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Cited by 677

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Surveying students’ conceptual knowledge of electricity and magnetism

Maloney, David P., O’Kuma, Thomas L., Hieggelke, Curtis J., Van Heuvelen, Alan·2001·American Journal of Physics

Abstract

The Conceptual Survey of Electricity and Magnetism (CSEM) was developed to assess students’ knowledge about topics in electricity and magnetism. The survey is a 32-question, multiple-choice test that can be used as both a pretest and posttest. During four years of testing and refinement, the survey has been given in one form or another to more than 5000 introductory physics students at 30 different institutions. Typical pretest results are that students in calculus-based courses get 31% of the questions correct and student’s in algebra/trigonometry-based courses average 25% correct. Posttest correct results only rise to 47% and 44%, respectively. From analysis of student responses, a number of student difficulties in electricity and magnetism are indicated.

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Metadata

Authors
Maloney, D. P., O’Kuma, T. L., Hieggelke, C. J., & Van Heuvelen, A.
Venue
American Journal of Physics
Published
2001-07-01
Volume
69
Issue
S1
Pages
S12-S23
ISSN
  • 0002-9505 (ISSN-L)
  • 1943-2909
OpenAlex
W1987750830
Cited by
677
References
2
FWCI
47.75
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Surveying students’ conceptual knowledge of electricity and magnetism. https://app.cochat.ai/papers/10.1119/1.1371296
The paper
Maloney, D. P., O’Kuma, T. L., Hieggelke, C. J., & Van Heuvelen, A. (2001). Surveying students’ conceptual knowledge of electricity and magnetism. American Journal of Physics. https://doi.org/10.1119/1.1371296

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.