The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Relation between students’ problem-solving performance and representational format
Abstract
An analysis is presented of data on students’ problem-solving performance on similar problems posed in diverse representations. Five years of classroom data on 400 students collected in a second-semester algebra-based general physics course are presented. Two very similar Newton’s third-law questions, one posed in a verbal representation and one in a diagrammatic representation using vector diagrams, were given to students at the beginning of the course. The proportion of correct responses on the verbal question was consistently higher than on the diagrammatic question, and the pattern of incorrect responses on the two questions also differed consistently. Two additional four-question quizzes were given to students during the semester; each quiz had four very similar questions posed in the four representations: verbal, diagrammatic, mathematical/symbolic, and graphical. In general, the error rates for the four representations were very similar, but there was substantial evidence that females had a slightly higher error rate on the graphical questions relative to the other representations, whereas the evidence for male students was more ambiguous. There also was evidence that females had higher error rates on circuit-diagram problems in comparison with males, although both males and females had received identical instruction .
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Cites
Hake, R. R. (1998) · American Journal of Physics · doi:10.1119/1.18809 · Cited by 6,072
Heller, P., & Hollabaugh, M. (1992) · American Journal of Physics · doi:10.1119/1.17118 · Cited by 726
Bars and lines: A study of graphic communication
Zacks, J., & Tversky, B. (1999) · Memory & Cognition · doi:10.3758/bf03201236 · Cited by 300
Students' difficulties in reading images. Comparing results from four national research groups
Pintó, R., & Ametller, J. (2002) · International Journal of Science Education · doi:10.1080/09500690110078932 · Cited by 78
Cited by
Singer, S., & Smith, K. A. (2013) · Journal of Engineering Education · doi:10.1002/jee.20030 · Cited by 835
Synthesis of discipline-based education research in physics
Docktor, J. L., & Mestre, J. P. (2014) · Physical Review Special Topics - Physics Education Research · doi:10.1103/physrevstper.10.020119 · Cited by 549
Toward AI grading of student problem solutions in introductory physics: A feasibility study
Kortemeyer, G. (2023) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.19.020163 · Cited by 59
Improving performance in quantum mechanics with explicit incentives to correct mistakes
Brown, B. R., Mason, A., & Singh, C. (2016) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.12.010121 · Cited by 59
Howard, C., Jordan, P., Di Eugenio, B., & Katz, S. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0071-y · Cited by 51
Metadata
- Authors
- Meltzer, D. E.
- Venue
- American Journal of Physics
- Published
- 2005-04-13
- Volume
- 73
- Issue
- 5
- Pages
- 463-478
- ISSN
- 0002-9505 (ISSN-L)
- 1943-2909
- OpenAlex
- W2019742881
- Cited by
- 231
- References
- 4
- FWCI
- 31.80
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.