Data fusion for prediction of variations in students grades

Abstract

Considering the undeniable relevance of education in today’s society, it is of great interest to be able to predict the academic performance of students in order to change teaching methods and create new strategies taking into account the situation of the students and their needs. This study aims to apply data fusion to merge information about several students and predict variations in their Portuguese Language or Math grades from one trimester to another, that is, whether the students improve, worsen or maintain their grade. The possibility to predict changes in a student’s grades brings great opportunities for teachers, because they can get an idea, from the predictions, of possible drops in grades, and can adapt their teaching and try to prevent such drops from happening. After the creation of the models, it is possible to suggest that they are not overfitting, and the metrics indicate that the models are performing well and appear to have high level of performance. For the Portuguese Language prediction, we were able to reach an accuracy of 97.3%, and for the Mathematics prediction we reached 95.8% of accuracy.