Model Prediction of Academic Performance for First Year Students

The aim of this paper was to obtain a model to predict new students´ academic performance taking into account socio-demographic and academic variables. The sample contained records of first semester students at a School of Engineering from a range of students´ generations. The data was divided into three groups: students who passed none or up to two courses (low), students who passed three or four courses (middle), and students who passed all five courses (high). By using data mining techniques, the Naïve Bayes classifier and the Rapid miner software, we obtained a model of almost 60% accuracy. This model was applied to predict the academic performance of the following generation. After checking the results of the predictions, 50% were classified as correct. However, we observed that, for students of certain engineering majors of high and low groups, the model´s accuracy was higher than 70%.