A Machine Learning and Explainable AI Approach for Predicting Secondary School Student Performance

Abstract

An essential component of the educational activity is rigorous examination and assessment of students' results, with a potential substantial influence on student growth. This paper offers a predictional model for student's success in secondary education using five classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost, and Naive Bayes, where the data is gathered from two Portuguese school reports and surveys. The two core disciplines (Mathematics subject and Portuguese language) of the dataset were modeled around binary/five-level classification tasks which is imbalanced. The imbalanced dataset is also balanced by using K-Means SMOTE (Synthetic Minority Oversampling Technique) before classification. The test results reveal that obtaining the most outstanding accuracy value is 96.89% of Support Vector Machine (SVM) is superior to Logistic Regression, KNN, XG-Boost, and Naive Bayes. Therefore, it is essential to consider whether a model makes a particular prediction. Thus, we then train an interpretable LIME (Local Interpretable Model-agnostic Explanations) model for all the classifiers and the construction of explainable models can have a major advantage: the model can be confident, the transparency of the model helps to understand the underlying processes for working.