A HYBRID PREDICTION MODEL FOR CLASSIFYING STUDENT’S ACADEMIC PERFORMANCE USING VOTING ENSEMBLE METHOD
DOI:
https://doi.org/10.33003/fjs-2026-1003-4751Keywords:
Hybrid Prediction, Classification, K-Nearest Neighbour (K-NN), Naïve Bayes (NB), Ensemble Method, Academic PerformanceAbstract
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.
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