Volume 12, Issue 4 (April 2025), Pages: 146-151

----------------------------------------------

 Technical Note

Evaluating the effectiveness of e-learning implementation in Saudi schools using machine learning

 Author(s): 

 Weam M. Binjumah *

 Affiliation(s):

  Applied College, Taibah University, Madina 42353, Saudi Arabia

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0003-3723-644X

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.04.016

 Abstract

The adoption of e-learning methods in Saudi schools has gained increasing importance in recent years, particularly due to the global impact of the COVID-19 pandemic. While e-learning offers several advantages, such as enhanced accessibility and flexibility, it also presents various challenges that require careful consideration. This study aims to identify the primary challenges Saudi schools face when implementing e-learning methods and to suggest potential solutions to address these challenges. The study employs a literature review approach for both data collection and analysis. Additionally, it aims to highlight various models and frameworks proposed in the literature that address e-learning challenges using machine learning (ML) techniques. The findings indicate that Saudi schools encounter several issues, including infrastructure problems, technological challenges, inadequate teacher training and support, low student engagement and motivation, and concerns related to academic integrity and assessment.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 E-learning challenges, Infrastructure issues, Teacher support, Student motivation, Machine learning

 Article history

 Received 25 November 2024, Received in revised form 28 March 2025, Accepted 24 April 2025

 Acknowledgment

No Acknowledgment.

  Compliance with ethical standards

  Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Binjumah WM (2025). Evaluating the effectiveness of e-learning implementation in Saudi schools using machine learning. International Journal of Advanced and Applied Sciences, 12(4): 146-151

  Permanent Link to this page

 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4 

 Tables

  Table 1  Table 2  Table 3  Table 4 

----------------------------------------------   

 References (13)

1. Abu Saa A, Al-Emran M, and Shaalan K (2019). Factors affecting students’ performance in higher education: A systematic review of predictive data mining techniques. Technology, Knowledge and Learning, 24(4): 567-598.  https://doi.org/10.1007/s10758-019-09408-7    [Google Scholar]
2. Alfallaj FSS (2020). E-learning challenges in Saudi Arabia: Exploration of factors with special reference to EFL. TESOL International Journal, 15(3): 20-40.    [Google Scholar]
3. Alharbi O and Lally V (2017). Adoption of e-learning in Saudi Arabian university education: Three factors affecting educators. European Journal of Open Education and E-Learning Studies, 2(2): 63-85.    [Google Scholar]
4. Allen M and Sites R (2012). Leaving ADDIE for SAM: An agile model for developing the best learning experiences. Association for Talent Development, Alexandria, USA.    [Google Scholar]
5. Al-Shehri AH (2023). Challenges and issues of public education in Saudi Arabia during COVID-19. Creative Education, 14(2): 245-257.  https://doi.org/10.4236/ce.2023.142017    [Google Scholar]
6. Eichhorn S and Matkin GW (2016). Massive open online courses, big data, and education research. New Directions for Institutional Research, 167: 27-40.  https://doi.org/10.1002/ir.20152    [Google Scholar]
7. Hai-tao P, Ming-qu F, Hong-bin Z, Bi-zhen Y, Jin-jiao L, Chun-fang L, Yan-ze Z, and Rui S (2021). Predicting academic performance of students in Chinese-foreign cooperation in running schools with graph convolutional network. Neural Computing and Applications, 33: 637-645.  https://doi.org/10.1007/s00521-020-05045-9    [Google Scholar]
8. Hooshyar D, Pedaste M, and Yang Y (2019). Mining educational data to predict students’ performance through procrastination behavior. Entropy, 22(1): 12.  https://doi.org/10.3390/e22010012    [Google Scholar] PMid:33285787 PMCid:PMC7516418
9. Pandey M and Taruna S (2018). An ensemble-based decision support system for the students’ academic performance prediction. In: Saini A, Nayak A, and Vyas R (Eds.), ICT based Innovations: Advances in Intelligent Systems and Computing: 163-169. Volume 653, Springer, Singapore, Singapore.  https://doi.org/10.1007/978-981-10-6602-3_16    [Google Scholar]
10. Rane NL, Paramesha M, Choudhary SP, and Rane J (2024). Artificial intelligence, machine learning, and deep learning for advanced business strategies: A review. Partners Universal International Innovation Journal, 2(3): 147-171.  https://doi.org/10.2139/ssrn.4835661    [Google Scholar]
11. Snyder H (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104: 333-339.  https://doi.org/10.1016/j.jbusres.2019.07.039    [Google Scholar]
12. Spatioti AG, Kazanidis I, and Pange J (2022). A comparative study of the ADDIE instructional design model in distance education. Information, 13(9): 402.  https://doi.org/10.3390/info13090402    [Google Scholar]
13. Wertz RE (2022). Learning presence within the community of inquiry framework: An alternative measurement survey for a four-factor model. The Internet and Higher Education, 52: 100832.  https://doi.org/10.1016/j.iheduc.2021.100832    [Google Scholar]