Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
