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Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
Proposed Working Memory Measures for Evaluating Information Visualization Tools.
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Proposed Working Memory Measures for Evaluating Information Visualization Tools.

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Presenter: Laura Matzen, Laura McNamara, Kerstan Cole, Alisa Bandlow, Courtney Dornburg, Travis Bauer …

Presenter: Laura Matzen, Laura McNamara, Kerstan Cole, Alisa Bandlow, Courtney Dornburg, Travis Bauer
BELIV 2010 Workshop
http://www.beliv.org/beliv2010/

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  • 1. Proposed Working Memory Measures for Evaluating Information Visualization Tools<br />Laura Matzen, Laura McNamara, Kerstan Cole, <br />Alisa Bandlow, Courtney Dornburg &amp; Travis Bauer<br />Sandia National Laboratories<br />Albuquerque, NM 87185<br />This work was funded by Sandia’s Laboratory Research and Development Program as part of the <br />Networks Grand Challenge (10-119351). <br />Sandia National Laboratories is a multi-program laboratory operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin company, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.<br />
  • 2. Evaluation of information visualization tools<br />Evaluations typically developed for a single, specific task and tool<br />Time consuming, expensive, results can’t be generalized<br />We propose using measures of cognitive resources to create standardized evaluation metrics<br />
  • 3. Why assess cognitive resources?<br />All analysis tasks are cognitively demanding<br />Human cognitive resources are finite<br />Well-designed interfaces should free cognitive resources for making sense of data<br />Reduce cognitive burden of searching and manipulating data<br />
  • 4. Working Memory<br />Mental workspace underlying all complex cognition<br />Has a limited, measurable capacity<br />Often used as a performance metric in other domains<br />
  • 5. Proposed methodology<br />Evaluate visual analytics interfaces using a dual-task methodology:<br />Primary task: Interaction with interface <br />Secondary task: Test of working memory capacity<br />Performance on the secondary task should correspond to cognitive resources that would be available for sensemaking in a real-world analysis task<br />
  • 6. Example working memory task<br />Sternberg task (Sternberg, 1969)<br />Memory set  delay  probe items<br />Low-load memory set: M G J<br />High-load memory set: D K H Y R Q<br />M G J<br />F<br />M<br />W<br />
  • 7. Comparisons of different interface designs<br />M<br />W<br />F<br />
  • 8. Later, Compare Different Visualizations of Same Dataset<br />M<br />W<br />F<br />
  • 9. By this time next year…<br />Pilot Study 1: Compare two interface designs for a simple video player with tagging feature<br />Pilot Study 2: Compare two different interface designs for a visual text analytics application developed at Sandia<br />Use NASA TLX to develop convergent evidence <br />Both studies should provide insight into the use of working memory based metrics for interface assessment <br />…we should have data!<br />
  • 10. Comments, please!<br />

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