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.