Date of Graduation
12-2017
Document Type
Thesis
Degree Name
Master of Science in Operations Management (MSOM)
Degree Level
Graduate
Department
Industrial Engineering
Advisor/Mentor
Caroline Beam
Committee Member
Gregory Parnell
Second Committee Member
Richard Ham
Third Committee Member
Ed Pohl
Keywords
Classification Tree, Delphi Analysis, Enrollment, Forecasting, Predictive Analysis, Regression Model
Abstract
A forecasting model, associated with predictive analysis, is an elementary requirement for academic leaders to plan course requirements. The M.S. in Operations Management (MSOM) program at the University of Arkansas desires to understand future student enrollment more accurately. The available literature shows that there is an absence of forecasting models based on quantitative, qualitative and predictive analysis. This study develops a combined forecasting model focusing on three admission stages. The research uses simple regression, Delphi analysis, Analysis of Variance (ANOVA), and classification tree system to develop the models. It predicts that 272, 173, and 136 new students will apply, matriculate and enroll in the MSOM program during Fall 2017, respectively. In addition, the predictive analysis reveals that 45% of applicants do not enroll in the program. The tuition fee of the program is negatively associated with the student enrollment and significantly influences individuals’ decision. Moreover, the students’ enrollment in the program is distributed over 6 semesters after matriculation. The classification tree classifies that 61% of applicants with non-military status will join the program. Based on the outcomes, this study proposes a set of recommendations to improve the admission process.
Citation
Hasnat, S. N. (2017). Analyzing the Fundamental Aspects and Developing a Forecasting Model to Enhance the Student Admission and Enrollment System of MSOM Program. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/2556