A Principled Way of Assessing Visualization Literacy

By Jeremy Boy, Ronald A. Rensink, Enrico Bertini, Jean-Daniel Fekete. IEEE Transactions on Visualization and Computer Graphics, 2014

306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7eb1c896a7823c4e3354e603cb46273cbb6ba03f

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

1. Bum Chul Kwon
2. Bill Howe
3. Dominik Moritz
4. Jeffrey Heer
5. Sukwon Lee
6. Adam M. Smith
7. Sung-Hee Kim
8. Greg L. Nelson

In Computer Vision and Pattern Recognition

1. Dominik Moritz
2. Bill Howe
3. Jeffrey Heer
4. Chenglong Wang
5. Adam M. Smith
6. Greg L. Nelson
7. Halden Lin
8. Bum Chul Kwon

In Statistics and Probability

1. Bill Howe
2. Dominik Moritz
3. Jeffrey Heer
4. Adam M. Smith
5. Greg L. Nelson
6. Halden Lin
7. Chenglong Wang

Citations in the corpus

• Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, background): “[6]”
• VLAT: Development of a Visualization Literacy Assessment Test (2017, methodology): “The topic of visualization literacy is gaining recognition within the Information Visualization community, and a number of researchers have begun to explore this topic [8] [10] [33].”