Application of Adaptive Neuro-Based Fuzzy Inference System to Evaluate the Resilience of E-learning in Education Systems, During the Covid-19 Pandemic

Education systems in the world are enduring COVID-19 induced perturbations and consequences. Given the growing use of E-learning during COVID-19 epidemic and expansion of Internet-based infrastructure, the need for a resilient approach to e-learning systems is deeply felt. This paper aims to address the issue of how to provide a model for evaluating the resilience of E-learning in Iranian virtual universities during the outbreak of coronavirus employing an Adaptive Neuro-Based Fuzzy Inference System (ANFIS). In the present paper, 5 substantial factors including individual, assessment and support, content, agility, and technology were identified as inputs, and e-learning resilience was considered as single output. Moreover, ANFIS was employed to model the resilience of E-learning systems. Findings revealed almost medium to low degree of resilience for the e-learning system established in Iran’s virtual university. Statistical analysis demonstrated that there was no meaningful difference between experts’ opinions and our proposed procedure for E-learning resilience measurement. The proposed model showed significant sensitivity to changes in agility. Therefore, agility should be considered as the first priority in achieving the desired level of resilience for the e-learning systems of the Iranian virtual university.