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Determinants of Learning Management Systems during COVID-19 Pandemic for Sustainable Education

Author

Listed:
  • Nadire Cavus

    (Computer Information Systems Research and Technology Centre, Near East University, Nicosia 99138, Cyprus
    Department of Computer Information Systems, Near East University, Mersin 10, Turkey)

  • Yakubu Bala Mohammed

    (Department of Computer Information Systems, Near East University, Mersin 10, Turkey
    Department of Computer Science, Abubakar Tatari Ali Polytechnic, Bauchi 0094, Nigeria)

  • Mohammed Nasiru Yakubu

    (Department of Information Systems, American University of Nigeria, 98 Lamido Zubairu Way, Yola 640231, Nigeria)

Abstract

Research has shown that effective and efficient learning management systems (LMS) were the main reasons for sustainable education in developed nations during COVID-19 pandemic. However, due to slow take-up of LMS many schools in developing countries, especially Africa were completely shut down due to COVID-19 pandemic. To fill this gap, 4 AI-based models; Support Vector Machine (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Boosted Regression Tree (BRT) were developed for prediction of LMS determinants. Nonlinear sensitivity analysis was employed to select the key parameters of the LMS determinants data obtained from 1244 schools’ students. Five statistical indices were used to validate the models. The performance results of the four developed AI models discovered facilitating conditions, attitude towards LMS, perceived enjoyment, users’ satisfaction, perceived usefulness, and ease of use to be the most significant factors that affect educational sustainability in Nigeria during COVID-19. Further, single model’s performance results comparison proved that SVM has the highest prediction ability compared to GPR, ANN, and BRT due to its robustness in handling data uncertainties. The study results identified the factors responsible for total schools’ closure during COVID-19. Future studies should examine the application of other linear and other nonlinear AI techniques.

Suggested Citation

  • Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "Determinants of Learning Management Systems during COVID-19 Pandemic for Sustainable Education," Sustainability, MDPI, vol. 13(9), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5189-:d:549622
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    References listed on IDEAS

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    1. Nadire Cavus & Abdullahi S. Sani & Yusuf Haruna & Abdulmalik A. Lawan, 2021. "Efficacy of Social Networking Sites for Sustainable Education in the Era of COVID-19: A Systematic Review," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    2. William H. DeLone & Ephraim R. McLean, 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, INFORMS, vol. 3(1), pages 60-95, March.
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    Cited by:

    1. Sayed Fayaz Ahmad & Heesup Han & Muhammad Mansoor Alam & Mohd. Khairul Rehmat & Muhammad Irshad & Marcelo Arraño-Muñoz & Antonio Ariza-Montes, 2023. "Impact of artificial intelligence on human loss in decision making, laziness and safety in education," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    2. Nadire Cavus & Nuriye Sancar, 2023. "The Importance of Digital Signature in Sustainable Businesses: A Scale Development Study," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    3. Jian-Hong Ye & Yi-Sang Lee & Chiung-Ling Wang & Weiguaju Nong & Jhen-Ni Ye & Yu Sun, 2023. "The Continuous Use Intention for the Online Learning of Chinese Vocational Students in the Post-Epidemic Era: The Extended Technology Acceptance Model and Expectation Confirmation Theory," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    4. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    5. Nadire Cavus & Yakubu Bala Mohammed & Abdulsalam Ya’u Gital & Mohammed Bulama & Adamu Muhammad Tukur & Danlami Mohammed & Muhammad Lamir Isah & Abba Hassan, 2022. "Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness," Sustainability, MDPI, vol. 14(10), pages 1-21, May.

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