Predicting academic risks from students' digital footprint

this research confronts the pressing issue of academic risk mitigation in higher education by leveraging novel approaches to digital footprint analytics. The study presents an integrated machine learning system that analyzes 27 distinctive behavioral indicators gathered from the digital interactions of 1,850 undergraduate students. The methodological framework incorporates three complementary predictive modeling techniques - logistic regression, random forest, and gradient boosting - supported by comprehensive validation protocols including cross-validation and rigorous statistical assessment. The gradient boosting algorithm achieved remarkable performance with an AUC-ROC score of 0.92, substantially surpassing conventional approaches reported in contemporary educational research. Experimental deployment resulted in an 18% decrease in student attrition rates (p<0.05) while generating a 500% return on investment. The investigation develops a mathematically formalized classification system for educational interventions customized to distinct risk profiles. These outcomes provide substantial practical value for academic institutions adopting evidence-based student success initiatives. The proposed approach offers an extensible architecture for proactive identification of at-risk learners while ensuring statistical robustness. This investigation pushes the boundaries of educational data science by creating new standards for predictive performance and implementation effectiveness in academic risk evaluation.

academic risks, forecasting, machine learning, digital footprint, educational analytics, gradient boosting, pedagogical interventions