Early detection of students’ failure using Machine Learning techniques

Abstract

The educational system determines one of the significant strengths of an advanced society. A country with a lack of culture is less competitive due to the inequality suffered by its people. Institutions and organizations are putting their efforts into tackling that problem. Nevertheless, it is not an easy task to ascertain why their students have failed or what are the conditions that affect such situations. In this work, an intelligent system is proposed to predict academic failure by using student information stored by the Industrial University of Santander (Colombia). The prediction model is powered by the XGBoost algorithm, where a TOPSIS-based feature extraction and ADASYN oversampling have been conducted. Hyperparameters of the classifier were tuned by a cross-validated grid-search algorithm. We have compared our results with other decision-tree classifiers and displayed the feature importance of our intelligent system as an explainability phase. In conclusion, our intelligent system has shown a superior performance of our prediction model and has indicated to us that economic, health and social factors are decisive for the academic performance of the students.