Optimizing Financial Aid Allocation to Improve Access and Affordability to Higher Education
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Published
Dec 18, 2022
Vinhthuy Phan
Laura Wright
Bridgette Decent
https://orcid.org/0000-0001-6108-1228
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
Phan, V., Wright, L., & Decent, B. (2022). Optimizing Financial Aid Allocation to Improve Access and Affordability to Higher Education. Journal of Educational Data Mining, 14(3), 26–51. https://doi.org/10.5281/zenodo.7304855
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Keywords
institutional data analytic, financial aid allocation, financial aid optimization, scholarship distribution, enrollment management
References
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ANTONS, C. M. AND MALTZ, E. N. 2006. Expanding the role of institutional research at small private universities: A case study in enrollment management using data mining. New Directions for Institutional Research 2006, 131, 69–81.
ASSALONE, A., DESHAWN, P., AND MCELROY, B. 2018. Unexpected hurdles: Unpacking the price tag of college affordability. Policy brief, Southern Education Foundation, Atlanta, GA.
AULCK, L., NAMBI, D., AND WEST, J. 2020. Increasing enrollment by optimizing scholarship allocations using machine learning and genetic algorithms. In Proceedings of the 13th International Conference on Educational Data Mining, A. N. Rafferty, J. Whitehill, V. Cavalli-Sforza, and C. Romero, Eds. International Educational Data Mining Society, Durham, United Kingdom, 29–38.
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BASU, K., BASU, T., BUCKMIRE, R., AND LAL, N. 2019. Predictive models of student college commitment decisions using machine learning. Data 4, 2, 65.
CHANG, L. 2006. Applying data mining to predict college admissions yield: A case study. New Directions for Institutional Research 131, 53–68.
CHEN, T. AND GUESTRIN, C. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16.Association for Computing Machinery, New York, NY, USA, 785–794.
DAVIS, D. J., GREEN-DERRY, L. C., AND JONES, B. 2013. The impact of federal financial aid policy upon higher education access. Journal of Educational Administration and History 45, 1, 49–57.
DELANEY, J. A. 2014. The role of state policy in promoting college affordability. The Annals of the American Academy of Political and Social Science 655, 1, 56–78.
DESJARDINS, S. L. 2002. An analytic strategy to assist institutional recruitment and marketing efforts. Research in Higher education 43, 5, 531–553.
DEVASENA, C. L., SUMATHI, T., GOMATHI, V., AND HEMALATHA, M. 2011. Effectiveness evaluation of rule based classifiers for the classification of iris data set. Bonfring International Journal of Man Machine Interface 1, 05–09.
FRIEDMAN, J. H. 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics 29, 5, 1189–1232.
GIDLEY, J. M., HAMPSON, G. P., WHEELER, L., AND BEREDED-SAMUEL, E. 2010. From access to success: An integrated approach to quality higher education informed by social inclusion theory and practice. Higher Education Policy 23, 1, 123–147.
GOENNER, C. F. AND PAULS, K. 2006. A predictive model of inquiry to enrollment. Research in Higher Education 47, 8, 935–956.
HOSSLER, D. 2000. The role of financial aid in enrollment management. In New Directions for Student Services, M. Saddlemire, Ed. Vol. 2000. Wiley Online Library, Hoboken, NJ, USA, 77–90.
HOSSLER, D. 2009. Enrollment management & the enrollment industry. College and University 85, 2, 2–9.
HOVEY, H. A. 1999. State spending for higher education in the next decade: The battle to sustain current support. Technical report, National Center for Public Policy and Higher Education, San Jose, CA.
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JOHNSON, A. W. AND JACOBSON, S. H. 2002. On the convergence of generalized hill climbing algorithms. Discrete Applied Mathematics 119, 1-2, 37–57.
JUELS, A. AND WATTENBERG, M. 1995. Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. Advances in Neural Information Processing Systems 8, 430–436.
KIRKPATRICK, S., GELATT JR, C. D., AND VECCHI, M. P. 1983. Optimization by simulated annealing. Science 220, 4598, 671–680.
LESLIE, L. L. AND BRINKMAN, P. T. 1987. Student price response in higher education: The student demand studies. The Journal of Higher Education 58, 2, 181–204.
MCKINNEY, W. 2010. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, S. van der Walt and J. Millman, Eds. Vol. 445. SciPy, Austin, TX, 51–56.
MCLENDON, M. K., HEARN, J. C., AND MOKHER, C. G. 2009. Partisans, professionals, and power: The role of political factors in state higher education funding. The Journal of Higher Education 80, 6, 686–713.
