Leveraging Feature Selection and Ensemble Learning to Predict Secondary School Achievement: A Comparative Study of Three Grade Granularities

Predictive analytics has become increasingly important in educational decision-making, supporting at-risk identification and adaptive tutoring. The accurate early prediction of school achievement can enable timely intervention. Using the Math Students dataset, which contains data on students from two Portuguese secondary schools, we model three categorical outcomes derived from the students’ final grade, namely the final grade level (low, medium, high), its qualitative evaluation (fail, satisfactory, good, excellent), and the final pass/fail outcome. After preprocessing, three filter methods—Correlation-Based Feature Subset Selection (CFS), Correlation Attribute Evaluation (CorrEval), and Information Gain (InfoGain)—are applied to reduce the dimensionality of the datasets. Nine classifiers (Naive Bayes, Logistic, MLP, SMO, IBk, Bagging, J48, Random Forest, Random Tree) are evaluated using ten-fold cross-validation in the Waikato Environment for Knowledge Analysis (Weka) platform. Random Forest with InfoGain achieves 90.7% accuracy on the three-band task, while Bagging with InfoGain achieves 92.5% on the binary pass/fail outcome, outperforming benchmarks in prior Educational Data Mining (EDM) studies. Results confirm that prior academic performance indicators (first- and second-period grades) and failure history dominate predictive power and contribute substantially to the success of ensemble models, particularly when paired with feature selection methods that reduce noise and highlight relevant attributes.