Student profile modeling using boosting algorithms

Abstract

The student profile has become an important component of education systems. Many education systems objectives, such as e-recommendation, e-orientation, e-recruitment, and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context, where several studies have been carried out either for classification, prediction, or clustering purpose. In this paper, the authors present a comparative study of different boosting algorithms, which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher score, information gain combined with recursive feature elimination to enhance the preprocessing task and models' performances. Using multi-label dataset to predict the class of the student performance in mathematics. This article shows that the light gradient boosting machine (LightGBM) algorithm achieved the best performance when using information gain with recursive feature elimination method compared to the other boosting algorithms.