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  1. Proceedings of the 3rd BELIV'10 Workshop (BELIV '10)
  2. Comparing information graphics: a critical look at eye tracking
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Developing qualitative metrics for visual analytic environments
Many roads lead to Rome: mapping users' problem solving strategies
Exploring information visualization: describing different interaction patterns
Towards information-theoretic visualization evaluation measure: a practical example for Bertin's matrices
Learning-based evaluation of visual analytic systems
A descriptive model of visual scanning
Generating a synthetic video dataset
Is your user hunting or gathering insights?: identifying insight drivers across domains
Comparing benchmark task and insight evaluation methods on timeseries graph visualizations
Do Mechanical Turks dream of square pie charts?
Comparing information graphics: a critical look at eye tracking
Evaluating information visualization in large companies: challenges, experiences and recommendations

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Comparing information graphics: a critical look at eye tracking

Content Provider ACM Digital Library
Author Helfman, Jonathan I. Goldberg, Joseph H.
Abstract Effective graphics are essential for understanding complex information and completing tasks. To assess graphic effectiveness, eye tracking methods can help provide a deeper understanding of scanning strategies that underlie more traditional, high-level accuracy and task completion time results. Eye tracking methods entail many challenges, such as defining fixations, assigning fixations to areas of interest, choosing appropriate metrics, addressing potential errors in gaze location, and handling scanning interruptions. Special considerations are also required designing, preparing, and conducting eye tracking studies. An illustrative eye tracking study was conducted to assess the differences in scanning within and between bar, line, and spider graphs, to determine which graphs best support relative comparisons along several dimensions. There was excessive scanning to locate the correct bar graph in easier tasks. Scanning across bar and line graph dimensions before comparing across graphs was evident in harder tasks. There was repeated scanning between the same dimension of two spider graphs, implying a greater cognitive demand from scanning in a circle that contains multiple linear dimensions, than from scanning the linear axes of bar and line graphs. With appropriate task design and targeted analysis metrics, eye tracking techniques can illuminate visual scanning patterns hidden by more traditional time and accuracy results.
Starting Page 71
Ending Page 78
Page Count 8
File Format PDF
ISBN 9781450300070
DOI 10.1145/2110192.2110203
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-04-10
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Evaluation Visualization Eye tracking
Content Type Text
Resource Type Article
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