Practical early prediction of students’ performance using machine learning and eXplainable AI
Author:
Funder
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Education
Link
https://link.springer.com/content/pdf/10.1007/s10639-022-11120-6.pdf
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3. Aggarwal, D., Mittal, S., & Bali, V. (2021). Significance of non-academic parameters for predicting student performance using ensemble learning techniques. International Journal of System Dynamics Applications (IJSDA), 10(3), 38–49.
4. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31(1), 542–550. https://doi.org/10.1016/j.chb.2013.05.031
5. Ahmed, N. S., & Hikmat Sadiq, M. (2018). Clarify of the Random Forest Algorithm in an Educational Field. ICOASE 2018 - International Conference on Advanced Science and Engineering, 179–184. https://doi.org/10.1109/ICOASE.2018.8548804
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