Role of Advanced Data Analytics in Higher Education: Using Machine Learning Models to Predict Student Success

This research article explores the pressing issue of college student dropout, employing a suite of predictive modeling techniques to forecast students' likelihood of discontinuing their studies.In an era where higher education plays a crucial role in individual career prospects and societal progress, understanding and mitigating dropout rates is of paramount importance.We leverage a comprehensive dataset, incorporating demographic, socioeconomic, academic, and financial factors, to train and test predictive models: Logistic Regression, Random Forest, Decision Tree Classifier, Support Vector Machine (SVM), and Gradient Boosting.Our analysis reveals that the gradient boosting model outperforms its counterparts in predicting student dropout, achieving the highest precision, recall, and F1 score among the evaluated models.Gradient boosting model correctly identified 94.4% of the student as dropouts.This superiority underscores the gradient boosting's robustness in handling the complex, multidimensional nature of factors influencing college retention.Moreover, it was found that circular units approved in the first semester is the most important factor in determining student success in terms of graduating or dropping out.This is then followed by tuition fees up to date.The previous qualification of students has the lowest predictive power indicating that current circumstances of studentsacademic courses, and finances contributes to students' success more.The findings underscore the potential of machine learning in crafting targeted interventions to support at-risk students, thereby enhancing retention rates and contributing to the broader objectives of educational equity and success.