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‘Externalizing Abstract Mathematical Models’

‘Externalizing Abstract Mathematical Models’. Lisa Tweedie,Robert Spence, Huw Dawkes and Hua Su Department of Electrical Engineering, Imperial College Of Science,Technology and Medicine London,UK. Conference proceedings on Human factors in computing systems ,

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‘Externalizing Abstract Mathematical Models’

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  1. ‘Externalizing Abstract Mathematical Models’ Lisa Tweedie,Robert Spence, Huw Dawkes and Hua Su Department of Electrical Engineering, Imperial College Of Science,Technology and Medicine London,UK. Conference proceedings on Human factors in computing systems, 1996, Page 406

  2. Data • Interactive Visualization Artifacts{IVAs} -Environments for problem solving • Visualization of precalculated or generated data from abstract mathematical models. • Not Raw Data

  3. Application Domain • - Engineering Design • Mantra • - Multiple ways of interactively linking simple graphs

  4. Mission • Parameters, Performances Optimize the Performance values by specifying the tolerance range of the Parameter variables. Overall Design Objective

  5. The Influence Explorer • Population of 600 precalculated light bulb designs • Performances -- Horizontal Histograms to the Left • Parameters -- Vertical Histograms to the Right

  6. The Prosection MatrixProjection of a section of parameter space • Alternative Perspective of the same precalculated data • Scatter plots arranged in a matrix • Each scatter plot corresponds to a pair of possible parameter combination • All combinations {4C2}of 4 parameters represented

  7. Projection of a section of parameter space

  8. Visualization when the parameters are set

  9. High yield and Wider tolerances

  10. Formative evaluation • Number of tests at different development stages • Ten pairs of participants • Tested first with Influence Explorer then with Prosection Matrix and then with Both • Each pair could complete a tolerance task in 30 min

  11. Lessons • Maximize directness of interactivity • Seek crucial information and give it a simple and pertinent representation • Trade off between amount of information, accuracy and simplicity

  12. Merits • Initial Qualitative Understanding • Performance Trade offs known with lesser effort • Quantitative Detail becomes clear by the color coding. • Parameter tolerance ranges defined with ease

  13. Demerits • Specific Requirements are hard to be visualized by color coded points • Hard to use without proper training • Designer’s experience is not enhanced

  14. HCI Metrics • User Performance **** • Error recovery **** • User satisfaction ? • Learning Time * • Retention ****

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