USING MACHINE LEARNING TO FORECAST COLLEGE STUDENTS' ACADEMIC PERFORMANCE: THE INFLUENCE OF EXTRACURRICULAR ACTIVITIES AND PREVIOUS ACADEMIC SCORES

Abstract

This study explores the application of machine learning techniques to predict college students academic performance. While traditional models have primarily focused such as extracurricular activities and academic scores, this research expands the analysis by including variables like extracurricular activities and previous academic scores. The study aims to provide a more comprehensive approach to understanding student success by analyzing a features that contribute to academic outcomes. Several machine learning models are applied to predict students' academic success, and their performance is evaluated. The findings show that machine learning models are more effective in identifying students at risk of underperforming compared to traditional methods. These models offer a more understanding of student performance and can help identify at-risk students early in their academic careers. The practical implications of this research are significant for Extracurricular Activities and Previous Academic Scores educational institutions, as early identification enables timely interventions and support, improving retention rates and overall student success. The study contributes to the research on predictive analytics in education, demonstrating the potential of machine learning to enhance decision-making processes and foster better academic outcomes.