Predicting Computer Engineering students' dropout in Cuban Higher Education with pre-enrollment and early performance data

Niurys Lázaro Alvarez, Zoraida Callejas, David Griol


We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not promoting). We have also tested a combination of classification features for training and testing decision trees and neural networks; including information obtained at the time of enrollment, after the first semester and after the first academic year. Our results show a considerable accuracy using all features (96.71%). Using only the features available at the time of enrolment and after the first semester we obtain very positive results (68.86% and 93.85% accuracy respectively) with a high recall of non-promoting students. Thus, it is possible to obtain an early assessment of the risk of dropout that can help defining prevention policies.


Dropout, retention, promotion, higher education, data analysis, computer engineering, automatic classification

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Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Technology and Science Education, 2011-2024

Online ISSN: 2013-6374; Print ISSN: 2014-5349; DL: B-2000-2012

Publisher: OmniaScience