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Physical Review Physics Education Research
written by Jie Yang, Seth DeVore, Dona Sachini Hewagallage, Paul M. Miller, Qing X. Ryan, and John Stewart
Machine learning algorithms have recently been used to predict students' performance in an introductory physics class. The prediction model classified students as those likely to receive an A or B or students likely to receive a grade of C, D, F or withdraw from the class. Early prediction could better allow the direction of educational interventions and the allocation of educational resources. However, the performance metrics used in that study become unreliable when used to classify whether a student would receive an A, B, or C (the ABC outcome) or if they would receive a D, F or withdraw (W) from the class (the DFW outcome) because the outcome is substantially unbalanced with between 10% to 20% of the students receiving a D, F, or W. This work presents techniques to adjust the prediction models and alternate model performance metrics more appropriate for unbalanced outcome variables. These techniques were applied to three samples drawn from introductory mechanics classes at two institutions (N = 7184, 1683, and 926). Applying the same methods as the earlier study produced a classifier that was very inaccurate, classifying only 16% of the DFW cases correctly; tuning the model increased the DFW classification accuracy to 43%. Using a combination of institutional and in-class data improved DFW accuracy to 53% by the second week of class. As in the prior study, demographic variables such as gender, underrepresented minority status, first-generation college student status, and low socioeconomic status were not important variables in the final prediction models.
Physical Review Physics Education Research: Volume 16, Issue 2, Pages 020130
Subjects Levels Resource Types
Education - Basic Research
- Achievement
- Assessment
= Formative Assessment
- Research Design & Methodology
= Data
= Validity
- Societal Issues
- Student Characteristics
= Ability
= Skills
General Physics
- Physics Education Research
- Lower Undergraduate
- Graduate/Professional
- Reference Material
= Research study
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Free access
License:
This material is released under a Creative Commons Attribution 4.0 license.
Rights Holder:
American Physical Society
DOI:
10.1103/PhysRevPhysEducRes.16.020130
NSF Numbers:
ECR-1561517
HRD-1834569
Keywords:
performance metrics, student preparation
Record Creator:
Metadata instance created May 12, 2021 by Bruce Mason
Record Updated:
July 2, 2022 by Caroline Hall
Last Update
when Cataloged:
October 28, 2020
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AIP Format
J. Yang, S. DeVore, D. Hewagallage, P. Miller, Q. Ryan, and J. Stewart, , Phys. Rev. Phys. Educ. Res. 16 (2), 020130 (2020), WWW Document, (https://doi.org/10.1103/PhysRevPhysEducRes.16.020130).
AJP/PRST-PER
J. Yang, S. DeVore, D. Hewagallage, P. Miller, Q. Ryan, and J. Stewart, Using machine learning to identify the most at-risk students in physics classes, Phys. Rev. Phys. Educ. Res. 16 (2), 020130 (2020), <https://doi.org/10.1103/PhysRevPhysEducRes.16.020130>.
APA Format
Yang, J., DeVore, S., Hewagallage, D., Miller, P., Ryan, Q., & Stewart, J. (2020, October 28). Using machine learning to identify the most at-risk students in physics classes. Phys. Rev. Phys. Educ. Res., 16(2), 020130. Retrieved April 18, 2024, from https://doi.org/10.1103/PhysRevPhysEducRes.16.020130
Chicago Format
Yang, J, S. DeVore, D. Hewagallage, P. Miller, Q. Ryan, and J. Stewart. "Using machine learning to identify the most at-risk students in physics classes." Phys. Rev. Phys. Educ. Res. 16, no. 2, (October 28, 2020): 020130, https://doi.org/10.1103/PhysRevPhysEducRes.16.020130 (accessed 18 April 2024).
MLA Format
Yang, Jie, Seth DeVore, Dona Sachini Hewagallage, Paul Miller, Qing Ryan, and John Stewart. "Using machine learning to identify the most at-risk students in physics classes." Phys. Rev. Phys. Educ. Res. 16.2 (2020): 020130. 18 Apr. 2024 <https://doi.org/10.1103/PhysRevPhysEducRes.16.020130>.
BibTeX Export Format
@article{ Author = "Jie Yang and Seth DeVore and Dona Sachini Hewagallage and Paul Miller and Qing Ryan and John Stewart", Title = {Using machine learning to identify the most at-risk students in physics classes}, Journal = {Phys. Rev. Phys. Educ. Res.}, Volume = {16}, Number = {2}, Pages = {020130}, Month = {October}, Year = {2020} }
Refer Export Format

%A Jie Yang %A Seth DeVore %A Dona Sachini Hewagallage %A Paul Miller %A Qing Ryan %A John Stewart %T Using machine learning to identify the most at-risk students in physics classes %J Phys. Rev. Phys. Educ. Res. %V 16 %N 2 %D October 28, 2020 %P 020130 %U https://doi.org/10.1103/PhysRevPhysEducRes.16.020130 %O text/html

EndNote Export Format

%0 Journal Article %A Yang, Jie %A DeVore, Seth %A Hewagallage, Dona Sachini %A Miller, Paul %A Ryan, Qing %A Stewart, John %D October 28, 2020 %T Using machine learning to identify the most at-risk students in physics classes %J Phys. Rev. Phys. Educ. Res. %V 16 %N 2 %P 020130 %8 October 28, 2020 %U https://doi.org/10.1103/PhysRevPhysEducRes.16.020130


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