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Dropout early warning systems for high school students using machine learning

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  • Chung, Jae Young
  • Lee, Sunbok

Abstract

Students' dropouts are a serious problem for students, society, and policy makers. Predictive modeling using machine learning has a great potential in developing early warning systems to identify students at risk of dropping out in advance and help them. In this study, we use the random forests in machine learning to predict students at risk of dropping out. The data used in this study are the samples of 165,715 high school students from the 2014 National Education Information System (NEIS), which is a national system for educational administration information connected through the Internet with around 12,000 elementary and secondary schools, 17 city/provincial offices of education, and the Ministry of Education in Korea. Our predictive model showed an excellent performance in predicting students' dropouts in terms of various performance metrics for binary classification. The results of our study demonstrate the benefit of using machine learning with students' big data in education. We briefly overview machine learning in general and the random forests model and present the various performance metrics to evaluate our predictive model.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:cysrev:v:96:y:2019:i:c:p:346-353
    DOI: 10.1016/j.childyouth.2018.11.030
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. 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.

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