Dropout early warning systems for high school students using machine learning
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Abstract
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DOI: 10.1016/j.childyouth.2018.11.030
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Cited by:
- Daniel Zapata-Medina & Albeiro Espinosa-Bedoya & Jovani Alberto Jiménez-Builes, 2024. "Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques," Mathematics, MDPI, vol. 12(12), pages 1-20, June.
- Bacon, Victoria R. & Kearney, Christopher A., 2020. "School climate and student-based contextual learning factors as predictors of school absenteeism severity at multiple levels via CHAID analysis," Children and Youth Services Review, Elsevier, vol. 118(C).
- Diogo E. Moreira da Silva & Eduardo J. Solteiro Pires & Arsénio Reis & Paulo B. de Moura Oliveira & João Barroso, 2022. "Forecasting Students Dropout: A UTAD University Study," Future Internet, MDPI, vol. 14(3), pages 1-14, February.
- Anne Parlina & Kalamullah Ramli & Hendri Murfi, 2021. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means," Sustainability, MDPI, vol. 13(5), pages 1-28, March.
- Joyce de Souza Zanirato Maia & Ana Paula Arantes Bueno & Joao Ricardo Sato, 2023. "Applications of Artificial Intelligence Models in Educational Analytics and Decision Making: A Systematic Review," World, MDPI, vol. 4(2), pages 1-26, May.
- Rebai, Sonia & Ben Yahia, Fatma & Essid, Hédi, 2020. "A graphically based machine learning approach to predict secondary schools performance in Tunisia," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
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Keywords
Dropout; Machine learning; Predictive model; Random forests model; Big data;All these keywords.
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