Review of Graph Comprehension Research: Implications for Instruction
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Characteristics such as education, graph reading ability, and statistical training have been shown to impact uncertainty understanding.
“Considering how characteristics such as education, graph reading ability (i.e., graph literacy), and statistical training have been shown to impact uncertainty understanding (Hopster-den Otter et al., 2019; Shah & Hoeffner, 2002; van der Bles et al., 2019; Zapata-Rivera et al., 2016), it is important to examine how to best communicate measurement accuracy in a population of clinical practitioners.”
From Visualizing Uncertainty to Promote Clinicians’ Understanding of Measurement Error (2023) · cites this paper as Shah & Hoeffner, 2002
Cites
Cleveland, W. S., & McGill, R. (1984) · Journal of the American Statistical Association · doi:10.2307/2288400 · Cited by 1,796 · see citing works
Functions, Graphs, and Graphing: Tasks, Learning, and Teaching
Leinhardt, G., Zaslavsky, O., & Stein, M. K. (1990) · Review of Educational Research · doi:10.3102/00346543060001001 · Cited by 1,012 · see citing works
Bars and lines: A study of graphic communication
Zacks, J., & Tversky, B. (1999) · Memory & Cognition · doi:10.3758/bf03201236 · Cited by 300 · see citing works
Melody Carswell, C., & Wickens, C. D. (1987) · Ergonomics · doi:10.1080/00140138708969741 · Cited by 169 · see citing works
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Challenges with graph interpretation: a review of the literature
Glazer, N. (2011) · Studies in Science Education · doi:10.1080/03057267.2011.605307 · Cited by 285 · see citing works
Teaching the process of science: faculty perceptions and an effective methodology
Coil, D., Wenderoth, M. P., Cunningham, M., & Dirks, C. (2010) · CBE—Life Sciences Education · doi:10.1187/cbe.10-01-0005 · Cited by 282 · see citing works
COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
Abdul, A., von der Weth, C., Kankanhalli, M., & Lim, B. Y. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376615 · Cited by 96 · see citing works
van den Bosch, R. M., Espin, C. A., Chung, S., & Saab, N. (2017) · Learning Disabilities Research & Practice · doi:10.1111/ldrp.12122 · Cited by 62 · see citing works
Visualizing Uncertainty to Promote Clinicians’ Understanding of Measurement Error
Frans, N., Hummelen, B., Albers, C. J., & Paap, M. C. S. (2023) · Assessment · doi:10.1177/10731911221147042 · Cited by 5 · see citing works
“Extensive research experience may influence participants’ familiarity with standard errors, confidence intervals, and certain visualizations, which in turn may influence their performance (Shah & Hoeffner, 2002).”
Background
- Authors
- Shah, P., & Hoeffner, J.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Shah, Priti
- ORCID
- Shah, Priti
- Venue
- Educational Psychology Review
- Published
- 2002-03
- Volume
- 14
- Issue
- 1
- Pages
- 47-69
- ISSN
- 1040-726X (ISSN-L)
- 1573-336X
- OpenAlex
- W1496523069
- Cited by
- 431
- References
- 4
- Citations by year
- 2026: 16 · 2025: 29 · 2024: 14 · 2023: 28 · 2022: 26 · 2021: 28 · 2020: 37 · 2019: 22 · 2018: 31 · 2017: 23 · 2016: 17 · 2015: 24 · 2014: 13 · 2013: 10 · 2012: 20
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Educational Psychology Review (publishedVersion)
- Deep Blue (University of Michigan) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- graph interpretation, graphical literacy, graph animation, graph size, data presentation, science education, social studies education, didactic implications
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