Predictive Analytics for Student Performance: A Machine Learning Model for Higher Education

Abstract

The dynamic landscape of higher education necessitates innovative approaches to enhance academic success and student performance. Traditional methods of evaluating and supporting student achievement frequently fall short in addressing the diverse and evolving needs of modern learners. This research explores the application of machine learning (ML) to predict student performance in higher education, aiming to develop a predictive model that can identify at-risk students early and enable targeted interventions. The study analyzes various factors influencing student performance, including demographic information, academic history, behavioral data, and socio-economic status. The proposed model demonstrates a high degree of accuracy, with an accuracy rate of 98.8%, a mean absolute error (MAE) of 0.402, and a root mean square error (RMSE) of 0.202. These metrics underscore the model's precision and reliability in predicting student outcomes. The significance of this research lies in its potential to transform educational practices by providing a data-driven framework for decision-making. Accurate predictions of student performance enable educational institutions to allocate resources more effectively, enhance student engagement, and ultimately improve graduation rates. Furthermore, this study contributes to the growing body of knowledge in educational data mining and learning analytics, offering insights that can be generalized across different educational contexts. This research aims to demonstrate the efficacy of machine learning in enhancing academic success and to provide a roadmap for future studies in this domain.