Aprendizaje automático aplicado al análisis del confinamiento por COVID-19 y su relación con el rendimiento de los estudiantes de educación superior

Abstract:

This article analyzes the academic performance of third level students and the effect of the COVID-19 pandemic due to the change in study modality. Using the Lesmeister methodology, the k-means and hierarchical clustering algorithms were applied to a sample of 400 records corresponding to two study periods, pre-pandemic and in pandemic, of the students of the" Instituto Superior Tecnológico Sudamericano" in the city of Loja, obtaining three groupings related to academic performance. Regarding a general analysis of the groups, it can be established that no significant difference was found in the variables of gender, ethnicity, type of school, employment status, scholarship holders, total household income and household members. Keywords: machine learning; academic performance; pandemic; COVID-19; higher education. 1. Introducción En el año 2020 la pandemia de COVID-19 irrumpió en la vida cotidiana …

Año de publicación:

2022

Keywords:

Fuente:

google