Knowledge networks in the era of Covid-19

Abstract

Roughly speaking, knowledge networks are explained from a neural network in which degrees of learning are established, considering the differences between the input layer, the intermediate or hidden layer and the output layer. A non-experimental, cross-sectional and exploratory study was carried out with a non-probabilistic selection of 300 students, managers and teachers from a public university in central Mexico. The results show a factorial asymmetry of one input layer unit for three output layer units, suggesting that there is a significant degree of learning around the knowledge network. However, there are areas of opportunity around the hidden layer, since its units reveal information processing that reduces the uncertainty of the input layer and amplifies the knowledge of the output layer.