Clustering Analysis in the Student Academic Activities on COVID-19 Pandemic in Mexico

Abstract

The pandemic caused by theCOVID-19 disease has affected all aspects of the life of the people in every region of the world. The academic activities at universities in Mexico have been particularly disturbed by two years of confinement; all activities were migrated to an online modality where improvised actions and prolonged isolation have implied a significant threat to the educational institutions. Amid this pandemic, some opportunities to use Artificial Intelligence tools for understanding the associated phenomena have been raised. In this sense,we use the K-means algorithm, a well-known unsupervised machine learning technique, to analyze the data obtained from questionaries applied to students in a Mexican university to understand their perception of how the confinement and online academic activities have affected their lives and their learning. Results indicate that the K-means algorithm has better results when the number of groups is bigger, leading to a lower error in the model. Also, the analysis helps to make evident that the lack of adequate computing equipment, internet connectivity, and suitable study spaces impact the quality of the education that students receive, causing other problems, including communication troubles with teachers and classmates, unproductive classes, and even accentuate psychological issues such as anxiety and depression.