Presentation is loading. Please wait.

Presentation is loading. Please wait.

Predicting Task Execution Time on Handheld Devices Using the Keystroke Level Model Annie Lu Luo and Bonnie E. John School of Computer Science Carnegie.

Similar presentations


Presentation on theme: "Predicting Task Execution Time on Handheld Devices Using the Keystroke Level Model Annie Lu Luo and Bonnie E. John School of Computer Science Carnegie."— Presentation transcript:

1 Predicting Task Execution Time on Handheld Devices Using the Keystroke Level Model Annie Lu Luo and Bonnie E. John School of Computer Science Carnegie Mellon University CHI’05 – April 6, 2005

2 © Annie Luo, Carnegie Mellon University, 2005 slide 2 Motivation and goals Keystroke Level Model (KLM) A priori prediction of expert user task time Intensively used on desktop computers Not yet been adapted to handheld devices Limited display size Input device: stylus, touch-screen, hardware buttons Interaction methods: tap, Graffiti, etc. Investigate KLM on handheld UIs Applicability of model to novel interface modalities Accuracy of model predictions

3 © Annie Luo, Carnegie Mellon University, 2005 slide 3 KLM in brief Describe a task by placing operators in a sequence K – keystroke (tap) P – point with mouse (stylus) H – homing (move hand from mouse to keyboard) (N/A) D (takes parameters) – drawing (N/A) R (takes parameters) – system response time M – mental preparation G – Graffiti stroke (580 ms – Fleetwood, et al 2002) Five heuristic rules to insert candidate Ms into the sequence Task execution time = Σ all operators involved

4 © Annie Luo, Carnegie Mellon University, 2005 slide 4 Start Handheld task: Find information about the MET 1 City map Museums list 2 Soft keyboard 4 Scroll list 3 Graffiti Region map Street map Query result

5 © Annie Luo, Carnegie Mellon University, 2005 slide 5 Create KLMs One KLM for each of the four methods Used CogTool (John, et al 2004) MacroMedia DreamWeaver Behavior Recorder Netscape HTML event handler ACT-R Environment Modeler mocks up interfaces as HTML storyboard Modeler demonstrates tasks on the HTML storyboard HTML mockups Interface event messages via LiveConnect ACT-Simple code based on KLM KLM Trace ACT-Simple complies code into ACT-R production rules

6 © Annie Luo, Carnegie Mellon University, 2005 slide 6 Mozilla Firefox Behavior Recorder ACT-R

7 © Annie Luo, Carnegie Mellon University, 2005 slide 7 User study 10 expert PDA users (Female:Male = 3:7) At least one year experience using: Palm series, pocket PC, or smart cell phone Instructed to perform the task on a PalmVx Using four different methods (within subject design) Training session before real session Repeating each method for 10 times Data collection EventLogger: records system events to a log file Videotaped modeler’s behavior for verification

8 © Annie Luo, Carnegie Mellon University, 2005 slide 8 New results since paper published -9.3% 8.9% 5.8% 7.7% Latest version of CogTool Better estimation of system response time Detailed analysis of model and user traces (140/400 removed) 2.3% -1.4% -6.9% -3.7%

9 © Annie Luo, Carnegie Mellon University, 2005 slide 9 Conclusion & Future work KLMs produced with CogTool are effective for handheld user interfaces: Produces accurate execution time prediction Supports new input modalities: Graffiti Future work: Detailed analysis of the user pauses (mental time) Use predictions of pauses to assist energy management

10 © Annie Luo, Carnegie Mellon University, 2005 slide 10 Thank you! Authors’ contact info: Bonnie John – bej@cs.cmu.edu Annie Luo – luluo@cs.cmu.edu The CogTool project: http://www.cs.cmu.edu/~bej/cogtool/

11 © Annie Luo, Carnegie Mellon University, 2005 slide 11 Participants information (backup) User (gender)Device ownedHow long 1 (M)Palm Vx5+ years 2 (M)Compaq iPAQ3 years 3 (M)Palm IIIe4 years 4 (M)Handspring Visor3 years 5 (F)Handspring visor Pro2 years 6 (F)Dell PDA1 year 7 (M)iPAQ 36304 years 8 (M)Kyocera 71354+ years 9 (M)Handspring Visor Prism3 years 10 (F)Palm VA3 years

12 © Annie Luo, Carnegie Mellon University, 2005 slide 12 Results in paper (backup) (Average)

13 © Annie Luo, Carnegie Mellon University, 2005 slide 13 New results since paper published 9.3% -8.9%-5.8% -7.7% Better measurements of system response time Removed error trials (140 out of 400) Latest version of CogTool

14 © Annie Luo, Carnegie Mellon University, 2005 slide 14 Interface Widgets: - Buttons - Check boxes - Text fields - Pull-down lists - Links - Menus - Audio input - Audio output

15 © Annie Luo, Carnegie Mellon University, 2005 slide 15 Netscape Behavior Recorder ACT-Simple


Download ppt "Predicting Task Execution Time on Handheld Devices Using the Keystroke Level Model Annie Lu Luo and Bonnie E. John School of Computer Science Carnegie."

Similar presentations


Ads by Google