A Course Recommendation Algorithm for a Personalized Online Learning Platform for Students From the Perspective of Deep Learning (2024)

Skip Abstract Section

1. Adedoyin, O. B., & Soykan, E. (2023). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 31(2), 863–875. doi:10.1080/10494820.2020.1813180.Google ScholarCross Ref
2. Al-Emran, M., Al-Nuaimi, M. N., Arpaci, I., Al-Sharafi, M. A., & Anthony, B.Jr. (2023). Towards a wearable education: Understanding the determinants affecting students’ adoption of wearable technologies using machine learning algorithms. Education and Information Technologies, 28(3), 2727–2746. doi:10.1007/s10639-022-11294-z.Google ScholarDigital Library
3. Al Shloul, T., Javeed, M., Gochoo, M., Alsuhibany, S. A., Ghadi, Y. Y., Jalal, A., & Park, J. (2023). Student’s health exercise recognition tool for E-learning education. Intelligent Automation and Soft Computing, 35(1), 149–161. https://doi.org/Google Scholar
4. Alsabhan, W. (2023). Student cheating detection in higher education by implementing machine learning and LSTM techniques. Sensors (Basel), 23(8), 4149. doi:10.3390/s23084149 PMID:37112489.Google ScholarCross Ref
5. Amini, M., & Rahmani, A. (2023). Agricultural databases evaluation with machine learning procedure. Australian Journal of Engineering and Applied Science, 8, 39–50. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4331902.Google Scholar
6. Bang, H. J., Li, L., & Flynn, K. (2023). Efficacy of an adaptive game-based math learning app to support personalized learning and improve early elementary school students’ learning. Early Childhood Education Journal, 51(4), 717–732. doi:10.1007/s10643-022-01332-3.Google ScholarCross Ref
7. Bharadiya, J. P. (2023). Machine learning and AI in business intelligence: Trends and opportunities. International Journal of Computer, 48(1), 123–134. https://www.researchgate.net/publication/371902170_Machine_Learning_and_AI_in_Business_Intelligence_Trends_and_OpportunitiesGoogle Scholar
8. Cardona, T., Cudney, E. A., ho*rl, R., & Snyder, J. (2023). Data mining and machine learning retention models in higher education. Journal of College Student Retention, 25(1), 51–75. doi:10.1177/1521025120964920.Google ScholarCross Ref
9. Chen, S., & Ding, Y. (2023). A machine learning approach to predicting academic performance in Pennsylvania’s schools. Social Sciences (Basel, Switzerland), 12(3), 118. doi:10.3390/socsci12030118.Google ScholarCross Ref
10. Dolezal, J. M., Wolk, R., Hieromnimon, H. M., Howard, F. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S., Kwon, J. W., Agni, M., Simon, R. C., Desai, C., Kherallah, R., Nguyen, T. D., Schulte, J. J., Cole, K., Khramtsova, G., Garassino, M. C., Husain, A. N., & Pearson, A. T. et al. (2023). Deep learning generates synthetic cancer histology for explainability and education. NPJ Precision Oncology, 7(1), 49. doi:10.1038/s41698-023-00399-4 PMID:37248379.Google ScholarCross Ref
11. Elglaly, Y. N., & Liu, Y. (2023). Promoting machine learning fairness education through active learning and reflective practices. ACM SIGCSE Bulletin, 55(3), 4–6.Google ScholarDigital Library
12. Guleria, P., & Sood, M. (2023). Explainable AI and machine learning: Performance evaluation and explainability of classifiers on educational data mining inspired career counseling. Education and Information Technologies, 28(1), 1081–1116. doi:10.1007/s10639-022-11221-2 PMID:35875826.Google ScholarDigital Library
13. Hussain, S., & Khan, M. Q. (2023). Student-performulator: Predicting students’ academic performance at secondary and intermediate level using machine learning. Annals of Data Science, 10(3), 637–655. doi:10.1007/s40745-021-00341-0.Google ScholarCross Ref
14. Johnson, C. C., Walton, J. B., Strickler, L., & Elliott, J. B. (2023). Online teaching in K-12 education in the United States: A systematic review. Review of Educational Research, 93(3), 353–411. doi:10.3102/00346543221105550.Google ScholarCross Ref
15. Jurgensmeier, K., Till, S. E., Lu, Y., Arguello, A. M., Stuart, M. J., Saris, D. B., Camp, C. L., & Krych, A. J. (2023). Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention. Knee Surgery, Sports Traumatology, Arthroscopy, 31(2), 518–529. doi:10.1007/s00167-022-07117-w PMID:35974194.Google ScholarCross Ref
