Radial Marking Menu Performance Improvement and User Type Detection Tim Burke - Prepared for CMSC601

Radial Marking Menus

Why?

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, 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, G. Julian Lepinski, Tovi Grossman, George Fitzmaurice, “The Design and Evaluation of Multitouch Marking Menus,” ACM SIGCHI Proceedings, Krystian Samp and Stefan Decker, “Supporting menu design with radial layouts,” Proceedings of the International Conference on Advanced Visual Interfaces, 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 Andy Cockburn, Carl Gutwin, and Saul Greenberg, “A Predictive Model of Menu Performance,” ACM SIGCHI Proceedings, Amy Hurst, Scott E. Hudson, and Jennifer Mankoff, “Dynamic Detection of Novice vs. Skilled Use Without a Task Model,” ACM SIGCHI Proceedings, 2007.

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 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 Menu Adaptation Experiment Statistically significant reduction in menu selection times as compared to baseline tests with static, unchanging menus Dynamic Detection Experiment 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