Student Performulator: Classification and Prediction of Academic Performance of Students Using Machine Learning.

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 a 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 model was formulated and simulated using machine learning algorithm, Support Vector Machines (SVM) 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 SVM having 100% accuracy 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.