Framework for suggesting corrective actions to help students intended at risk of low performance based on experimental study of college students using explainable machine learning model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Education
Link
https://link.springer.com/content/pdf/10.1007/s10639-023-12072-1.pdf
Reference78 articles.
1. Abu-Oda, G. S., & El-Halees, A. M. (2015). Data mining in higher education : university student dropout case study. International Journal of Data Mining & Knowledge Management Process (IJDKP), 5(1). https://doi.org/10.5121/ijdkp.2015.5102
2. Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data mining: A prediction for student’s performance using classification method. World Journal of Computer Application and Technology (CEASE PUBLICATION), 2(2), 43–47. https://doi.org/10.13189/WJCAT.2014.020203
3. al Karim, M., Masnad, M. M., Ara, M. Y., Rasel, M., & Nandi, D. (2022). A Comprehensive study to investigate student performance in online education during Covid-19. International Journal of Modern Education and Computer Science, 14(3), 1–25. https://doi.org/10.5815/IJMECS.2022.03.01
4. Alboaneen, D., Almelihi, M., Alsubaie, R., Alghamdi, R., Alshehri, L., & Alharthi, R. (2022). Development of a web-based prediction system for students’ academic performance. Data, 7, 21. https://doi.org/10.3390/data7020021
5. Albreiki, B. (2022). Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models. International Journal of Educational Technology in Higher Education, 19(1), 1–26. https://doi.org/10.1186/S41239-022-00354-6/TABLES/9
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