A HYBRID PREDICTION MODEL FOR CLASSIFYING STUDENT’S ACADEMIC PERFORMANCE USING VOTING ENSEMBLE METHOD - Oruh, Jane Ngozi; Iwuji, Basil Chukwuemeka (FUDMA Journal of Sciences, 2026)
Single classification algorithms for predicting students’ academic performance often face limitations in accuracy and robustness, especially with high-dimensional educational data. This study proposes a hybrid prediction model that integrates K-Nearest Neighbour (K-NN) and Naïve Bayes (NB) algorithms using a soft voting ensemble technique to classify students’ academic performance into high, average, and low categories. The Cross-Industry Standard Process for Data Mining (CRISP-DM) and Object-Oriented Analysis and Design Methodology (OOADM) guided the study. Primary data were collected from 240 third-year nursing students of Abia State University, Uturu, across two academic semesters, using 38 demographic, academic, social, and prior performance features. The models were implemented in Python using Scikit-learn and related libraries, with an 80:20 train-test split after preprocessing and feature scaling. Performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices. Results show that the hybrid model achieved superior performance, attaining 100% accuracy and outperforming the standalone K-NN model. The classification results indicated that 51.7% of students were high performers, 10.8% average, and 37.5% low. The deployed system provides performance-based advisory support and demonstrates strong potential for early intervention, personalized learning, and academic planning in educational institutions.
Keywords: Hybrid Prediction; Classification; K-Nearest Neighbour (K-NN); Naïve Bayes (NB); Ensemble Method; Academic Performance