This is like an early‑warning system for student grades: it uses past student data (attendance, assignments, prior scores, demographics, etc.) to predict whether a student is likely to get an A, B, C, or fail, so educators can intervene sooner.
Reduces surprise failures and dropouts by forecasting students’ final grade bands using historical and in‑term performance data, allowing schools to target support and resources to at‑risk students before it’s too late.
Quality and breadth of institutional student data over time (multi‑year cohorts, enriched features like LMS interactions, attendance, and assessments) and integration into existing academic workflows (advising dashboards, LMS alerts) can create a defensible, sticky solution.
Classical-ML (Scikit/XGBoost)
Feature Store
Medium (Integration logic)
Data quality and label drift over time (grading policies, curricula, and cohorts change, requiring frequent model retraining and feature maintenance).
Early Majority
Focus on grade-band classification (discrete classes like A/B/C/Fail) rather than only regression-style GPA prediction, enabling clearer risk flags and simpler interpretation for educators and advisors.