Predicting computer engineering students' dropout in cuban higher education with pre-enrollment and early performance data
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...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/334597 |
| Acceso en línea: | https://hdl.handle.net/2117/334597 https://dx.doi.org/10.3926/jotse.922 |
| Access Level: | acceso abierto |
| Palabra clave: | College dropouts Computer engineering Education, Higher Dropout Retention Promotion Higher education Data analysis Automatic classification Abandó dels estudis (Ensenyament universitari) Enginyeria d'ordinadors Ensenyament universitari Àrees temàtiques de la UPC::Ensenyament i aprenentatge::Psicologia de l'educació::Motivació en l'educació Àrees temàtiques de la UPC::Ensenyament i aprenentatge::Ensenyament universitari |
| Sumario: | 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 |
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