MACHINE LEARNING-BASED DROPOUT PREDICTION FOR UNDERGRADUATES - Soni, Manish; Jain, Nilesh (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Increasing rates of undergraduate dropout pose a danger to the credibility, financial stability, and future opportunities of higher education institutions. To address this critical issue, our study use machine learning to predict which students would withdraw from a course. Factors influencing student retention include socioeconomic status, degree of participation, and academic performance, according to our examination of institutional records and surveys. The research constructs prediction models by using neural networks, decision trees, random forests, and logistic regression. The accuracy, precision, recall, F1 score, and ROC-AUC are evaluated for these models, while the robustness and reliability are tested using cross-validation. Our study shows that student dropouts may be predicted by looking at academic indicators, social factors, and engagement metrics. The most effective strategy is providing schools with individualized interventions to boost retention rates. Educational data mining and predictive analytics are both advanced by this research, which offers administrators and legislators options to reduce dropout rates. This study adds to the growing body of evidence that machine learning algorithms have the potential to aid in the early detection and prompt intervention of children at risk. Despite its useful findings, the study acknowledges the limitations of its data collection methods and calls for more investigation into how to improve prediction models. It is possible that future studies may use more diverse datasets and more robust machine learning techniques to enhance the accuracy of predictions. As this research demonstrates, machine learning has the potential to revolutionize the educational system by opening the door to data-driven solutions that boost both student success and school resilience.