Support System to Predict Student Dropout in Universities

D. Rivero-Albarrán, L. Guerra Torrealba, S. Arciniegas Aguirre, Ortiz Alexander

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva


The objective of this research was to provide a prediction system for the possibility of student dropout at the Pontificia Universidad Católica del Ecuador Sede Ibarra. It is applied research with a mixed approach. It was developed in two phases. In the first phase, the KDD methodology and the Scikit-Learn tool were applied to select the best prediction algorithm (KNN, Decision Tree, Random Forest, SVM, and Neural Network). In the second phase, the information system was built to make use of the model obtained in the first phase, where users will be able to consult the possibility of risks of academic dropout of students. Technologies such as Django, Python, HTML, JavaScript, and MySQL, among others, were used in this study. The results show an information system that allows consultation by the student, by the level of schooling, or by subject, based on neural networks that provide an accuracy of 92%.

Idioma originalInglés
Título de la publicación alojadaCommunication and Applied Technologies - Proceedings of ICOMTA 2022
EditoresPaulo Carlos López-López, Ángel Torres-Toukoumidis, Andrea De-Santis, Óscar Avilés, Daniel Barredo
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas10
ISBN (versión impresa)9789811963469
EstadoPublicada - 2023
EventoInternational Conference on Communication and Applied Technologies, ICOMTA 2022 - Cuenca, Ecuador
Duración: 7 sep. 20229 sep. 2022

Serie de la publicación

NombreSmart Innovation, Systems and Technologies
ISSN (versión impresa)2190-3018
ISSN (versión digital)2190-3026


ConferenciaInternational Conference on Communication and Applied Technologies, ICOMTA 2022

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.


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