intervention design

How Machine Learning Helps Predict Student Success and Dropouts

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Core idea Machine learning predicts student success and potential dropouts by learning patterns from historical data—grades, attendance, LMS activity, demographics, and engagement—to flag at‑risk learners early and recommend targeted interventions that improve retention and completion. What ML models use Algorithms that perform well Evidence and 2025 signals From prediction to action Guardrails: equity, privacy, trust … Read more

The Role of Machine Learning in Predicting Student Dropout Rates

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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