Academic Performance Prediction Using Machine Learning Algorithms
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-37164-6_27
Reference17 articles.
1. Widyahastuti, F., Tjhin, V.U.: Predicting Students’ Performance in Final examination Using Linear Regression and Multilayer Perceptron. In: 2017 10th International Conference on Human System Interactions (HSI), pp. 188–192. Ulsan, Korea (South) (2017). https://doi.org/10.1109/HSI.2017.8005026
2. Kiu, C-C.: Data Mining Analysis on Student’s Academic Performance through Exploration of Student’s Background and Social Activities. In: 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA), pp. 1–5. Subang Jaya, Malaysia (2018). https://doi.org/10.1109/ICACCAF.2018.8776809.
3. Huang, S., Fang, N.: Work in Progress—Prediction of Students' Academic Performance in an Introductory Engineering Course. In: 2011 Frontiers in Education Conference (FIE), pp. S4D-1-S4D-3. Rapid City, SD, USA (2011). https://doi.org/10.1109/FIE.2011.6142729.
4. Jain, A., Shah, K., Chaturvedi, P., Tambe, A.: Prediction and Analysis of Student Performance using Hybrid Model of Multilayer Perceptron and Random Forest. In: 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), pp. 1–7. Bhopal, India (2018). https://doi.org/10.1109/ICACAT.2018.8933580
5. Alkilani, A.H., Nusir, M.I.: Students' Performance-Prediction-Model Based on Physical and Physiological Constraints. In: 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 47–52, Sétif, Algeria (2022). https://doi.org/10.1109/SSD54932.2022.9955809
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