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Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics
written by Jon M. Geiger, Lisa M. Goodhew, and Tor Ole B. Odden
Research characterizing common student ideas about particular physics topics has significantly impacted university-level physics teaching by providing knowledge that supports instructors to target their instruction and by informing curriculum development. In this work, we utilize a Natural Language Processing algorithm (Latent Dirichlet Allocation, or LDA) to identify distinct student ideas in a set of written responses to a conceptual physics question, with the goal of significantly expediting the process of characterizing student ideas. We preliminarily test the LDA approach by applying the algorithm to a collection of introductory physics student responses to a conceptual question about circuits, specifically attending to whether it is useful for characterizing instructionally-relevant student ideas. We find that for a large enough collection of student responses (N ? 500), LDA can be useful for characterizing the ideas students used to answer conceptual physics questions. We discuss some considerations that researchers may take into account as they interpret the results of the LDA algorithm for characterizing student's physics ideas.
Physics Education Research Conference 2022
Part of the PER Conference series
Grand Rapids, MI: July 13-14, 2022
Pages 206-211
Subjects Levels Resource Types
Education - Applied Research
- Technology
Education - Basic Research
- Assessment
= Methods
- Research Design & Methodology
= Data
= Evaluation
- Lower Undergraduate
- Reference Material
= Research study
PER-Central Type Intended Users Ratings
- PER Literature
- Researchers
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Format:
application/pdf
Mirror:
https://doi.org/10.1119/perc.2022…
Access Rights:
Free access
License:
This material is released under a Creative Commons Attribution 4.0 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.2022.pr.Geiger
NSF Numbers:
1914603
1914572
Keyword:
PERC 2022
Record Creator:
Metadata instance created September 7, 2022 by Lyle Barbato
Record Updated:
September 14, 2022 by Lyle Barbato
Last Update
when Cataloged:
September 15, 2022
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Record Link
AIP Format
J. Geiger, L. Goodhew, and T. Odden, , presented at the Physics Education Research Conference 2022, Grand Rapids, MI, 2022, WWW Document, (https://www.compadre.org/Repository/document/ServeFile.cfm?ID=16233&DocID=5602).
AJP/PRST-PER
J. Geiger, L. Goodhew, and T. Odden, Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics, presented at the Physics Education Research Conference 2022, Grand Rapids, MI, 2022, <https://www.compadre.org/Repository/document/ServeFile.cfm?ID=16233&DocID=5602>.
APA Format
Geiger, J., Goodhew, L., & Odden, T. (2022, July 13-14). Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics. Paper presented at Physics Education Research Conference 2022, Grand Rapids, MI. Retrieved June 16, 2024, from https://www.compadre.org/Repository/document/ServeFile.cfm?ID=16233&DocID=5602
Chicago Format
Geiger, J, L. Goodhew, and T. Odden. "Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics." Paper presented at the Physics Education Research Conference 2022, Grand Rapids, MI, July 13-14, 2022. https://www.compadre.org/Repository/document/ServeFile.cfm?ID=16233&DocID=5602 (accessed 16 June 2024).
MLA Format
Geiger, Jon M., Lisa M. Goodhew, and Tor Ole B. Odden. "Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics." Physics Education Research Conference 2022. Grand Rapids, MI: 2022. 206-211 of PER Conference. 16 June 2024 <https://www.compadre.org/Repository/document/ServeFile.cfm?ID=16233&DocID=5602>.
BibTeX Export Format
@inproceedings{ Author = "Jon M. Geiger and Lisa M. Goodhew and Tor Ole B. Odden", Title = {Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics}, BookTitle = {Physics Education Research Conference 2022}, Pages = {206-211}, Address = {Grand Rapids, MI}, Series = {PER Conference}, Month = {July 13-14}, Year = {2022} }
Refer Export Format

%A Jon M. Geiger %A Lisa M. Goodhew %A Tor Ole B. Odden %T Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics %S PER Conference %D July 13-14 2022 %P 206-211 %C Grand Rapids, MI %U https://www.compadre.org/Repository/document/ServeFile.cfm?ID=16233&DocID=5602 %O Physics Education Research Conference 2022 %O July 13-14 %O application/pdf

EndNote Export Format

%0 Conference Proceedings %A Geiger, Jon M. %A Goodhew, Lisa M. %A Odden, Tor Ole B. %D July 13-14 2022 %T Developing a natural language processing approach for analyzing student ideas in calculus-based introductory physics %B Physics Education Research Conference 2022 %C Grand Rapids, MI %P 206-211 %S PER Conference %8 July 13-14 %U https://www.compadre.org/Repository/document/ServeFile.cfm?ID=16233&DocID=5602


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