Prediction of school efficiency rates through ensemble regression application

Abstract

Educational data mining is concerned with developing, researching, and applying automated methods to detect patterns in collections of educational data, gaining insights into and explaining phenomena in this scenario. The present study describes the application of the prediction of educational indicators in the Brazilian scenario through ensemble models. Ensemble models usually result in better accuracy and are more stable than individual techniques, since they combine the prediction of their components by providing a result more robust. The first model we developed combining parametric regression techniques with baselevel learners. The second model uses the set of methods found in the literature in a Stacking regression application formed by parametric and non-parametric techniques. We compare these models, and the results indicate a smaller prediction error for our Stacking model in most of the scenarios studied.