AI-Powered Academic Success: Predicting and Combating Dropout RatesAI-Powered Academic Success: Predicting and Combating Dropout Rates
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kaze nesia
• 14h
AI Student Success Intelligence Platform
A twelve‑module analytics platform analyzed 50,000 learners across six countries to predict dropout, model engagement, and simulate interventions, finding that a composite Student Engagement Index (SEI)—built from Time Commitment, Academic Quality, Platform Activity, and Social Learning—is the strongest predictor of dropout (behavior beats demographics), an ensemble of XGBoost/LightGBM/CatBoost achieved 99.72% AUC and F1 = 0.9522, risk tiers were highly precise (Low Risk = 0.0% dropout; Critical Risk = 99.7%), multi‑dimensional “Full Interventions” produced the largest simulated risk reductions, and correcting a data‑leakage issue (attendance proxy) was essential to preserve model integrity.
Data AnalysisData EngineeringData SciencepandasPythonscikit-learn
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