Knowledge networks in the era of Covid-19

Cruz García Lirios
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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.
Covid-19 时代的知识网络
粗略地说,知识网络是通过神经网络来解释的,在这种网络中,考虑到输入层、中间层或隐藏层以及输出层之间的差异,确定了学习的程度。对墨西哥中部一所公立大学的 300 名学生、管理人员和教师进行了非实验性、横断面和探索性研究。研究结果表明,一个输入层单位对三个输出层单位的因子不对称,这表明围绕知识网络存在着很大程度的学习。不过,隐藏层周围也有机会,因为其单元揭示的信息处理减少了输入层的不确定性,放大了输出层的知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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