Optimizing Financial Aid Allocation to Improve Access and Affordability to Higher Education
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Abstract
The allocation of merit-based awards and need-based aid is important to both universities and students
who wish to attend the universities. Current approaches tend to consider only institution-centric objectives
(e.g. enrollment, revenue) and neglect student-centric objectives in their formulations of the problem.
There is lack of consideration to the need to improve access and affordability to higher education.
Previously, we contributed a metaheuristic and machine learning approach for optimizing strategies that
allocate merit-based awards and need-based aid. The approach can be used to optimize both institutioncentric
(e.g. enrollment and revenue) and student-centric objectives (affordability and accessibility to
higher education). We now employed an improved version of this approach to explore comprehensively a
recent admission dataset from our university. We showed that current applicants depended very much on
financial sources other than federal and institution aid to attend the university. This potentially created a
financial burden for many of these applicants. We identified seven budget-friendly strategies that promise
to increase access to higher education significantly by more than 100%, while still keeping it affordable
for students and limiting a budget increase to less than 7%. Additionally, we identified a total of 111
strategies, including those that benefit from more aggressive changes in the budget to obtain higher increases
in enrollment, revenue, and/or higher affordability and accessibility for students. This method
may be used by other institutions in ways that best fit their institutional objectives and students’ profiles.
How to Cite
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institutional data analytic, financial aid allocation, financial aid optimization, scholarship distribution, enrollment management
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