A Machine Learning-Based Predictive Model for the Classification of Academic Performance of Students

Abstract

Predicting student academic performance is critical for enhancing personalized learning and improving educational

outcomes. Traditional assessment methods, while useful, often fail to capture the complex factors influencing

performance, such as socio-economic background and engagement metrics. This study explores the development of a

predictive model using an ensemble of machine learning algorithms to classify students' academic performance in

higher institutions. By leveraging data collected from Department of Computer Science, Tai Solarin University of

Education records, relevant features were selected using the mutual information method. The ensemble model was

formulated and simulated using multiple machine learning algorithms such as Naïve Bayes (NB), Support Vector

Machines (SVM) and Decision Trees (DT) in the Google CoLaboratory environment. The model’s predictive accuracy

was evaluated based on key performance metrics, including accuracy, precision, and F-measure. Results indicate that

the ensemble approach outperforms single-model methods by enhancing prediction robustness and reducing variance.

This study demonstrates the effectiveness of machine learning techniques in identifying at-risk students early with NB

and SVM having 100% accuracy respectively, allowing for timely interventions and improved resource allocation.

Moreover, it contributes to evidence-based decision-making in educational institutions, helping to optimize learning

experiences and boost student retention rates.