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All issues Volume 328 (2025) EPJ Web Conf., 328 (2025) 01072 References
Open Access
Issue
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
Article Number 01072
Number of page(s) 14
DOI https://doi.org/10.1051/epjconf/202532801072
Published online 18 June 2025
  1. Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2021). A systematic review of social media acceptance from the perspective of educational and information systems theories and models. Journal of Educational Computing Research, 59(2), 280312. https://doi.org/10.1177/0735633120962061 [Google Scholar]
  2. Chen, X., Liu, C., Li, B., & Xu, K. (2022). Federated learning for adaptive e-learning systems: A privacy-preserving approach. IEEE Transactions on Learning Technologies, 15(3), 456-470. https://doi.org/10.1109/TLT.2022.3156789 [Google Scholar]
  3. Deng, Y., & Yu, Z. (2023). A hierarchical semantic graph-based approach for personalized learning path recommendation. Expert Systems with Applications, 213, 119284. https://doi.org/10.1016/j.eswa.2022.119284 [Google Scholar]
  4. El Saddik, A., Hossain, M.S., & Kantarci, B. (2021). Federated learning for AI-driven e-learning systems: Opportunities and challenges. IEEE Multimedia, 28(2), 8997. https://doi.org/10.1109/MMUL.2021.3056778 [Google Scholar]
  5. Gao, J., Wang, H., & Shen, H. (2023). Context-aware federated learning for adaptive e-learning in edge computing environments. Future Generation Computer Systems, 141, 512-525. https://doi.org/10.1016/j.future.2022.12.015 [Google Scholar]
  6. Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., & Tygar, J.D. (2021). Adversarial machine learning in federated learning systems. ACM Computing Surveys, 54(5), 1-36. https://doi.org/10.1145/3453165 [Google Scholar]
  7. Khan, M.A., & Algarni, F. (2023). A novel deep learning-based semantic analysis framework for adaptive e-learning. Computers & Education, 194, 104703. https://doi.org/10.1016/j.compedu.2022.104703 [CrossRef] [Google Scholar]
  8. Li, T., Sahu, A.K., Talwalkar, A., & Smith, V. (2022). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 5060. https://doi.org/10.1109/MSP.2020.2975749 [Google Scholar]
  9. Liu, Y., Kang, Y., Xing, C., Chen, T., & Yang, Q. (2021). A secure federated transfer learning framework for adaptive e-learning. IEEE Intelligent Systems, 36(5), 5866. https://doi.org/10.1109/MIS.2021.3086281 [CrossRef] [Google Scholar]
  10. Ma, L., & Zhang, Y. (2023). Semantic-enhanced knowledge graphs for adaptive learning recommendations. *Knowledge-Based Systems, 260*, 110145. https://doi.org/10.1016/j.knosys.2022.110145 [CrossRef] [Google Scholar]
  11. Mouri, K., Uosaki, N., & Ogata, H. (2021). Context-aware personalized learning path recommendation using federated learning. Educational Technology & Society, 24(4), 114. https://www.jstor.org/stable/48629250 [Google Scholar]
  12. Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., & Poor, H.V. (2022). Federated learning for Internet of Things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 24(3), 16251658. https://doi.org/10.1109/COMST.2022.3183916 [Google Scholar]
  13. Pardo, A., Han, F., & Ellis, R.A. (2023). A federated learning approach for adaptive assessment in online learning. Computers in Human Behavior, 138, 107473. https://doi.org/10.1016/j.chb.2022.107473 [CrossRef] [Google Scholar]
  14. Qiu, H., Zheng, Q., & Memon, A.R. (2022). A hierarchical semantic graph model for automated feedback generation in e-learning. IEEE Transactions on Education, 65(4), 612-623. https://doi.org/10.1109/TE.2022.3156782 [Google Scholar]
  15. Rana, M., & Dabbagh, N. (2023). Context-aware federated reinforcement learning for personalized e-learning systems. Journal of Artificial Intelligence in Education, 34(1), 45-67. https://doi.org/10.1007/s40593-022-00316-z [Google Scholar]
  16. Shorfuzzaman, M., Hossain, M.S., & Alhamid, M.F. (2021). Towards leveraging deep learning models for adaptive e-learning in smart environments. IEEE Access, 9, 4538245398. https://doi.org/10.1109/ACCESS.2021.3066889 [Google Scholar]
  17. Sun, Y., & Gao, F. (2022). A federated learning-based knowledge tracing model for adaptive learning systems. Interactive Learning Environments, 30(5), 789803. https://doi.org/10.1080/10494820.2022.2042039 [Google Scholar]
  18. Tang, C., Wu, X., & Chen, G. (2023). A knowledge graph-based semantic analysis framework for intelligent tutoring systems. Expert Systems with Applications, 214, 119102. https://doi.org/10.1016/j.eswa.2022.119102 [Google Scholar]
  19. Wang, L., Wang, X., & Zhang, L. (2021). Federated meta-learning for adaptive e-learning systems. Neural Computing and Applications, 33(14), 86238635. https://doi.org/10.1007/s00521-021-05908-9 [Google Scholar]
  20. Wu, D., Zhang, H., & Xue, Y. (2022). Context-aware federated learning for personalized e-learning recommendations. IEEE Transactions on Knowledge and Data Engineering, 34(8), 3845-3858. https://doi.org/10.1109/TKDE.2021.3056783 [Google Scholar]
  21. Xu, J., Glicksberg, B.S., & Bian, J. (2023). Hierarchical semantic graph representation learning for adaptive e-learning. Information Sciences, 622, 11241138. https://doi.org/10.1016/j.ins.2022.11.045 [Google Scholar]
  22. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2021). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 119. https://doi.org/10.1145/3298981 [Google Scholar]
  23. Zhang, W., Zhou, T., & Lu, Q. (2023). A hybrid federated learning and semantic reasoning approach for adaptive e-learning. Journal of Educational Technology & Society, 26(1), 1-15. https://www.jstor.org/stable/48707971 [Google Scholar]
  24. Zhao, Y., Li, M., & Wang, J. (2022). Privacy-preserving federated learning for adaptive e-learning systems. Computers & Security, 114, 102592. https://doi.org/10.1016/j.cose.2022.102592 [Google Scholar]
  25. Zhu, H., Jin, Y., & Tan, C. (2023). A survey on federated learning for adaptive educational systems. Artificial Intelligence Review, 56(3), 23452378. https://doi.org/10.1007/s10462-022-10219-z [Google Scholar]

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