Optimizing Financial Aid Allocation to Improve Access and Affordability to Higher Education

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.

Publication Title

Journal of Educational Data Mining

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