ARCHIVES

. Akour, M., Sghaier, H.A., Qasem, O.A., "The effectiveness of using deep learning algorithms in predicting students
achievements", Indonesian Journal of Electrical Engineering and Computer Science, 19 (2020), pp. 388-394.
2. Al-Zawqari, A., Peumans, D., Vandersteen, G., “A flexible feature selection approach for predicting students’ academic performance
in online courses”, Computers and Education: Artificial Intelligence, 3, 2022.
3. Alhassan, A., Zafar, B., and Mueen, A., “Predict students’ academic performance based on their assessment grades and online activity
data,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 11, no. 4, 2020.
4. Aljohani, N.R., Fayoumi, A., Hassan, S.U., “Predicting at-risk students using clickstream data in the virtual learning environment”,
Sustainability (Switzerland), 11, 2019.
5. Arasaratnam-Smith, L.A. and Northcote, M., “Community in Online Higher Education: Challenges and Opportunities”, Electronic
Journal of e-Learning, 2017, 15, 188-198.
6. Bao, W. “Covid-19 and Online Teaching in Higher Education: A Case Study of Peking University”, Human Behavior and Emerging
Technologies , 2, 113-115, 2020.
7. Larreamendy-Joerns, J. and Leinhardt, G., “Going the Distance with Online Education”, Review of Educational Research , 76, 567-
605, 2006.
8. Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., and Durán- Domínguez, A., “Analyzing and predicting students’ performance by
means of machine learning: A review”, Applied sciences., 10(3), 1042, 2020.
9. Salal, Y., Abdullaev, S. and Kumar, M., “Educational Data Mining: Student Performance Prediction in Academic”, International
Journal of Engineering and Advanced Technology, 8, 54-59. 2019.
10. Sani, B. A. and Badamasi, H., Machine learning algorithms to predict student’s academic performance, Bakolori Journal of General
Studies. (2021) 12, no. 2, 3656–3671.
11. Senthamaraiselvi, S. and Meenakshi Sundaram, K., “A study on social networking usage amoung students”, 4th International
Conference on Artificial Intelligence Trending Towards Automation, 2023.
12. Senthamaraiselvi, S. and Meenakshi Sundaram, K., “Reprecussion of Social Media in the Area of Education”, Inernational
Conference on Advanced Computing (ICAC), 2023.
13. Senthamaraiselvi, S., and Meenakshi Sundaram, K., “Predicting Online Social Network Student Performance Using Enhanced
RandomBayesian Algorithm”, International Journal of Intelligent Systems and Applications in Engineering, 12(2), 384–390, 2023.
14. Senthamaraiselvi, S., MeenakshiSundaram, K. and Vandarkuzhali, J., “Hybrid Deep Neural Network for Predicting Online Social
Network Student Performance”, International Journal of Communication Networks and Information Security, 2024, 16(3), 6701.
15. Senthamaraiselvi, S., MeenakshiSundaram, K. and Vandarkuzhali, J., “Hybrid RNN-BiLSTM Approach for Student Success
Estimation in Online Social Networks, Journal of Information Systems Engineering and Management, Vol.10 No.2, 2025.
16. Sokkheyand, P., and Okazaki, T., “Developing web-based support systems for predicting poor-performing students using educational
data mining techniques,” Studies, vol. 11, no. 7, 2020.
17. Xin Chen, Mihaela, and Krishna P.C, “Mining Social Media Data for Understanding Students Learning Experiences”, IEEE
Transactions on Learning Technologies, 2014.
18. Xu, X., Wang, J., Peng, H., and Wu, R., “Prediction of academic performance associated with internet usage behaviors using machine
learning algorithms,” Computers in Human Behavior, vol. 98, pp. 166–173, 2019.
19. Yaacob W. W., Sobri N. M., Nasir S. M., Norshahidi N. D., and Husin W. W., “Predicting student drop-out in higher institution using
data mining techniques,” Journal of Physics: Conference Series, vol. 1496, p. 012005, 2020.