Student dropout in times of Covid-19

This article presents a study on student dropout in COVID19 time. As a case study, the socioeconomic and academic performance data of students of the State Technical University of Quevedo, Ecuador in the periods from 2019 to 2022 where educational activities were developed in virtual modality are analyzed. With the requested information, the objective variable (Permanence) was constructed and a process of exploration and analysis of the information was carried out. From this process, 58 variables were chosen and used for the construction of two classification models using Decision Trees and Logistic Regression. Of the algorithms studied, Decision Trees was the best model for identifying students who dropped out, with a value higher than 95% correct classification

Coronavirus disease 2019 (COVID-19 · Decision tree · Dropout (neural networks · Logistic regression · Machine learning · Mathematics education · Process (computing · Socioeconomic status · Sociology · Statistics · Variable (mathematics · Business, Innovation, and Economy · Computer Science · Demography · Educational Innovations and Technology · Educational Outcomes and Influences · Mathematics · Medicine · Psychology · Artificial Intelligence