Predictive model for students’ academic performance using machine learning approach

Article ID: 4181

Keywords: students, performance, prediction, machine learning algorithms, datasets

Abstract

The early prediction of students' academic performance using machine learning has emerged as a valuable approach for identifying at-risk learners and enabling timely intervention. Many factors, such as students' academic background, prior performance, institutional policies, and learning environment, influence educational outcomes; their complex interplay remains inadequately understood in many contexts. This study aimed to explore the effectiveness of machine learning algorithms in predicting students' academic performance at Adamawa State University, Mubi, Adamawa State, Nigeria. This study used 1,730 datasets from the academic records of first-year students from the Faculty of Science for the 2024/2025 academic session. The study split the datasets into 80% training and 20% for testing. Data were analysed using the Waikato Environment for Knowledge Analysis (Weka) and Python. The model was evaluated using students' cumulative grade point averages (CGPAs) from the academic session results. The machine learning algorithms used were Logistic Regression (LR), Random Forest (RF), Decision Trees (DT), Naïve Bayesian (NB), and Support Vector Machines (SVM). Experimental results based on various performance metrics indicate that the SVM model achieved the best result with an accuracy of 0.92, precision of 0.92, recall of 0.93, and F1-score of 0.93. The results revealed that the SVM approach outperforms individual benchmark methods and provides robust insight into factors that determine academic success. The findings offer evidence-based guidance for educators, departments, faculties, institutional management, and policymakers to design targeted interventions to improve learning outcomes.