Abstract
One of the biggest problems in higher education is student dropout. Prior to the pandemic, one of the biggest problems for university institutions was the dropout and dropout of many of their students. Today, the situation has become even more critical, as the pandemic has forced many people to drop out of school for a variety of reasons, whether financial or personal. Investigating the causes of dropout with appropriate means to reduce it contributes to decision making within academic management. The objective of this work is to develop a machine learning model that generates early warnings about course loss, which is based on historical data of pupils and students. The model is based on historical data from an undergraduate program that includes, student grades, at various points in time, percentage of course loss in previous semesters, percentage of student loss in previous semesters, subjects passed at the time of evaluating the data, along with student and course average. This would facilitate the identification and internal management of alarms for the early detection of potential dropouts, as well as efficiently display the results found with the execution of these models.
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Notes
- 1.
SENA: The national learning service, SENA, is an entity that offers free training to millions of Colombians who benefit from technical, technological and complementary programmes focused on the economic, technological and social development of the country.
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Bustamante, D., Garcia-Bedoya, O. (2021). Predictive Academic Performance Model to Support, Prevent and Decrease the University Dropout Rate. In: Florez, H., Pollo-Cattaneo, M.F. (eds) Applied Informatics. ICAI 2021. Communications in Computer and Information Science, vol 1455. Springer, Cham. https://doi.org/10.1007/978-3-030-89654-6_16
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