Scholarships Assignments: An Important Challenge of Resources Allocation. Case Study: Completion Prediction System for Mexican Institution

This exploratory study proposes a system that uses machine learning and data science to predict which students from e-Learning programs will finish their studies, allowing institutions to allocate scholarship resources more effectively. The system is built and tested with data from the National Autonomous University of Mexico (UNAM), with their Open University and Distance Education System, and shows promising results, meeting business restrictions for a false positive rate under 2%. This approach can be used to improve resource allocation for education in Mexico and worldwide. By leveraging technology to evaluate data and make educated choices, organizations can more effectively identify students who would gain the most from scholarships and maximize their educational investment.