A machine learning model to predict standardized tests in engineering programs in Colombia
Fecha
2023Resumen
This research develops a model to predict the
results of the national standardized test for Engineering
programs in Colombia. 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, the
application of 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.