This is like running a competition between different “prediction robots” to see which one is best at answering a specific education question, such as who might pass a course, drop out, or need extra support. The paper compares several robots (machine‑learning classifiers) on the same student data and measures who does the job best and most consistently.
Education stakeholders need evidence on which predictive models work best for tasks such as early‑warning systems, student performance prediction, dropout risk identification, or admission decisions. The study reduces guesswork by benchmarking multiple classifiers on the same dataset and criteria so institutions can choose an algorithm that is accurate, robust, and operationally feasible.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Feature engineering quality and availability of clean, labeled student data will be the main constraints; compute costs are modest for classical models.
Early Majority
This work focuses on head‑to‑head benchmarking of multiple supervised learning algorithms in an education context, helping practitioners move beyond generic enthusiasm for ‘AI in education’ to concrete, data‑backed choices among standard classifiers.