True-Ed Select: A Machine Learning Based University Selection Framework

Author

Jerry C. Cearley, University of the PacificFollow

Date of Award

2022

Document Type

Thesis

Degree Name

Master of Science (M.S.)

Department

Engineering

First Advisor

Venkittaraman K. Pallipuram

First Committee Member

David Mueller

Second Committee Member

Mary Kay Camarillo

Abstract

University/College selection is a daunting task for young adults and their parents alike. This research presents True-Ed Select, a machine learning framework that simplifies the college selection process. The framework uses a four-layered approach including the user survey, machine learning, consolidation, and recommendation. The first layer collects both the objective and subjective attributes from users that best characterize their ideal college experience. The second layer employs machine learning techniques to analyze the objective and subjective attributes. The third layer combines the results from the machine learning techniques. The fourth layer inputs the consolidated result and presents a user-friendly list of top educational institutions that best match the user’s interests. We use our framework to analyze over 3500 United States post-secondary institutions and show search space reduction to top 20 institutions. This drastically reduced search space facilitates effective and assured college selection for end users. Our survey results with 10 participants highlight an average satisfaction rating of 4.11, showing the efficacy of the framework.

Pages

80

Recommended Citation

Cearley, Jerry C.. (2022). True-Ed Select: A Machine Learning Based University Selection Framework. University of the Pacific, Thesis. https://scholarlycommons.pacific.edu/uop_etds/4250