A stacking ensemble machine learning method for early identification of students at risk of dropout
Author:
Funder
Instituto Tecnológico y de Estudios Superiores de Monterrey
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Education
Link
https://link.springer.com/content/pdf/10.1007/s10639-023-11682-z.pdf
Reference28 articles.
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2. Berens, J., Schneider, K., Gortz, S., Oster, S., & Burghoff, J. (2019). Early detection of students at risk - Predicting student dropouts using administrative student data from german universities and machine learning methods. Journal of Educational Data Mining, 11(3), 1–41. https://doi.org/10.5281/zenodo.3594771
3. Borrella, I., Caballero-Caballero, S., & Ponce-Cueto, E. (2022). Taking action to reduce dropout in MOOCs: tested interventions. Computers & Education, 179, 104412. https://doi.org/10.1016/J.COMPEDU.2021.104412
4. Casanova, J. R., Cervero, A., Núñez, J. C., Almeida, L. S., & Bernardo, A. (2018). Factors that determine the persistence and dropout of university students. Psicothema, 30(4), 408–414. https://doi.org/10.7334/psicothema2018.155
5. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
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