home - login - register

Conference Proceedings Detail Page

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
Pages 669-674
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
- Researchers
- Administrators
  • Currently 0.0/5

Want to rate this material?
Login here!


Format:
application/pdf
Mirror:
https://doi.org/10.1119/perc.2019…
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

2019 PERC Notable Paper

Author: Lyle
Posted: May 4, 2021 at 4:42PM

This paper was one of four 2019 PERC Proceedings papers selected as notable by PERLOC and the Notable Papers subcommittee.

» reply

Post a new comment on this item
ComPADRE is beta testing Citation Styles!

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 November 10, 2024, from https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141
Chicago Format
Young, Nicholas "Nick", and Marcos D. 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 10 November 2024).
MLA Format
Young, Nicholas "Nick", and Marcos D. Caballero. "Using machine learning to understand physics graduate school admissions." Physics Education Research Conference 2019. Provo, UT: 2019. 669-674 of PER Conference. 10 Nov. 2024 <https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141>.
BibTeX Export Format
@inproceedings{ Author = "Nicholas "Nick" Young and Marcos D. Caballero", Title = {Using machine learning to understand physics graduate school admissions}, BookTitle = {Physics Education Research Conference 2019}, Pages = {669-674}, Address = {Provo, UT}, Series = {PER Conference}, Month = {July 24-25}, Year = {2019} }
Refer Export Format

%A Nicholas "Nick" Young %A Marcos D. Caballero %T Using machine learning to understand physics graduate school admissions %S PER Conference %D July 24-25 2019 %P 669-674 %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. %D July 24-25 2019 %T Using machine learning to understand physics graduate school admissions %B Physics Education Research Conference 2019 %C Provo, UT %P 669-674 %S PER Conference %8 July 24-25 %U https://www.compadre.org/Repository/document/ServeFile.cfm?ID=15226&DocID=5141


Disclaimer: ComPADRE offers citation styles as a guide only. We cannot offer interpretations about citations as this is an automated procedure. Please refer to the style manuals in the Citation Source Information area for clarifications.

Citation Source Information

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

The AJP/PRST-PER presented is based on the AIP Style with the addition of journal article titles and conference proceeding article titles.

The APA Style presented is based on information from APA Style.org: Electronic References.

The Chicago Style presented is based on information from Examples of Chicago-Style Documentation.

The MLA Style presented is based on information from the MLA FAQ.

Using machine learning to understand physics graduate school admissions:


Know of another related resource? Login to relate this resource to it.
Save to my folders

Supplements

Contribute

Related Materials

Similar Materials