Journal Article Detail Page

written by Antje Kohnle and Gina Passante
Analyzing, constructing, and translating between graphical, pictorial, and mathematical representations of physics ideas and reasoning flexibly through them ("representational competence") is a key characteristic of expertise in physics but is a challenge for learners to develop. Interactive computer simulations and University of Washington style tutorials both have affordances to support representational learning. This article describes work to characterize students' spontaneous use of representations before and after working with a combined simulation and tutorial on first-order energy corrections in the context of quantum-mechanical time-independent perturbation theory. Data were collected from two institutions using pre-, mid-, and post-tests to assess short- and long-term gains. A representational competence level framework was adapted to devise level descriptors for the assessment items. The results indicate an increase in the number of representations used by students and the consistency between them following the combined simulation tutorial. The distributions of representational competence levels suggest a shift from perceptual to semantic use of representations based on their underlying meaning. In terms of activity design, this study illustrates the need to support students in making sense of the representations shown in a simulation and in learning to choose the most appropriate representation for a given task. In terms of characterizing representational abilities, this study illustrates the usefulness of a framework focusing on perceptual, syntactic, and semantic use of representations.
Physical Review Physics Education Research: Volume 13, Issue 2, Pages 13
Subjects Levels Resource Types
Education Foundations
- Problem Solving
= Representational Use
- Student Characteristics
= Ability
Education Practices
- Instructional Material Design
= Simulation
= Tutorial
Quantum Physics
- Approximation Techniques
= Rayleigh-Schrodinger Perturbation Theory
- Bound State Systems
- Upper Undergraduate
- Lower Undergraduate
- Reference Material
= Article
= Research study
Categories Intended Users Ratings
- Pedagogy
- Activity
- Researchers
- Educators
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This material is released under a Creative Commons Attribution 4.0 license.
Rights Holder:
Antje Kohnle and Gina Passante
DOI:
10.1103/PhysRevPhysEducRes.13.020131
Record Creator:
Metadata instance created May 12, 2021 by Bruce Mason
Record Updated:
May 12, 2021 by Bruce Mason
Last Update
when Cataloged:
November 28, 2017
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AIP Format
A. Kohnle and G. Passante, Phys. Rev. Phys. Educ. Res. 13 (2), 13 (2017), WWW Document, (https://doi.org/10.1103/PhysRevPhysEducRes.13.020131).
AJP/PRST-PER
A. Kohnle and G. Passante, Characterizing representational learning: A combined simulation and tutorial on perturbation theory, Phys. Rev. Phys. Educ. Res. 13 (2), 13 (2017), <https://doi.org/10.1103/PhysRevPhysEducRes.13.020131>.
APA Format
Kohnle, A., & Passante, G. (2017, November 28). Characterizing representational learning: A combined simulation and tutorial on perturbation theory. Phys. Rev. Phys. Educ. Res., 13(2), 13. Retrieved June 17, 2021, from https://doi.org/10.1103/PhysRevPhysEducRes.13.020131
Chicago Format
Kohnle, Antje, and Gina Passante. "Characterizing representational learning: A combined simulation and tutorial on perturbation theory." Phys. Rev. Phys. Educ. Res. 13, no. 2, (November 28, 2017): 13, https://doi.org/10.1103/PhysRevPhysEducRes.13.020131 (accessed 17 June 2021).
MLA Format
Kohnle, Antje, and Gina Passante. "Characterizing representational learning: A combined simulation and tutorial on perturbation theory." Phys. Rev. Phys. Educ. Res. 13.2 (2017): 13. 17 June 2021 <https://doi.org/10.1103/PhysRevPhysEducRes.13.020131>.
BibTeX Export Format
@article{ Author = "Antje Kohnle and Gina Passante", Title = {Characterizing representational learning: A combined simulation and tutorial on perturbation theory}, Journal = {Phys. Rev. Phys. Educ. Res.}, Volume = {13}, Number = {2}, Pages = {13}, Month = {November}, Year = {2017} }
Refer Export Format

%A Antje Kohnle
%A Gina Passante
%T Characterizing representational learning: A combined simulation and tutorial on perturbation theory
%J Phys. Rev. Phys. Educ. Res.
%V 13
%N 2
%D November 28, 2017
%P 13
%U https://doi.org/10.1103/PhysRevPhysEducRes.13.020131
%O application/pdf

EndNote Export Format

%0 Journal Article
%A Kohnle, Antje
%A Passante, Gina
%D November 28, 2017
%T Characterizing representational learning: A combined simulation and tutorial on perturbation theory
%J Phys. Rev. Phys. Educ. Res.
%V 13
%N 2
%P 13
%8 November 28, 2017
%U https://doi.org/10.1103/PhysRevPhysEducRes.13.020131


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