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Scanning Between Graph Visualizations: An Eye Tracking Evaluation.
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Scanning Between Graph Visualizations: An Eye Tracking Evaluation.


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Presenter: Joseph Goldberg, Jonathan Helfman …

Presenter: Joseph Goldberg, Jonathan Helfman
BELIV 2010 Workshop

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  • 1. Comparing Information Graphics: A Critical Look at Eye Tracking Joe Goldberg and Jon Helfman BELIV ‘10 – 4/10/10
  • 2. Introduction
    • How to select the most appropriate graph type?
    • Consider three graphs, showing identical data. Can eye tracking methods help determine which graph type(s)
      • Are easiest to comprehend?
      • Are most appropriate for tasks?
      • Can be compared efficiently?
    • Eye tracking may potentially reveal trends that are not easily observed by traditional usability metrics...but, eye tracking brings its own set of challenges
  • 3. Eye Tracking Challenges: Practice-Related
    • When is Eye Tracking Appropriate, and What Can it Tell Us?
    • Designing and Conducting Studies
      • Length/number of tasks?
      • Allow mouse? Voice?
      • Recruitment profile? Number?
      • Allow dynamic stimuli?
    • Analyzing and Reporting Data
      • How much time required?
      • Use quantitative metrics?
      • Spatial (heatmaps) vs. temporal (scanpaths)?
      • How to aggregate eye tracking scanpaths?
      • How to extract scanning strategy from data?
      • When are results significant enough to be meaningful?
  • 4. Eye Tracking Challenges: Technical
    • How to Define Fixations?
      • Several methods
    • How to Define AOIs?
      • Padding
      • Overlap, hierarchy
    • Assign Fixations to AOIs?
      • Spatial location + path?
    • Gaze Location Error
      • Calibration
      • Foveal Area/Resolution
      • Attentional Dissociation
    • Manage Scanpath Interruptions?
    • How to Define Metrics?
      • No best practices or standards
  • 5. Comparison of Information Graphics: An Evaluation Study
    • Objective
      • Which graph type(s) best support relative comparison along multiple dimensions, within and between graphs?
      • Compare bar, line, and spider graphs
    • Participants: 5 graph users (Oracle colleagues)
    • Apparatus: Tobii T60 Eye Tracking system
    • 6 Tasks (same order)
      • 3 Easy (2 dimensions, 1 graph)
        • “ Should Candidate 2 be hired, considering only job dimensions a and b?”
      • 3 Difficult (8 dimensions, 2 graphs)
        • “ Which job candidate exceeded the greatest number of job requirements: 2 or 4?”
  • 6. Graph Stimuli
    • Each 4-graph set:
      • 15x15 cm, Center of screen
    1 job candidate per graph, indicated by number Job dimensions indicated by a-h Job requirements Candidate abilities
  • 7. Completion vs. First Fixation Times
    • Completion Times Include Scanning, Decision Making, and Validation
    Decision making, validation, uncertainty
  • 8. Scanning Strategies
    • Two Different Comparison Strategies
      • Bar Graphs: Most of first bar graph dimensions viewed before second bar graph viewed
      • Spider Graphs: Much more reciprocal scanning between dimensions, compared to bar graphs
  • 9. Discussion Questions
    • How can eye tracking methods help evaluate information visualizations?
    • What are the most appropriate metrics?
    • Can eye tracking methods evaluate dynamic, interactive visualization usability?
    • How to aggregate and represent group scanning strategies on visualizations?
    • Is there a ‘standard practice’ for eye tracking methods?