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Physical Review Physics Education Research
written by John M. Aiken, Riccardo De Bin, Heather J. Lewandowski, and Marcos D. Caballero
Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large datasets and machine learning techniques. In physics education research (PER), this increased focus has recently been shown through the 2019 Physical Review PER Focused Collection examining quantitative methods in PER. Quantitative PER has provided strong arguments for reforming courses by including interactive engagement, demonstrated that students often move away from scientistlike views due to science education, and has injected robust assessment into the physics classroom via concept inventories. The work presented here examines the impact that machine learning may have on physics education research, presents a framework for the entire process including data management, model evaluation, and results communication, and demonstrates the utility of this framework through the analysis of two types of survey data.
Physical Review Physics Education Research: Volume 17, Issue 2, Pages 020104
Subjects ADS Supplements Resource Types
Education Foundations
- Research Design & Methodology
= Data
= Statistics
= Validity
Education Practices
- Technology
General Physics
- Physics Education Research
- Reference Material
= Research study
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Access Rights:
Free access
License:
This material is released under a Creative Commons Attribution 4.0 license.
Rights Holder:
American Physical Society
DOI:
10.1103/PhysRevPhysEducRes.17.020104
NSF Number:
PHY-1734006
Keywords:
AI in PER, educational data mining, machine learning, quantitative methods
Record Creator:
Metadata instance created November 10, 2021 by Lyle Barbato
Record Updated:
January 25, 2022 by Lyle Barbato
Last Update
when Cataloged:
July 28, 2021
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AIP Format
J. Aiken, R. De Bin, H. Lewandowski, and M. Caballero, , Phys. Rev. Phys. Educ. Res. 17 (2), 020104 (2021), WWW Document, (https://doi.org/10.1103/PhysRevPhysEducRes.17.020104).
AJP/PRST-PER
J. Aiken, R. De Bin, H. Lewandowski, and M. Caballero, Framework for evaluating statistical models in physics education research, Phys. Rev. Phys. Educ. Res. 17 (2), 020104 (2021), <https://doi.org/10.1103/PhysRevPhysEducRes.17.020104>.
APA Format
Aiken, J., De Bin, R., Lewandowski, H., & Caballero, M. (2021, July 28). Framework for evaluating statistical models in physics education research. Phys. Rev. Phys. Educ. Res., 17(2), 020104. Retrieved February 3, 2023, from https://doi.org/10.1103/PhysRevPhysEducRes.17.020104
Chicago Format
Aiken, J, R. De Bin, H. Lewandowski, and M. Caballero. "Framework for evaluating statistical models in physics education research." Phys. Rev. Phys. Educ. Res. 17, no. 2, (July 28, 2021): 020104, https://doi.org/10.1103/PhysRevPhysEducRes.17.020104 (accessed 3 February 2023).
MLA Format
Aiken, John, Riccardo De Bin, Heather J. Lewandowski, and Marcos D. Caballero. "Framework for evaluating statistical models in physics education research." Phys. Rev. Phys. Educ. Res. 17.2 (2021): 020104. 3 Feb. 2023 <https://doi.org/10.1103/PhysRevPhysEducRes.17.020104>.
BibTeX Export Format
@article{ Author = "John Aiken and Riccardo De Bin and Heather J. Lewandowski and Marcos D. Caballero", Title = {Framework for evaluating statistical models in physics education research}, Journal = {Phys. Rev. Phys. Educ. Res.}, Volume = {17}, Number = {2}, Pages = {020104}, Month = {July}, Year = {2021} }
Refer Export Format

%A John Aiken %A Riccardo De Bin %A Heather J. Lewandowski %A Marcos D. Caballero %T Framework for evaluating statistical models in physics education research %J Phys. Rev. Phys. Educ. Res. %V 17 %N 2 %D July 28, 2021 %P 020104 %U https://doi.org/10.1103/PhysRevPhysEducRes.17.020104 %O application/pdf

EndNote Export Format

%0 Journal Article %A Aiken, John %A De Bin, Riccardo %A Lewandowski, Heather J. %A Caballero, Marcos D. %D July 28, 2021 %T Framework for evaluating statistical models in physics education research %J Phys. Rev. Phys. Educ. Res. %V 17 %N 2 %P 020104 %8 July 28, 2021 %U https://doi.org/10.1103/PhysRevPhysEducRes.17.020104


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