Publisher
Springer Nature Switzerland
Reference17 articles.
1. Nouri, J., Larsson, K., Saqr, M.: Bachelor thesis analytics: using machine learning to predict dropout and identify performance factors. Int. J. Learn. Analytics Artif. Intell. Educ. 1(1), 116–131 (2019)
2. Tang, Z., Chen, L., Jain, A. (2023). Exploring Individual Feature Importance in Student Persistence Prediction. Journal of Higher Education Theory & Practice, 23(6)
3. Mduma, N., Kalegele, K., Machuve, D.: A survey of machine learning approaches and techniques for student dropout prediction. Data Sci. J. (2019). https://doi.org/10.5334/dsj-2019-014
4. Realinho, V., Machado, J., Baptista, L., Martins, M.V.: Predicting student dropout and academic success. Data 7(11), 146 (2022)
5. Rastrollo-Guerrero, J.L., Gómez-Pulido, J.A., Durán-Domínguez, A.: Analyzing and predicting students’ performance by means of machine learning: a review. Appl. Sci. 10(3), 1042 (2020)