Determinants of Learning Management Systems during COVID-19 Pandemic for Sustainable Education
| Autor: | Nadire Cavus, Yakubu Bala Mohammed, Nasiru Yakubu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Geography Planning and Development Decision tree Developing country TJ807-830 010501 environmental sciences Management Monitoring Policy and Law Machine learning computer.software_genre TD194-195 01 natural sciences Renewable energy sources ensemble modelling Robustness (computer science) GE1-350 0105 earth and related environmental sciences education Artificial neural network Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry 05 social sciences 050301 education COVID-19 Usability artificial intelligence Support vector machine Environmental sciences Sustainability Learning Management Artificial intelligence business 0503 education computer LMS determinants |
| Zdroj: | Sustainability, Vol 13, Iss 5189, p 5189 (2021) Sustainability Volume 13 Issue 9 |
| ISSN: | 2071-1050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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