Slideshow transcript
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



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