Predicting Students’ Performance Using Feature Selection-Based Machine Learning Technique
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6544-1_29
Reference10 articles.
1. Imran M, Latif S, Mahmood D, Shah M (2019) Student academic performance prediction using supervised learning techniques. Int J Emerg Technol Learn 14(14):92–104. https://doi.org/10.3991/ijet.v14i14.10310
2. Chaudhury P, Tripathy H (2020) A novel academic performance estimation model using two stage feature selection. Indonesian J Electric Eng Comput Sci 19(3):1610–1619. https://doi.org/10.11591/ijeecs.v19.i3.pp1610-1619
3. Alshabandar R, Hussain A, Keight R, Khan W (2020) Students performance prediction in online courses using machine learning algorithms. Proc IJCNN Conf 2020:1–7. https://doi.org/10.1109/IJCNN48605.2020.9207196
4. Velarde L, Gerardo C, Chamorro-Atalaya O, Morales-Romero G, Meza-Chaupis Y, Auqui-Ramos E, Ramos-Cruz J, Aybar-Bellido I (2022) Quadratic vector support machine algorithm, applied to prediction of university student satisfaction. 11591/ijeecs.v27.i1, pp 139–148
5. Chitti M, Chitti P, Jayabalan M (2020) Need for interpretable student performance prediction. Proc DeSE Conf, 269–272. https://doi.org/10.1109/DeSE51703.2020.9450735
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