PEDREGOSA, F., VAROQUAUX, G., GRAMFORT, A., MICHEL, V., THIRION, B., GRISEL, O., BLONDEL, M., PRETTENHOFER, P., WEISS, R., DUBOURG, V., ..., DUCHESNAY, É. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12, Oct, 2825–2830.
PERNA, L. W., ASHA COOPER, M., AND LI, C. 2007. Improving educational opportunities for students who work. Readings on Equal Education 22, 109–160.
PERNA, L. W. AND LI, C. 2006. College affordability: Implications for college opportunity. NASFAA Journal of Student Financial Aid 36, 1, 7–24.
PHAN, V., WRIGHT, L., AND DECENT, B. 2022. Addressing competing objectives in allocating funds to scholarships and need-based financial aid. In Proceedings of the 15th International Conference on Educational Data Mining, A. Mitrovic and N. Bosch, Eds. International Educational Data Mining Society, Durham, United Kingdom, 110–121.
ROHMAN, A., ARIF, I., HARIANTI, I., SURHOMAN HIDAYAT, A., ANSELMUS TENIWUT, W., BAHARUN, H., DJA’WA, A., RAHMI, U., SARAGIH, J. R., BURHANUDIN, J., ... SURADI, A. 2019. Id3 algorithm approach for giving scholarships. Journal of Physics: Conference Series 1175, 1, 012116.
SARAFRAZ, Z., SARAFRAZ, H., SAYEH, M., AND NICKLOW, J. 2015. Student yield maximization using genetic algorithm on a predictive enrollment neural network model. Procedia Computer Science 61, 341–348.
SCHAPIRE, R. E. 2013. Explaining adaboost. In Empirical Inference, B. Sch¨olkopf, Z. Luo, and V. Vovk, Eds. Springer, New York City, NY, USA, 37–52.
SEXTON, R. S., DORSEY, R. E., AND JOHNSON, J. D. 1999. Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. European Journal of Operational Research 114, 3, 589–601.
SHACKLOCK, X. 2016. From bricks to clicks: The potential of data and analytics in higher education. Higher Education Commission, London, UK.
SIRAJ, F. AND ABDOULHA, M. A. 2009. Uncovering hidden information within university’s student enrollment data using data mining. In 2009 Third Asia International Conference on Modelling & Simulation, D. Al-Dabass, R. Triweko, S. Susanto, and A. Abraham, Eds. IEEE Computer Society, 1730 Massachusetts Ave., NW Washington, DC, 413–418.
SPAULDING, R. AND OLSWANG, S. 2005. Maximizing enrollment yield through financial aid packaging policies. Journal of Student Financial Aid 35, 1, 27–38.
SUGRUE, P. 2014. A model for predicting enrolment yields. International Journal of Operational Research 19, 1, 60–67.
SUGRUE, P. K. 2010. An optimization model for the allocation of university based merit aid. Journal of Student Financial Aid 40, 2, 21–30.
SUGRUE, P. K. 2019. A comprehensive merit aid allocation model. International Journal of Operational Research 36, 467–476.
SUGRUE, P. K., MEHROTRA, A., AND OREHOVEC, P. M. 2006. Financial aid management: an optimisation approach. International Journal of Operational Research 1, 3, 267–282.
TANDBERG, D. A. 2010. Politics, interest groups and state funding of public higher education. Research in Higher Education 51, 5, 416–450.
TRUSHEIM, D. AND RYLEE, C. 2011. Predictive modeling: Linking enrollment and budgeting. Planning for Higher Education 40, 1, 12–21.
WALCZAK, S. 1998. Neural network models for a resource allocation problem. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 28, 2, 276–284.
WALCZAK, S. AND SINCICH, T. 1999. A comparative analysis of regression and neural networks for university admissions. Information Sciences 119, 1-2, 1–20.
WHITLEY, D. 1994. A genetic algorithm tutorial. Statistics and Computing 4, 2, 65–85.
ZWICK, R. 2020. Using mathematical models to improve access to postsecondary education. In Higher Education Admission Practices: An International Perspective, M. E. Oliveri and C. Wendler, Eds. Cambridge University Press, New York, NY, Chapter 18, 333–346.
ZWICK, R., BLATTER, A., YE, L., AND ISHAM, S. 2021. Using an index of admission obstacles with constrained optimization to increase the diversity of college classes. Educational Assessment 26, 1, 20–34.
ZWICK, R., YE, L., AND ISHAM, S. 2019. Using constrained optimization to increase the representation of students from low-income neighborhoods. Applied Measurement in Education 32, 4, 281–297.
