International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

Predicting the Saudi Student Perception of Benefits of Online Classes during the Covid-19 Pandemic using Artificial Neural Network Modelling

  • 투고 : 2022.02.05
  • 발행 : 2022.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

One of the impacts of Covid-19 on education systems has been the shift to online education. This shift has changed the way education is consumed and perceived by students. However, the exact nature of student perception about online education is not known. The aim of this study was to understand the perceptions of Saudi higher education students (e.g., post-school students) about online education during the Covid-19 pandemic. Various aspects of online education including benefits, features and cybersecurity were explored. The data collected were analysed using statistical techniques, especially artificial neural networks, to address the research aims. The key findings were that benefits of online education was perceived by students with positive experience or when ensured of safe use of online platforms without the fear cyber security breaches for which recruitment of a cyber security officer was an important predictor. The issue of whether perception of online education as a necessity only for Covid situation or a lasting option beyond the pandemic is a topic for future research.

키워드

참고문헌

  1. A. Sun and X. Chen, "Online education and its effective practice: A research review," Journal of Information Technology Education, vol. 15, 2016.
  2. S. Appana, "A review of benefits and limitations of online learning in the context of the student, the instructor and the tenured faculty," International Journal on E-learning, vol. 7, no. 1, pp. 5-22, 2008.
  3. H. Kauffman, "A review of predictive factors of student success in and satisfaction with online learning," Research in Learning Technology, vol. 23, 2015.
  4. C. M. Toquero, "Challenges and opportunities for higher education amid the COVID-19 pandemic: The Philippine context," Pedagogical Research, vol. 5, no. 4, 2020.
  5. J. Daniel, "Education and the COVID-19 pandemic," Prospects, vol. 49, no. 1, pp. 91-96, 2020. https://doi.org/10.1007/s11125-020-09464-3
  6. P. Paudel, "Online education: Benefits, challenges and strategies during and after COVID-19 in higher education," International Journal on Studies in Education, vol. 3, no. 2, pp. 70-85, 2021. https://doi.org/10.46328/ijonse.32
  7. L. Al-Qaisi et al., "Evaluation of e-commerce website functionality using a Mamdani fuzzy system," Engineering, Technology & Applied Science Research, vol. 5, no. 5, pp. 860-863, 2015. https://doi.org/10.48084/etasr.594
  8. A. Alam, "Challenges and possibilities of online education during Covid-19," 2020.
  9. T. Chen, L. Peng, B. Jing, C. Wu, J. Yang, and G. Cong, "The impact of the COVID-19 pandemic on user experience with online education platforms in China," Sustainability, vol. 12, no. 18, p. 7329, 2020. https://doi.org/10.3390/su12187329
  10. L. Zhou, S. Wu, M. Zhou, and F. Li, "'School's Out, But Class' On', The Largest Online Education in the World Today: Taking China's Practical Exploration During The COVID-19 Epidemic Prevention and Control As an Example," Best Evid Chin Edu, vol. 4, no. 2, pp. 501-519, 2020. https://doi.org/10.15354/bece.20.ar023
  11. R. Khalil et al., "The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: a qualitative study exploring medical students' perspectives," BMC medical education, vol. 20, no. 1, pp. 1-10, 2020. https://doi.org/10.1186/s12909-019-1842-1
  12. M. S. Shawaqfeh et al., "Pharmacy students perceptions of their distance online learning experience during the COVID-19 pandemic: a Cross-Sectional Survey Study," Journal of medical education and curricular development, vol. 7, p. 2382120520963039, 2020.
  13. H. Al-Nofaie, "Saudi University Students' Perceptions towards Virtual Education during COVID-19 Pandemic: A Case Study of Language Learning via Blackboard," Arab World English Journal, vol. 11, no. 3, pp. 4-20, 2020. https://doi.org/10.24093/awej/vol11no3.1
  14. E. Lau, L. Sun, and Q. Yang, "Modelling, prediction and classification of student academic performance using artificial neural networks," SN Applied Sciences, vol. 1, no. 9, pp. 1-10, 2019. https://doi.org/10.1007/s42452-018-0001-3
  15. I. Pozon-Lopez, Z. Kalinic, E. Higueras-Castillo, and F. Liebana-Cabanillas, "A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC)," Interactive Learning Environments, vol. 28, no. 8, pp. 1003-1021, 2020. https://doi.org/10.1080/10494820.2019.1636074
  16. I. Akour, M. Alshurideh, B. Al Kurdi, A. Al Ali, and S. Salloum, "Using machine learning algorithms to predict people's intention to use mobile learning platforms during the COVID-19 pandemic: machine learning approach," JMIR Medical Education, vol. 7, no. 1, p. e24032, 2021. https://doi.org/10.2196/24032
  17. M. F. Musso, C. F. Rodriguez Hernandez, and E. C. Cascallar, "Predicting key educational outcomes in academic trajectories: a machine-learning approach," 2020.
  18. J. W. Creswell, Educational research: Planning, conducting, and evaluating quantitative. Prentice Hall Upper Saddle River, NJ, 2002.
  19. A. J. Onwuegbuzie and N. L. Leech, "Linking research questions to mixed methods data analysis procedures," The qualitative report, vol. 11, no. 3, pp. 474-498, 2006.
  20. I. Monitor, "Saudi Arabia's expanding higher education capacity," ed: Retrieved from ICEF website: https://monitor.icef.com/2018/07/saudi ..., 2018.
  21. M. Touahmia, M. Aichouni, A. Alghamdi, L. Kolsi, and H. Alzamil, "A Foresight Study about the Skills and Competencies Needed for Quality Professionals in 2030: An Empirical Study of Saudi Arabia," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6176-6182, 2020. https://doi.org/10.48084/etasr.3713
  22. F. Bagheri, N. Ghafarnia, and F. Bahrami, "Electrocardiogram (ECG) signal modeling and noise reduction using Hopfield neural networks," Engineering, Technology & Applied Science Research, vol. 3, no. 1, pp. 345-348, 2013. https://doi.org/10.48084/etasr.243
  23. T. Chen, L. Peng, X. Yin, J. Rong, J. Yang, and G. Cong, "Analysis of user satisfaction with online education platforms in China during the COVID-19 pandemic," in Healthcare, 2020, vol. 8, no. 3, p. 200: Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/healthcare8030200