16. Kang, X., & Zhang, W. (2023). An experimental case study on forum-based online teaching to improve student’s engagement and motivation in higher education. Interactive Learning Environments, 31(2), 1029–1040. doi:10.1080/10494820.2020.1817758.Google ScholarCross Ref
17. Kuadey, N. A., Mahama, F., Ankora, C., Bensah, L., Maale, G. T., Agbesi, V. K., Kuadey, A. M., & Adjei, L. (2023). Predicting students’ continuance use of learning management system at a technical university using machine learning algorithms. Interactive Technology and Smart Education, 20(2), 209–227. doi:10.1108/ITSE-11-2021-0202.Google ScholarCross Ref
18. Kubsch, M., Krist, C., & Rosenberg, J. M. (2023). Distributing epistemic functions and tasks—A framework for augmenting human analytic power with machine learning in science education research. Journal of Research in Science Teaching, 60(2), 423–447. doi:10.1002/tea.21803.Google ScholarCross Ref
19. Liang, Z., Liu, Z., Shi, H., Chen, Y., Cai, Y., Hong, H., Liang, Y., Feng, Y., Yang, Y., Zhang, J., & Fu, P. (2023). SPOC learner’s final grade prediction based on a novel sampling batch normalization embedded deep neural network method. Multimedia Tools and Applications, 82(7), 9843–9853. doi:10.1007/s11042-022-13628-y.Google ScholarDigital Library
20. Martins, R. M., & Gresse Von Wangenheim, C. (2023). Findings on teaching machine learning in high school: A ten-year systematic literature review. Informatics in Education, 22(3), 421–440.Google Scholar
21. Safarov, F., Kutlimuratov, A., Abdusalomov, A. B., Nasimov, R., & Cho, Y. I. (2023). Deep learning recommendations of E-education based on clustering and sequence. Electronics (Basel), 12(4), 809. doi:10.3390/electronics12040809.Google ScholarCross Ref
22. Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023). A systematic review of teaching and learning machine learning in K-12 education. Education and Information Technologies, 28(5), 5967–5997. doi:10.1007/s10639-022-11416-7.Google ScholarDigital Library
23. Sofi-Karim, M., Bali, A. O., & Rached, K. (2023). Online education via media platforms and applications as an innovative teaching method. Education and Information Technologies, 28(1), 507–523. doi:10.1007/s10639-022-11188-0 PMID:35791317.Google ScholarDigital Library
24. Tzavara, A., Lavidas, K., Komis, V., Misirli, A., Karalis, T., & Papadakis, S. (2023). Using personal learning environments before, during and after the pandemic: The case of “e-me”. Education Sciences, 13(1), 87. doi:10.3390/educsci13010087.Google ScholarCross Ref
25. Wong, R. (2023). When no one can go to school: Does online learning meet students’ basic learning needs? Interactive Learning Environments, 31(1), 434–450. doi:10.1080/10494820.2020.1789672.Google ScholarCross Ref

Index Terms

1. A Course Recommendation Algorithm for a Personalized Online Learning Platform for Students From the Perspective of Deep Learning

   1. Applied computing

      1. Education

   2. Computing methodologies

      1. Machine learning

         1. Machine learning approaches

   3. Information systems

      1. Information retrieval

         1. Retrieval tasks and goals

      2. Information systems applications

      3. World Wide Web

• Representation learning with collaborative autoencoder for personalized recommendation

  Abstract

  In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful ...

  Highlights

  • Two different autoencoders are used to capture characteristics for users and items.
  • Manifold regularization is integrated into autoencoder for user’s features learning.
  • The comprehensive experiments evaluate the effectiveness of ...

  Read More

• Personalized Recommendation Algorithm Using User Demography Information

  WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining

  Personalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography ...

  Read More

• Review of Deep Learning-Based Personalized Learning Recommendation

  IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

  Online learning has become a significant way for learners to acquire knowledge, which enables anybody to learn anywhere and anytime. However, how to choose appropriate content for learning is an interesting issue, especially when one faces massive ...

  Read More