ANTONS, C. M. AND MALTZ, E. N. 2006. Expanding the role of institutional research at small private universities: A case study in enrollment management using data mining. New Directions for Institutional Research 2006, 131, 69–81.
ASSALONE, A., DESHAWN, P., AND MCELROY, B. 2018. Unexpected hurdles: Unpacking the price tag of college affordability. Policy brief, Southern Education Foundation, Atlanta, GA.
AULCK, L., NAMBI, D., AND WEST, J. 2020. Increasing enrollment by optimizing scholarship allocations using machine learning and genetic algorithms. In Proceedings of the 13th International Conference on Educational Data Mining, A. N. Rafferty, J. Whitehill, V. Cavalli-Sforza, and C. Romero, Eds. International Educational Data Mining Society, Durham, United Kingdom, 29–38.
BARROW, L. AND ROUSE, C. E. 2006. U.S. elementary and secondary schools: Equalizing opportunity or replicating the status quo? In The Future of Children, C. Paxson, E. Donahue, C. T. Orleans, and J. A. Grisso, Eds. Vol. 16. Princeton University Press, Princeton, NJ, USA, 99–123.
BASU, K., BASU, T., BUCKMIRE, R., AND LAL, N. 2019. Predictive models of student college commitment decisions using machine learning. Data 4, 2, 65.
CHANG, L. 2006. Applying data mining to predict college admissions yield: A case study. New Directions for Institutional Research 131, 53–68.
CHEN, T. AND GUESTRIN, C. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16.Association for Computing Machinery, New York, NY, USA, 785–794.
DAVIS, D. J., GREEN-DERRY, L. C., AND JONES, B. 2013. The impact of federal financial aid policy upon higher education access. Journal of Educational Administration and History 45, 1, 49–57.
DELANEY, J. A. 2014. The role of state policy in promoting college affordability. The Annals of the American Academy of Political and Social Science 655, 1, 56–78.
DESJARDINS, S. L. 2002. An analytic strategy to assist institutional recruitment and marketing efforts. Research in Higher education 43, 5, 531–553.
DEVASENA, C. L., SUMATHI, T., GOMATHI, V., AND HEMALATHA, M. 2011. Effectiveness evaluation of rule based classifiers for the classification of iris data set. Bonfring International Journal of Man Machine Interface 1, 05–09.
FRIEDMAN, J. H. 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics 29, 5, 1189–1232.
GIDLEY, J. M., HAMPSON, G. P., WHEELER, L., AND BEREDED-SAMUEL, E. 2010. From access to success: An integrated approach to quality higher education informed by social inclusion theory and practice. Higher Education Policy 23, 1, 123–147.
GOENNER, C. F. AND PAULS, K. 2006. A predictive model of inquiry to enrollment. Research in Higher Education 47, 8, 935–956.
HOSSLER, D. 2000. The role of financial aid in enrollment management. In New Directions for Student Services, M. Saddlemire, Ed. Vol. 2000. Wiley Online Library, Hoboken, NJ, USA, 77–90.
HOSSLER, D. 2009. Enrollment management & the enrollment industry. College and University 85, 2, 2–9.
HOVEY, H. A. 1999. State spending for higher education in the next decade: The battle to sustain current support. Technical report, National Center for Public Policy and Higher Education, San Jose, CA.
INGBER, L. 1993. Simulated annealing: Practice versus theory. Mathematical and Computer Modelling 18, 11, 29–57.
JOHNSON, A. W. AND JACOBSON, S. H. 2002. On the convergence of generalized hill climbing algorithms. Discrete Applied Mathematics 119, 1-2, 37–57.
JUELS, A. AND WATTENBERG, M. 1995. Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. Advances in Neural Information Processing Systems 8, 430–436.
KIRKPATRICK, S., GELATT JR, C. D., AND VECCHI, M. P. 1983. Optimization by simulated annealing. Science 220, 4598, 671–680.
LESLIE, L. L. AND BRINKMAN, P. T. 1987. Student price response in higher education: The student demand studies. The Journal of Higher Education 58, 2, 181–204.
MCKINNEY, W. 2010. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, S. van der Walt and J. Millman, Eds. Vol. 445. SciPy, Austin, TX, 51–56.
MCLENDON, M. K., HEARN, J. C., AND MOKHER, C. G. 2009. Partisans, professionals, and power: The role of political factors in state higher education funding. The Journal of Higher Education 80, 6, 686–713.
