Forecasting Students Dropout: A UTAD University Study
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References listed on IDEAS
- Chung, Jae Young & Lee, Sunbok, 2019. "Dropout early warning systems for high school students using machine learning," Children and Youth Services Review, Elsevier, vol. 96(C), pages 346-353.
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Cited by:
- Ivan Miguel Pires, 2022. "Smart Objects and Technologies for Social Good," Future Internet, MDPI, vol. 14(12), pages 1-3, December.
- Raghul Gandhi Venkatesan & Dhivya Karmegam & Bagavandas Mappillairaju, 2024. "Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis," Journal of Computational Social Science, Springer, vol. 7(1), pages 171-196, April.
- Isaac Caicedo-Castro, 2023. "Course Prophet: A System for Predicting Course Failures with Machine Learning: A Numerical Methods Case Study," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
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Keywords
students dropout; Random Forest; XGBoost; CatBoost; artificial neural network; permutation feature importance;All these keywords.
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