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Abstract Title: Using Machine Learning to Understand Physics Graduate School Admissions
Abstract: Among all of the first-year graduate students enrolled in doctoral-granting physics departments, the percentage of women and underrepresented minorities has remained unchanged for the past 20 years. The current graduate program admissions process can create challenges for achieving diversity goals in physics. In this presentation, we will investigate how the various aspects of a prospective student's application to a physics doctoral program affect the likelihood the applicant will be admitted. Admissions data was collected from a large, Midwestern public research university that has a decentralized admissions process and included applicants' undergraduate GPAs and institutions and GRE and physics GRE scores. Supervised machine learning algorithms were used to create models that predict who was admitted into the PhD program. Here, we will present the results of this analysis as well as compare models between the various subdisciplines of physics represented in this department.
Abstract Type: Contributed Poster Presentation
Session Time: Poster Session II
Poster Number: B18
Contributed Paper Record: Contributed Paper Information
Contributed Paper Download: Download Contributed Paper

Author/Organizer Information

Primary Contact: Nicholas T Young
Department of Physics and Astronomy, Michigan State University
and Co-Presenter(s)
Marcos D. Caballero Center for Computing in Science Education & Department of Physics, University of Oslo, Department of Physics and Astronomy, Michigan State University, CREATE for STEM Institute, Michigan State University

Contributed Poster

Contributed Poster: Download the Contributed Poster