Frequency Based Detection Of Task Switches

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

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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

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

Frequency Based Detection Of Task Switches

by rnair on Aug 08, 2007

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