Evaluation of Distance Learning Resilience During COVID-19 Pandemic using ANFIS

Given the growing usage of e-learning systems during COVID-19 epidemic and expansion of internet-based infrastructure, a resilient approach for e-learning systems is highly required. This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate e-learning resilience. In the ANFIS model, five substantial factors including individual, technology, content, agility, and assessment/support factors are considered as fuzzy inputs, while e-learning resilience is considered as a single output. The proposed ANFIS model has been successfully implemented for e-learning resilience measurement during COVID-19 epidemic in virtual Iranian university. Statistical analysis demonstrated that there was no meaningful difference between experts’ opinions and our proposed procedure for e-learning resilience measurement. Sensitivity analysis via the proposed model on changing the different factors showed significant sensitivity to changes in the agility factor. The proposed model can be used in all educational institutions to evaluate the improvement of resilience in e-learning systems. To implement the model for an organization, the values of the designed ANFIS model should be defined specifically for the organization and the corresponding model need to be simulated by examining the involved components and relationships. © 2021, Success Culture Press. All rights reserved.

Combined adaptive neuro-fuzzy inference system and genetic algorithm for e-learning resilience assessment during COVID-19 pandemic

Given the growing use of e-learning and expansion of internet-based infrastructure during COVID-19 epidemic, the need for a resilient approach to e-learning systems is deeply felt. This article introduces a combined technique utilizing adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), named ANFIS-GA, to evaluate e-learning resilience. In the proposed ANFIS model, 22 features from five main factors including individual, technology, content, agility, and assessment/support factors are used as fuzzy inputs, while the e-learning resilience is considered as a single output of the model. To select the most significant features for the evaluation of the e-learning resilience, an evolutionary feature selection based on GA is used. The proposed ANFIS-GA model has been successfully developed for evaluation of e-learning resilience in virtual Iranian university. According to the obtained results, agility is the most important factor, and then, technology and assessment/support factors have the next priorities to evaluate e-learning resilience in virtual Iranian university. Statistical analysis demonstrated that there is no significant difference between the experts' opinion and the resilience obtained via the proposed model. The proposed ANFIS-GA model can be used in any educational institution to evaluate the improvement of resilience in e-learning.