The Role of Machine Learning in Predicting Student Dropout Rates
Core idea Machine learning identifies at‑risk students earlier and more accurately by analyzing patterns across academic, engagement, and socio‑demographic data, enabling timely, targeted interventions that improve retention—especially when models are explainable, fair, and embedded in student support workflows. Why ML works for dropout prediction Evidence and 2024–2025 signals High‑value features to engineer Model choices and … Read more