Predicting Student Retention in Higher Education Using Machine Learning

Student retention is a critical concern for higher education institutions worldwide, impacting both institutional success and student outcomes. High dropout rates can lead to significant financial losses for universities and detrimental effects on students’ personal and professional futures. Predicting student retention accurately enables institutions to proactively address factors leading to dropouts and implement targeted interventions to support at-risk students. This study addresses the problem of student retention prediction by leveraging advanced machine learning techniques. Specifically, we utilized a RandomForestClassifier to analyze a comprehensive dataset of student records, which includes various features related to demographics, academic performance, and other relevant factors influencing student retention. Our methodology involved several steps: data preprocessing to encode categorical variables and scale numerical features, hyperparameter tuning using GridSearchCV to optimize the model, and evaluation of the model's performance using metrics such as accuracy, precision, recall, F1-score, and ROC curves. Visualizations were generated to provide deeper insights into the model's performance and behavior. The results of our analysis indicate that the RandomForestClassifier can effectively predict student retention, achieving an accuracy score of 76.72%. This performance demonstrates the model's potential as a valuable tool for higher education institutions aiming to improve student retention rates. By integrating such predictive models into their student support systems, universities can identify at-risk students early and provide targeted support to enhance their chances of success. This proactive approach can lead to better academic outcomes for students and reduced financial losses for institutions due to dropouts. Future research could explore the integration of additional features and alternative machine learning models to further improve predictive accuracy and applicability in diverse educational contexts.