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Using machine learning to understand physics graduate school admissions
written by Nicholas T. Young and Marcos D. Caballero
Among all of the first-year graduate students enrolled in doctoral-granting physics departments, the percentage of female and racial minority students 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 paper, 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, research interests, and GRE scores. Because the collected data varied in scale, we used supervised machine learning algorithms to create models that predict who was admitted into the PhD program. We find that using only the applicant's undergraduate GPA and physics GRE score, we are able to predict with 75% accuracy who will be admitted to the program.
Physics Education Research Conference 2019
Part of the PER Conference series
Provo, UT: July 24-25, 2019
Subjects Levels Resource Types
Education - Applied Research
- Careers
- Recruitment
= Diversity
Education - Basic Research
- Achievement
- Assessment
- Student Characteristics
= Ability
- Graduate/Professional
- Reference Material
= Research study
PER-Central Type Intended Users Ratings
- PER Literature
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Format:
application/pdf
Mirror:
http://dx.doi.org/10.1119/perc.20…
Access Rights:
Free access
License:
This material is released under a Creative Commons Attribution 3.0 license. Further distribution of this work must maintain attribution to the published article's author(s), title, proceedings citation, and DOI.
Rights Holder:
American Association of Physics Teachers
DOI:
10.1119/perc.2019.pr.Young
Keyword:
PERC 2019
Record Creator:
Metadata instance created December 31, 2019 by Lyle Barbato
Record Updated:
January 2, 2020 by Lyle Barbato
Last Update
when Cataloged:
December 31, 2019
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Record Link
AIP Format
N. Young and M. Caballero, presented at the Physics Education Research Conference 2019, Provo, UT, 2019, WWW Document, (https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141).
AJP/PRST-PER
N. Young and M. Caballero, Using machine learning to understand physics graduate school admissions, presented at the Physics Education Research Conference 2019, Provo, UT, 2019, <https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141>.
APA Format
Young, N., & Caballero, M. (2019, July 24-25). Using machine learning to understand physics graduate school admissions. Paper presented at Physics Education Research Conference 2019, Provo, UT. Retrieved February 25, 2020, from https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141
Chicago Format
Young, Nicholas "Nick", and Marcos Caballero. "Using machine learning to understand physics graduate school admissions." Paper presented at the Physics Education Research Conference 2019, Provo, UT, July 24-25, 2019. https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141 (accessed 25 February 2020).
MLA Format
Young, Nicholas "Nick", and Marcos Caballero. "Using machine learning to understand physics graduate school admissions." Physics Education Research Conference 2019. Provo, UT: 2019. of PER Conference. 25 Feb. 2020 <https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141>.
BibTeX Export Format
@inproceedings{ Author = "Nicholas "Nick" Young and Marcos Caballero", Title = {Using machine learning to understand physics graduate school admissions}, BookTitle = {Physics Education Research Conference 2019}, Address = {Provo, UT}, Series = {PER Conference}, Month = {July 24-25}, Year = {2019} }
Refer Export Format

%A Nicholas "Nick" Young
%A Marcos Caballero
%T Using machine learning to understand physics graduate school admissions
%S PER Conference
%D July 24-25 2019
%C Provo, UT
%U https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141
%O Physics Education Research Conference 2019
%O July 24-25
%O application/pdf

EndNote Export Format

%0 Conference Proceedings
%A Young, Nicholas "Nick"
%A Caballero, Marcos
%D July 24-25 2019
%T Using machine learning to understand physics graduate school admissions
%B Physics Education Research Conference 2019
%C Provo, UT
%S PER Conference
%8 July 24-25
%U https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141


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The AIP Style presented is based on information from the AIP Style Manual.

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