This is like an early-warning radar for a classroom: it watches students’ activity in an online–offline (blended) course and predicts which students are likely to do well or poorly, so teachers can step in before final grades are set.
Manually spotting at‑risk students in blended courses is slow, subjective, and often too late. This model uses learning data (clicks, homework, quizzes, forum activity, etc.) to automatically predict student performance so educators can target support earlier and improve pass rates and outcomes.
Access to rich, longitudinal blended-learning data (LMS logs + offline records) and the institution’s ability to embed predictions into teaching workflows (dashboards, interventions) rather than the prediction algorithm itself, which is relatively commoditized classical ML.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data integration and feature engineering across multiple LMS and offline data sources; model performance heavily depends on data quality and consistent logging rather than raw compute.
Early Majority
The focus is specifically on blended-learning environments, combining online behavioral data with traditional academic records to predict performance—richer than pure LMS-log models but still implementable with standard supervised-learning techniques.