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Frequency-based detection of task switches Rahul Nair Yahoo! Research Berkeley [email_address] Steve Voida & Elizabeth Mynatt Georgia Institute of Technology {svoida, mynatt}@cc.gatech.edu
A Question How did you know that this talk was beginning?
A Question How did you know that this talk was beginning? Increase in movement Fiddling with the projector New presenter looking around
A Question How did you know that this talk was beginning? Increase in movement Fiddling with the projector New presenter looking around The amount of activity in the room changed
Frequency-based detection of task switches Rahul Nair Yahoo! Research Berkeley [email_address] Steve Voida & Elizabeth Mynatt Georgia Institute of Technology {svoida, mynatt}@cc.gatech.edu
People are particular about Displays Users have a working set of windows related to each task (Henderson 1986) There are distinct differences in window layouts for each task (Hutchins 2004) As interested in hiding windows as they are in displaying task windows (Hutchins 2004)
A frequency-based approach There is a shift in the window interaction frequencies as users setup for each task The shift could be either an increase or decrease in the average time between interactions Rearranging windows is as significant as opening or closing them
Software design Tracks the low level window manipulation  Uses a sliding window algorithm to compare current interaction speed with session average If the window average exceeds the thresholds of the session average a switch is logged Best results with a 7 interaction window and a thresholds of 0.67 and 1.5
Software design If a switch is detected the software asks the user for confirmation Uses an IM style popup window Ignored popups are considered to be false positives 5 minute timeout between successive popups
Study Design 6 participants 2 Professors, 3 grad students, 1 IT professional Installed on their primary work machine 2 week study Followed by questionnaires and interviews
Results 21.33 48 225 IT Professional (IP) 40.66 37 91 Graduate Student 3 (G3) 53.92 117 217 Graduate Student 2 (G2) 33.51 123 367 Graduate Student 1 (G1) 56.58 43 76 Professor 2 (P2) 94.74 54 57 Professor 1 (P1) Accuracy (%) Switches confirmed Switches detected Subject
User Feedback P1 was very appreciative and said that almost all her tasks were detected P2 felt that short tasks were not always detected Rapid task switching behavior IP had the lowest accuracy but was the most enthusiastic co-opted out log files to fill out project time cards Noticed all switches but was over sensitive
The Instant Messaging (IM) Effect Some of the variance can be explained by IM usage  High accuracy subjects did not use IM while low accuracy subjects were regular IM users The software detected IM usage as a task switch while the users felt that IM was not a new task Users felt that IM was a side channel of information and were irritated when it was detected as a new task
Advantages Can have extremely high accuracy Low computational cost Fewer privacy concerns since actual document and activity data is not being tagged Can spot a task switch without requiring identification of the task itself Time management applications
Future Work Integrating web browser URL information Dynamically adapting to users by adjusting window sizes, threshold values and popup timeouts Allow users to explicitly ignore windows like IM, etc
Questions? Rahul Nair [email_address] www.rahulnair.net
Consider a situation where the algorithm is evaluating the n+1 th  window event. t i  is the time between the i th  and (i-1) th  event Task average,    = Moving average,    = Ratio   = Algorithm
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Frequency Based Detection Of Task Switches

  • 1. Frequency-based detection of task switches Rahul Nair Yahoo! Research Berkeley [email_address] Steve Voida & Elizabeth Mynatt Georgia Institute of Technology {svoida, mynatt}@cc.gatech.edu
  • 2. A Question How did you know that this talk was beginning?
  • 3. A Question How did you know that this talk was beginning? Increase in movement Fiddling with the projector New presenter looking around
  • 4. A Question How did you know that this talk was beginning? Increase in movement Fiddling with the projector New presenter looking around The amount of activity in the room changed
  • 5. Frequency-based detection of task switches Rahul Nair Yahoo! Research Berkeley [email_address] Steve Voida & Elizabeth Mynatt Georgia Institute of Technology {svoida, mynatt}@cc.gatech.edu
  • 6. People are particular about Displays Users have a working set of windows related to each task (Henderson 1986) There are distinct differences in window layouts for each task (Hutchins 2004) As interested in hiding windows as they are in displaying task windows (Hutchins 2004)
  • 7. A frequency-based approach There is a shift in the window interaction frequencies as users setup for each task The shift could be either an increase or decrease in the average time between interactions Rearranging windows is as significant as opening or closing them
  • 8. Software design Tracks the low level window manipulation Uses a sliding window algorithm to compare current interaction speed with session average If the window average exceeds the thresholds of the session average a switch is logged Best results with a 7 interaction window and a thresholds of 0.67 and 1.5
  • 9. Software design If a switch is detected the software asks the user for confirmation Uses an IM style popup window Ignored popups are considered to be false positives 5 minute timeout between successive popups
  • 10. Study Design 6 participants 2 Professors, 3 grad students, 1 IT professional Installed on their primary work machine 2 week study Followed by questionnaires and interviews
  • 11. Results 21.33 48 225 IT Professional (IP) 40.66 37 91 Graduate Student 3 (G3) 53.92 117 217 Graduate Student 2 (G2) 33.51 123 367 Graduate Student 1 (G1) 56.58 43 76 Professor 2 (P2) 94.74 54 57 Professor 1 (P1) Accuracy (%) Switches confirmed Switches detected Subject
  • 12. User Feedback P1 was very appreciative and said that almost all her tasks were detected P2 felt that short tasks were not always detected Rapid task switching behavior IP had the lowest accuracy but was the most enthusiastic co-opted out log files to fill out project time cards Noticed all switches but was over sensitive
  • 13. The Instant Messaging (IM) Effect Some of the variance can be explained by IM usage High accuracy subjects did not use IM while low accuracy subjects were regular IM users The software detected IM usage as a task switch while the users felt that IM was not a new task Users felt that IM was a side channel of information and were irritated when it was detected as a new task
  • 14. Advantages Can have extremely high accuracy Low computational cost Fewer privacy concerns since actual document and activity data is not being tagged Can spot a task switch without requiring identification of the task itself Time management applications
  • 15. Future Work Integrating web browser URL information Dynamically adapting to users by adjusting window sizes, threshold values and popup timeouts Allow users to explicitly ignore windows like IM, etc
  • 16. Questions? Rahul Nair [email_address] www.rahulnair.net
  • 17. Consider a situation where the algorithm is evaluating the n+1 th window event. t i is the time between the i th and (i-1) th event Task average, = Moving average, = Ratio = Algorithm