PEDREGOSA, F., VAROQUAUX, G., GRAMFORT, A., MICHEL, V., THIRION, B., GRISEL, O., BLONDEL, M., PRETTENHOFER, P., WEISS, R., DUBOURG, V., ..., DUCHESNAY, É. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12, Oct, 2825–2830.
PERNA, L. W., ASHA COOPER, M., AND LI, C. 2007. Improving educational opportunities for students who work. Readings on Equal Education 22, 109–160.
PERNA, L. W. AND LI, C. 2006. College affordability: Implications for college opportunity. NASFAA Journal of Student Financial Aid 36, 1, 7–24.
PHAN, V., WRIGHT, L., AND DECENT, B. 2022. Addressing competing objectives in allocating funds to scholarships and need-based financial aid. In Proceedings of the 15th International Conference on Educational Data Mining, A. Mitrovic and N. Bosch, Eds. International Educational Data Mining Society, Durham, United Kingdom, 110–121.
ROHMAN, A., ARIF, I., HARIANTI, I., SURHOMAN HIDAYAT, A., ANSELMUS TENIWUT, W., BAHARUN, H., DJA’WA, A., RAHMI, U., SARAGIH, J. R., BURHANUDIN, J., ... SURADI, A. 2019. Id3 algorithm approach for giving scholarships. Journal of Physics: Conference Series 1175, 1, 012116.
SARAFRAZ, Z., SARAFRAZ, H., SAYEH, M., AND NICKLOW, J. 2015. Student yield maximization using genetic algorithm on a predictive enrollment neural network model. Procedia Computer Science 61, 341–348.
SCHAPIRE, R. E. 2013. Explaining adaboost. In Empirical Inference, B. Sch¨olkopf, Z. Luo, and V. Vovk, Eds. Springer, New York City, NY, USA, 37–52.
SEXTON, R. S., DORSEY, R. E., AND JOHNSON, J. D. 1999. Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. European Journal of Operational Research 114, 3, 589–601.
SHACKLOCK, X. 2016. From bricks to clicks: The potential of data and analytics in higher education. Higher Education Commission, London, UK.
SIRAJ, F. AND ABDOULHA, M. A. 2009. Uncovering hidden information within university’s student enrollment data using data mining. In 2009 Third Asia International Conference on Modelling & Simulation, D. Al-Dabass, R. Triweko, S. Susanto, and A. Abraham, Eds. IEEE Computer Society, 1730 Massachusetts Ave., NW Washington, DC, 413–418.
SPAULDING, R. AND OLSWANG, S. 2005. Maximizing enrollment yield through financial aid packaging policies. Journal of Student Financial Aid 35, 1, 27–38.
SUGRUE, P. 2014. A model for predicting enrolment yields. International Journal of Operational Research 19, 1, 60–67.
SUGRUE, P. K. 2010. An optimization model for the allocation of university based merit aid. Journal of Student Financial Aid 40, 2, 21–30.
SUGRUE, P. K. 2019. A comprehensive merit aid allocation model. International Journal of Operational Research 36, 467–476.
SUGRUE, P. K., MEHROTRA, A., AND OREHOVEC, P. M. 2006. Financial aid management: an optimisation approach. International Journal of Operational Research 1, 3, 267–282.
TANDBERG, D. A. 2010. Politics, interest groups and state funding of public higher education. Research in Higher Education 51, 5, 416–450.
TRUSHEIM, D. AND RYLEE, C. 2011. Predictive modeling: Linking enrollment and budgeting. Planning for Higher Education 40, 1, 12–21.
WALCZAK, S. 1998. Neural network models for a resource allocation problem. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 28, 2, 276–284.
WALCZAK, S. AND SINCICH, T. 1999. A comparative analysis of regression and neural networks for university admissions. Information Sciences 119, 1-2, 1–20.
WHITLEY, D. 1994. A genetic algorithm tutorial. Statistics and Computing 4, 2, 65–85.
ZWICK, R. 2020. Using mathematical models to improve access to postsecondary education. In Higher Education Admission Practices: An International Perspective, M. E. Oliveri and C. Wendler, Eds. Cambridge University Press, New York, NY, Chapter 18, 333–346.
ZWICK, R., BLATTER, A., YE, L., AND ISHAM, S. 2021. Using an index of admission obstacles with constrained optimization to increase the diversity of college classes. Educational Assessment 26, 1, 20–34.
ZWICK, R., YE, L., AND ISHAM, S. 2019. Using constrained optimization to increase the representation of students from low-income neighborhoods. Applied Measurement in Education 32, 4, 281–297.
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