Application of data mining techniques to characterize students under the effect of COVID-19
Abstract
In this research, data mining (DM) techniques were integrated, such as hierarchical cluster analysis and logistic regression, to characterize students of an educational program of the Autonomous University of the State of Hidalgo (UAEH) who may be seen as vulnerable due to the fast migration to online education and other repercussions of the actions taken to prevent the spread of COVID-19. For the application of the DM techniques, a survey was carried out on the students to obtain data about the sociodemographic, economic, technical, mental health and academic conditions that allow finding patterns that affect the academic performance of the students. With the results obtained, the existence of 2 groups (clusters) can be observed, one of them with better conditions than the other and also a list of items that carry both weight negatively and positively a student's academic performance being affected.
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