Apply Data Analytics to Schedule Best-suited Classes for Students with Different Academic Histories

Abstract

This Innovative Practice Full Paper presents our work on how to apply data analytics to schedule best-suited classes for students, especially the working adult students, with different academic histories. In this computer-technology-driven economy, many working adults are going back to school to complete their college degrees. They usually bring various numbers of transfer credits with them. Often a group of students enrolled at the same time will end up in different classes. The working adult students with different needs make schools with a limited number of classrooms difficult to predict their course schedule. Also, manually scheduling courses for such students not only consumes a large amount of time, but also increases the chance for human error in the scheduling process. The paper will present our software’s architecture, functionality, algorithm, as well as the results of some Use Cases. The future work will allow admission staff to evaluate the “what-if” scenarios of working adult students based on their future working and/or family situations to foresee how they might plan ahead for their schooling, so that they can balance both educational goals and other priorities. All of these will effectively support student-centered education and have a positive impact on student retention.