Think of this as a data-driven early‑warning system for student performance. It watches how students study and interact with learning systems (attendance, homework, online activity, quiz results), then uses a prediction model to estimate who is likely to struggle or succeed so teachers can intervene early.
Manual tracking of student progress is slow, reactive, and often misses at‑risk students until it’s too late. This work uses behavioral and performance data to automatically predict future academic performance or risk levels, enabling proactive support and more targeted teaching interventions.
Classical-ML (Scikit/XGBoost)
Feature Store
Medium (Integration logic)
Data quality and integration across LMS, SIS, and assessment systems; risk of concept drift as curricula and student behavior change over time.
Early Majority
Focuses on fine-grained analysis of students’ learning behaviors (not just grades) to construct supervised prediction models, likely using multiple behavioral features and potentially ensemble or optimized classifiers, to reach higher predictive accuracy and more actionable early‑warning insights than simple rule-based or grade-threshold systems.