Making sense of graphs: Critical factors influencing comprehension and instructional implications.
By Susan N. Friel, Frances R. Curcio, G. Bright. 2001
807 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5c59c684260130d4fd435b0da729e01a13358c75
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
- Matthew Brehmer
- Bum Chul Kwon
- Sukwon Lee
- Sung-Hee Kim
- Tamara Munzner
- E. Mooney
- Michael Sedlmair
- Shixia Liu
In Statistics and Probability
- Carmen Díaz
- Tamara Munzner
- Carmen Batanero
- Matthew Brehmer
- Mitchell J. Nathan
- E. Mooney
- Kristen N. Bieda
- K. Koedinger
Citations in the corpus
- A Framework for Characterizing Middle School Students' Statistical Thinking (2002, background): “Friel, Curcio, and Bright (2001) stated that the graphical sense involved in representing data “includes a consideration of what is involved in constructing graphs as tools for structuring data and, more important, what is the optimal choice for a graph in a given situation” (p. 145).”
- Representational disfluency in algebra: evidence from student gestures and speech (2009, background): “In later grades, the emphasis is put on ‘‘reading beyond the data’’ (Curcio, 1987) and abstraction or modeling, where quantitative patterns in the data are captured by concise equations or rules (Bertin, 1983, in Friel, Curcio, and Bright, 2001).”
- VLAT: Development of a Visualization Literacy Assessment Test (2017, background): “They defined graph comprehension as reading and interpreting a graph [24], and they proposed the three levels of graph comprehension framework [7, 13, 49]: (1) the first level is the elementary level in which a graph reader can read a specific value in a graph (e.”