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![]() Title:Enhancing Student Well‑Being and Digital Literacy with Machine Learning and Spatial Analysis Conference:edu4AI Tags:Digital literacy, Educational analytics, Machine learning, Spatial econometrics and Student well‑being Abstract: Advanced data analytics and machine learning can transform how schools foster both digital skills and student well‑being. We analysed data from three Italian high‑school classes (N = 64), combining random‑forest and neural‑network predictors with Spatial Autoregressive and Geographically Weighted Regression models to capture how individual attributes, classroom geography and peer interactions jointly influence learning. Over one semester, average grades increased from 5.34 to 6.15 and well‑being scores from 0.48 to 0.95. Spatial estimates (P = 0.31, P < 0.01) indicate that sitting next to high achievers yields a mean gain of 0.38 grade points, while local pockets of well‑being amplify the effect of digital‑literacy growth on performance. These results demonstrate that digital‑literacy interventions, when delivered in spatially aware learning environments, can produce measurable academic and affective benefits. However, the limited sample and short observation window restrict generalizability. To address this, we plan a larger, longitudinal study to document the scalability and long‑term sustainability of these effects across diverse educational contexts, providing robust evidence for data‑driven, equitable classroom design and teacher training strategies. Enhancing Student Well‑Being and Digital Literacy with Machine Learning and Spatial Analysis ![]() Enhancing Student Well‑Being and Digital Literacy with Machine Learning and Spatial Analysis | ||||
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