Prediction of Underperforming Students Using Boosting Ensemble Methods
Abstract
This study compares boosting and ensemble methods for predicting at-risk students using three public datasets. Models like AdaBoost, CatBoost, and Random Forest are evaluated for early warning of underperformance. A practical dashboard concept is proposed for teachers and students. The work supports interpretable and actionable educational interventions.
Fields of Study & Subjects
Online Learning and Analytics
Artificial Intelligence in Healthcare
Data Mining and Machine Learning Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 14 January 2026
- Language
- EN
- Document type
- Preprint