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Springer Science and Business Media LLC
Reference23 articles.
1. Abid, A., Kallel, I., Sanchez-Medina, J. J., & Ayed, M. B. (2024). Parameters sensitivity analysis of ant colony based clustering: application for student grouping in collaborative learning environment. IEEE Access, 12, 24751–24761. https://doi.org/10.1109/ACCESS.2023.3279723
2. Ajibade, S., Dayupay, J., & Oyebode, O. (2022). Utilization of ensemble techniques for prediction of the academic performance of students. Journal of Optoelectronics Laser, 41(6), 48–54. https://www.researchgate.net/publication/361101272.
3. Alsariera, Y. A., Baashar, Y., Alkawsi, G., Mustafa, A., Alkahtani, A. A., & Ali, N. (2022). Assessment and evaluation of different machine learning algorithms for predicting student performance. Computational Intelligence and Neuroscience, 2022, 1–11. https://doi.org/10.1155/2022/4151487
4. Alsayed, A. O., Shafry, M., Rahim, M., Albidewi, I., Hussain, M., Jabeen, S. H., Alromema, N., Hussain, S., & Jibril, M. L. (2021). Selection of the right undergraduate major by students using supervised learning techniques. Applied Sciences, 11, 10639.
5. Asselman, A., Khaldi, M., & Aammou, S. (2021). Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 0(0), 1–20. https://doi.org/10.1080/10494820.2021.1928235