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Pbkp_rediction model of university dropout through data analytics: Strategy for sustainability

Abstract:

Data analytics enables organizations to make timely and sound decisions to stay or grow in their markets. Educational institutions must retain their students, because without them the academy does not make sense. This research aims to develop a prototype of a pbkp_redictive model for university dropout from the evaluation of five data analytics algorithms (KNN, decision tree, random forest, SVM and neural networks), considering the independent variables, characteristics grouped as personal-cognitive, academic-organizational and socioeconomic. It is an applied research, with a mixed focus and where the survey technique and the KDD data mining methodology were applied. The resulting model is the one that is based on neural networks and provides an accuracy of 92%, precision of 90% and f1 of 90%, transforming it into a robust model with a very low percentage of error.