A Machine Learning Model to Predict Standardized Tests in Engineering Programs in Colombia
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Misorly Soto-Acevedo [1] ; Alfredo Miguel Abuchar-Curi [1] ; Rohemi Alfredo Zuluaga-Ortiz [2] ; Enrique J. Delahoz-Domínguez [3]
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[1] Universidad Tecnológica de Bolívar
Universidad Tecnológica de Bolívar
Colombia
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[2] Universidad del Sinú
Universidad del Sinú
Colombia
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[3] Universidad de la Costa
Universidad de la Costa
Colombia
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- Localización: Revista Iberoamericana de Tecnologías del Aprendizaje: IEEE-RITA, ISSN 1932-8540, Vol. 18, Nº. 3, 2023, págs. 211-218
- Idioma: varios idiomas
- Texto completo no disponible (Saber más ...)
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Resumen
Forecasting of Standardized Test Results for engineering students through Machine Learning This research develops a model to predict the results of Colombia’s national standardized test for Engineering programs. The research made it possible to forecast each student’s results and thus make decisions on reinforcement strategies to improve student performance. Therefore, a Learning Analytics approach based on three stages was developed: first, analysis and debugging of the database; second, multivariate analysis; and third, machine learning techniques. The results show an association between the performance levels in the Highschool test and the university test results. In addition, the machine learning algorithm that adequately fits the research problem is the Generalized Linear Network Model. For the training stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.810, 0.820, 0.813, and 0.827, respectively; in the evaluation stage, the results of the model in Accuracy, AUC, Sensitivity, and Specificity were 0.820, 0.820, 0.827 and 0.813 respectively.
