Toward measuring visualization insight
By Chris North. IEEE Computer Graphics and Applications, 2006
552 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/16ba2a31e479bf7a72137c9194f7961dd249e52b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Michael Sedlmair
- Geoffrey P. Ellis
- Petra Isenberg
- Florian Mansmann
- Daniel A. Keim
- Bum Chul Kwon
- Tobias Isenberg
- Jörn Kohlhammer
Citations in the corpus
- Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “As one aspect of efficiency of evaluation, Task based evaluation is generally not suitable to measure insight North in his Visualisation Viewpoints paper[82] focuses on the question of how to measure insight, the ultimate goal of all of visualisation.”
- Knowledge Generation Model for Visual Analytics (2014, background): “North [26] takes another view and lists some important characteristics of an insight such as being complex, deep, quantitative unexpected and relevant, and going beyond an individual observation of the data.”
- A Systematic Review on the Practice of Evaluating Visualization (2013, methodology): “Important differences between a quantitative, controlled approach and a more qualitative study technique that aims at measuring insight using open-ended protocols have also been argued well by North [50].”
- Visual Analytics: Definition, Process, and Challenges (2008, unknown): “How to assess the value of visualization is a topic of lively debate [42,33].”
- A Framework for Considering Comprehensibility in Modeling (2016, unknown): “For example, insight quantification approaches 30,31 could measure how well a model supports hypothesis formulation (building theory).”
- Evaluating Information Visualizations (2008, background): “While there is growing recognition in our research community that evaluation information visualization is difficult [55, 57, 67], the recognition of this difficulty has not in itself provided immediate answers of how to approach this problem.”