A Machine Learning Model to Predict Standardized Tests in Engineering Programs in Colombia

Abstract

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.