Data Mining and Mathematical Models for Optimal Scholarship Allocation for a State University¶
Abstract¶
Financial aid is funding available to students attending an educational institution to cover various costs such as tuition, fees, room and board, and books. Financial aid is available from federal, state, institutions, and private agencies, and can be awarded in the forms of grants, education loans, and scholarships. This thesis focuses on the optimal allocation of scholarship for a university with the objective to increase its revenue. For a higher institution, the composition, allocation, and optimization of scholarships is an important part of its enrollment management and has a profound impact on its financial health. On the one hand, tuition is a significant portion of a university’s revenue and excess use of scholarships or financial aid could reduce the revenue; on the other hand, insufficient use of scholarship or financial aid would reduce student enrollment and quality, and thus undermine the revenue. The optimal allocation of scholarship to solve the dilemma is nontrivial and has been a challenge for the enrollment office at many universities. This research proposes a series of models and methodologies to the solution of the optimal allocation of scholarship and the methodology can be composed of two parts. The first part is to predict the response to scholarships from applicants with various socioeconomic backgrounds and the second is the allocation of scholarships among these applicants to maximize revenue.
Bio
Shuai Wang is a PhD candidate from Biomedical ,Industrial and Human Factor deparment at Wright State University. He earned his bachelor of management degree from Dalian Jiaotong University, China. His research interests are in data mining, operations research, management science. He is a consultant at a large grocery chain.