Frequency Based Detection Of Task Switches

Slide 1: Frequency-based detection of task switches Rahul Nair Yahoo! Research Berkeley rnair@yahoo-inc.com Steve Voida & Elizabeth Mynatt Georgia Institute of Technology {svoida, mynatt}@cc.gatech.edu

Slide 2: A Question How did you know that this talk was beginning?

Slide 3: A Question How did you know that this talk was beginning?  Increase in movement  Fiddling with the projector  New presenter looking around

Slide 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 

Slide 5: Frequency-based detection of task switches Rahul Nair Yahoo! Research Berkeley rnair@yahoo-inc.com Steve Voida & Elizabeth Mynatt Georgia Institute of Technology {svoida, mynatt}@cc.gatech.edu

Slide 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)

Slide 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

Slide 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

Slide 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 

Slide 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 

Slide 11: Results Subject Switches detected Switches confirmed Accuracy (%) Professor 1 (P1) 57 54 94.74 Professor 2 (P2) 76 43 56.58 Graduate Student 1 (G1) 367 123 33.51 Graduate Student 2 (G2) 217 117 53.92 Graduate Student 3 (G3) 91 37 40.66 IT Professional (IP) 225 48 21.33

Slide 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 –

Slide 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

Slide 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 

Slide 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…

Slide 16: Questions? Rahul Nair rnair@yahoo-inc.com www.rahulnair.net

Slide 17: Algorithm Consider a situation where the algorithm is evaluating the n+1th window event. ti is the time between the ith and (i-1)th event 1n Task average,  TA = n  t i i 1 1n  MA = kt i Moving average, k i n  MA Ratio = TA