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

Abstract

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.