Transcript Tim Burke

Radial Marking Menu
Performance
Improvement and
User Type Detection
Tim Burke - [email protected]
Prepared for CMSC601
Radial Marking
Menus
Why?
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Related Work Design
Gilles Bailly, Eric Lecolinet, and Laurence Nigay, “Flower Menus: A New Type
of Marking Menu with Large Menu Breadth, Within groups and Efficient
Expert Mode Memorization,” Proceedings of the working conference on
Advanced visual interfaces, 2008.
Tobias Hesselmann, Stefan Floring, and Marwin Schmitt, “Stacked Half-Pie
Menus: Navigating Nested Menus on Interactive Tabletops,” Proceedings of
the ACM International Conference on Interactive Tabletops and Surfaces, 2009.
G. Julian Lepinski, Tovi Grossman, George Fitzmaurice, “The Design and
Evaluation of Multitouch Marking Menus,” ACM SIGCHI Proceedings, 2010.
Krystian Samp and Stefan Decker, “Supporting menu design with radial
layouts,” Proceedings of the International Conference on Advanced Visual
Interfaces, 2010.
Feng Tian, Lishuang Xu, Hongan Wang, Xiaolong Zhang, Yuanyuan Liu, Vidya
Setlur, and Guozhong Dai, “Tilt Menu: Using the 3D Orientation Information
of Pen Devices to Extend the Selection Capability of Pen-based User
Interfaces,” ACM SIGCHI Proceedings, 2008.
Related Work - Performance
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Andy Cockburn, Carl Gutwin, and Saul
Greenberg, “A Predictive Model of
Menu Performance,” ACM SIGCHI
Proceedings, 2007.
Amy Hurst, Scott E. Hudson, and Jennifer
Mankoff, “Dynamic Detection of Novice
vs. Skilled Use Without a Task Model,”
ACM SIGCHI Proceedings, 2007.
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Background - Predictive
Model
“Morphing Menus”
change over time in
response to user
Yields a 12% to
25% reduction in
selection time of
frequently used
commands even
when factoring out
user memorization
Background - Dynamic
Detection
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Dynamic user type
detection through
trained C4.5 decision
tree statistical classifier
Novice and expert
users differ in needs
when interacting with
software
Achieves 90%+
detection accuracy with
proper training data
Experiment
• Two experiments:
• Menu adaptation through “morphing
menu” concept from Cockburn
experiment to radial marking menus
• Build dynamic detection classifier
from Hurst experiment by collecting
training data and testing for
accuracy with radial marking menus
Test Application
Evaluation
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Menu Adaptation Experiment
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Statistically significant reduction in menu
selection times as compared to baseline
tests with static, unchanging menus
Dynamic Detection Experiment
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Once trained, is the classifier able to
detect the user as novice or expert with
accuracy approaching that of the previous
experiment (approaching 90% accuracy)
Conclusion
• Radial marking menus poised to
become more popular
• Understanding ways to better leverage
them to provide a more powerful user
experience