotional strain. This study analyzes a multimodal, four-term longitudinal dataset collected from 27 rural middle schools in western China (N=4,362), combining classroom observations, teacherstudent interaction logs, and fortnightly wellbeing surveys to uncover latent patterns of socio-emotional support and estimate their effects on learning resilience. Unsupervised sequence clustering and temporal network motif analysis recover three dominant trajectories, consistenthigh, fluctuating, and latentlow. A multilevel growth model with random intercepts and slopes, complemented by a gradient-boosted decision tree ensemble, explains a substantial share of variance in resilience growth and achievement rebounds. Consistenthigh scaffolding is associated with a 0.42 improvement in resilience after baseline adjustment, while latentlow support shows cumulative risk effects. Out-of-sample validation confirms stable predictive performance (median RMSE=0.37; cross-validated R=0.44), and robustness checks across alternative operationalizations (buoyancy scale vs. grade recovery slope) and sample restrictions support generalizability. The findings provide actionable levers for teacher professional development focused on maintaining support consistency and furnish a data-informed blueprint for monitoring socio-emotional climates in resource-limited schools.