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All issues Volume 341 (2025) EPJ Web Conf., 341 (2025) 01033 References
Open Access
Issue
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
Article Number 01033
Number of page(s) 12
DOI https://doi.org/10.1051/epjconf/202534101033
Published online 20 November 2025
  1. H. Waheed, S. U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, "Predicting academic performance of students from VLE big data using deep learning models," Computers in Human Behavior, vol. 104, Mar. 2020, doi: 10.1016/j.chb.2019.106189. [Google Scholar]
  2. X. Li, X. Zhu, X. Zhu, Y. Ji, and X. Tang, "Student Academic Performance Prediction Using Deep Multi-source Behavior Sequential Network," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2020, pp. 567-579. doi: 10.1007/978-3-030-47426-3_44. [Google Scholar]
  3. B. Sekeroglu, R. Abiyev, A. Ilhan, M. Arslan, and J. B. Idoko, "Systematic literature review on machine learning and student performance prediction: Critical gaps and possible remedies," Applied Sciences (Switzerland), vol. 11, no. 22. MDPI, Nov. 01, 2021. doi: 10.3390/app112210907. [Google Scholar]
  4. A. Namoun and A. Alshanqiti, "Predicting student performance using data mining and learning analytics techniques: A systematic literature review," Applied Sciences (Switzerland), vol. 11, no. 1. MDPI AG, pp. 1-28, Jan. 01, 2021. doi: 10.3390/app11010237. [Google Scholar]
  5. Y. A. Alsariera, Y. Baashar, G. Alkawsi, A. Mustafa, A. A. Alkahtani, and N. Ali, "Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance," Computational Intelligence and Neuroscience, vol. 2022. Hindawi Limited, 2022. doi: 10.1155/2022/4151487. [Google Scholar]
  6. M. Yagci, "Educational data mining: prediction of students' academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, no. 1, Dec. 2022, doi: 10.1186/s40561-022-00192-z. [Google Scholar]
  7. J. Valverde-Berrocoso, J. Acevedo-Borrega, and M. Cerezo-Pizarro, "Educational Technology and Student Performance: A Systematic Review," Frontiers in Education, vol. 7. Frontiers Media S.A., Jun. 28, 2022. doi: 10.3389/feduc.2022.916502. [Google Scholar]
  8. S. Chen and Y. Ding, "A Machine Learning Approach to Predicting Academic Performance in Pennsylvania's Schools," Social Sciences, vol. 12, no. 3, Mar. 2023, doi: 10.3390/socsci12030118. [Google Scholar]
  9. X. Wen and H. Juan, "Early Prediction of Students' Performance Using a Deep Neural Network Based on Online Learning Activity Sequence," Applied Sciences (Switzerland), vol. 13, no. 15, Aug. 2023, doi: 10.3390/app13158933. [Google Scholar]
  10. M. A. Aslam, F. Murtaza, M. E. U. Haq, A. Yasin, and M.A. Azam, "A Human-Centered Approach to Academic Performance Prediction Using Personality Factors in Educational AI," Information (Switzerland), vol. 15, no. 12, Dec. 2024, doi: 10.3390/info15120777. [Google Scholar]
  11. E. Ahmed, "Student Performance Prediction Using Machine Learning Algorithms," Applied Computational Intelligence and Soft Computing, vol. 2024, 2024, doi: 10.1155/2024/4067721. [Google Scholar]
  12. J. Mai, F. Wei, W. He, H. Huang, and H. Zhu, "An Explainable Student Performance Prediction Method Based on Dual-Level Progressive Classification Belief Rule Base," Electronics (Switzerland), vol. 13, no. 22, Nov. 2024, doi: 10.3390/electronics13224358. [Google Scholar]
  13. Z. Luo et al., "A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space," Mathematics, vol. 12, no. 22, Nov. 2024, doi: 10.3390/math12223597. [Google Scholar]
  14. Z. Shou, M. Xie, J. Mo, and H. Zhang, "Predicting Student Performance in Online Learning: Multidimensional Time- Series Data Analysis Approach," Applied Sciences (Switzerland), vol. 14, no. 6, Mar. 2024, doi: 10.3390/app14062522. [Google Scholar]
  15. D. Ying and J. Ma, "Student Performance Prediction with Regression Approach and Data Generation," Applied Sciences (Switzerland), vol. 14, no. 3, Feb. 2024, doi: 10.3390/app14031148. [Google Scholar]

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