Review of Graph Comprehension Research: Implications for Instruction
By Priti Shah, J. Hoeffner. 2002
432 citations recorded by the source corpus.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/02491e2c97a4323dba5f4fee412f1b687777e748
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Lace M. K. Padilla
- Priti Shah
- Steven L. Franconeri
- Jeffrey M. Zacks
- Jessica R. Hullman
- Timothy Hubbard
In Computer Vision and Pattern Recognition
- Ashraf Abdul
- Mohan Kankanhalli
- Steven L. Franconeri
- Jeffrey M. Zacks
- Lace M. K. Padilla
- Priti Shah
- Brian Y. Lim
- Jessica R. Hullman
In Experimental and Cognitive Psychology
- Michael Schneider
- Priti Shah
- Ellen Peters
- Steven L. Franconeri
- Brian Y. Lim
- Roland H. Grabner
- Jeffrey M. Zacks
- Mirta Galesic
Citations in the corpus
- Mental number line, number line estimation, and mathematical achievement : Their interrelations in grades 5 and 6 (2009, background): “Some of the correct interpretations related to the meaning of the widths or heights of the graphs, while others related to the meaning of the slope as representing the proportion of y-change and x-change (Shah & Hoeffner, 2002).”
- Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers (2013, background): “Such theories could help to translate the growing research in graph cognition and design [78,79] into practical advice for risk communicators.”
- The Science of Visual Data Communication: What Works (2021, background): “…might try to remove roadblocks for students struggling to translate visual depictions to their underlying concepts (Börner & Polley, 2014; Shah & Hoeffner, 2002) or seek multimedia design principles for designing effective graphics and integrating them with text (e.g., Mayer & Fiorella,…”
- COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations (2020, background): “Since explanations are often visualized as charts, we focus on human understanding of line and bar charts and draw on research on graph comprehension and visualization literacy [33, 58, 59].”