UMBC ebiquity
ASEE Mid Atlantic Section Conference
WIP: The Impact of Financial Aid and Academic Pathways on Graduation
March 28, 2026
Our work seeks to find out whether we can predict a student's graduation in STEM majors using AI, specifically in Engineering and Computing, based on both academic factors known at admission time and financial factors that the institution can control. We create an integrated predictive modeling system that uses financial assistance in conjunction with academic preparation and enrollment methods to determine student success as measured by graduation. The dataset contains anonymized records of 13,694 students who matriculated at our university from 2013 to 2019. For each student, we have data on their high school GPA, placement test results, standardized test results, Advanced Placement coursework, demographic details, and multiple years of financial aid records. The pre-enrollment and first-term variables underwent multiple stages of data preparation which included cleaning, leakage prevention, feature engineering, and de-duplication. We then trained machine learning models on these data.
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