A novel methodology using RNN + LSTM + ML for predicting student’s academic performance

Author:

Kukkar Ashima,Mohana Rajni,Sharma Aman,Nayyar AnandORCID

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Education

Reference67 articles.

1. Alhakbani, H. A., & Alnassar, F. M. (2022, March). Open Learning Analytics: A Systematic Review of Benchmark Studies Using Open University Learning Analytics Dataset (OULAD). In Proceedings of the 2022 7th International Conference on Machine Learning Technologies (ICMLT '22). Association for Computing Machinery, New York, NY, USA, (pp. 81–86). https://doi.org/10.1145/3529399.3529413

2. Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., & Olatunji, S. O. (2017). Student performance prediction using support vector machine and k-nearest neighbor. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada (pp. 1–4). https://doi.org/10.1109/CCECE.2017.7946847

3. Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007

4. Asif, R., Merceron, A., & Pathan, M. K. (2015). Investigating performance of students: a longitudinal study. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (LAK '15). Association for Computing Machinery, New York, NY (pp. 108–112). https://doi.org/10.1145/2723576.2723579

5. Awaludin, Muryan, & Muryan. (2022). Optimization of Naïve Bayes Algorithm Parameters for Student Graduation Prediction at Universitas Dirgantara Marsekal Suryadarma. Journal of Information System, informatic and Computing, 6(1), 91–106.

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