predicting task execution time on handheld devices using the keystroke level model
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Predicting Task Execution Time on Handheld Devices
Using the Keystroke Level Model
Annie Lu Luo and Bonnie E. John
School of Computer ScienceCarnegie Mellon University
CHI’05 – April 6, 2005
© 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
© 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
© Annie Luo, Carnegie Mellon University, 2005 slide 4
Start
Handheld task: Find information about the MET
1
City map
Museums list
2Soft keyboard
4
Scroll list
3Graffiti
Region map Street map
Query result
© Annie Luo, Carnegie Mellon University, 2005 slide 5
Create KLMs
One KLM for each of the four methods Used CogTool (John, et al 2004)
MacroMediaDreamWeaver
BehaviorRecorder
NetscapeHTML
event handler
ACT-REnvironment
Modeler mocks up interfaces as HTML
storyboard
Modeler demonstrates tasks
on the HTML storyboard
HTML mockups
Interface event
messagesvia
LiveConnect
ACT-Simplecode
based on KLM
KLM Trace
ACT-Simple complies code into ACT-R production
rules
© Annie Luo, Carnegie Mellon University, 2005 slide 6
Mozilla Firefox
Behavior Recorder
ACT-R
© 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
© Annie Luo, Carnegie Mellon University, 2005 slide 8
0.000
2.000
4.000
6.000
8.000
10.000
12.000
14.000
16.000
Map SoftKB Graffiti ScrollBar
Old Model Time
Old User Time
New Model Time
New User Time
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%
© 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
© Annie Luo, Carnegie Mellon University, 2005 slide 10
Thank you!
Authors’ contact info:Bonnie John – bej@cs.cmu.eduAnnie Luo – luluo@cs.cmu.edu
The CogTool project:http://www.cs.cmu.edu/~bej/cogtool/
© Annie Luo, Carnegie Mellon University, 2005 slide 11
Participants information (backup)
User (gender) Device owned How long
1 (M) Palm Vx 5+ years
2 (M) Compaq iPAQ 3 years
3 (M) Palm IIIe 4 years
4 (M) Handspring Visor 3 years
5 (F) Handspring visor Pro 2 years
6 (F) Dell PDA 1 year
7 (M) iPAQ 3630 4 years
8 (M) Kyocera 7135 4+ years
9 (M) Handspring Visor Prism 3 years
10 (F) Palm VA 3 years
© Annie Luo, Carnegie Mellon University, 2005 slide 12
Results in paper (backup)Model Time vs. User Time
9.213
12.662 12.662
9.913
9.0055
12.8363
13.60328
10.29767
0
2
4
6
8
10
12
14
16
M1-MapNavigation
M2-Soft Keyboard M3-Graffiti M4-Scroll Bar
Tasks
Tim
e (s
ec)
Model TimeUser Time
(Average)
© Annie Luo, Carnegie Mellon University, 2005 slide 13
New results since paper published
0.000
2.000
4.000
6.000
8.000
10.000
12.000
Map SoftKB Graffiti ScrollBar
Model Time User Time
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
© Annie Luo, Carnegie Mellon University, 2005 slide 14
Interface Widgets: - Buttons- Check boxes- Text fields- Pull-down lists- Links- Menus- Audio input- Audio output
© Annie Luo, Carnegie Mellon University, 2005 slide 15
Netscape
Behavior Recorder
ACT-Simple
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