Title
True-Ed Select Enters Social Computing: A Machine Learning Based University Selection Framework
Document Type
Conference Presentation
Department
Electrical and Computer Engineering
Conference Title
Lecture Notes in Networks and Systems
Date of Presentation
1-1-2023
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 comprising 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.
ISSN
23673370
Volume
561 LNNS
First Page
103
Last Page
120
DOI
10.1007/978-3-031-18344-7_7
Recommended Citation
Cearley, J.,
&
Pallipuram, V. K.
(2023).
True-Ed Select Enters Social Computing: A Machine Learning Based University Selection Framework.
Paper presented at Lecture Notes in Networks and Systems.
https://scholarlycommons.pacific.edu/soecs-facpres/465
