Are You an Early Dropper or Late Shopper? Mining Enrollment Transaction Data to Study Procrastination in Higher Education

Educational data mining increasingly leverages enrollment data for higher education applications. However, these data describe final end-of-semester course selections, not the often complex enrollment activities leading up to a finalized schedule. Fine-grain transaction data of student waitlist, add, and drop actions during academic semester planning can explain the decision-making processes that lead to mismanaged course selection (e.g., dropping a course late with an institutional penalty). The present study investigates student late drops through the lens of procrastination. Results suggest that students who procrastinate on enrolling in courses are likelier to drop courses late. Further, students who enrolled late had a larger predicted workload than their nominal enrollment based on credit hours would suggest, pointing to potential mismanagement in the workload these students take on. These findings motivate the hypothesis that academic advising interventions that support students in academic planning could mitigate the adverse effects of procrastination on academic outcomes.