Difference: C-TOCLiteratureReview (52 vs. 53)

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C-TOC Literature Review

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  • extend COi model to include non-boundary points
  • implement our COI model within an existing interruption reasoning system
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[Adamczyk 04]

Adamczyk PD, Bailey BP. If not now, when?: the effects of interruption at different moments within task execution. of the SIGCHI conference on Human. 2004;6(1):271-278. Available at: http://portal.acm.org/citation.cfm?id=985692.985727.

  • abstract: measure effects of interrupting a user at different moments within task execution in terms of task performance, emotional state, social attribution; different interruption moments have different impacts on user emotional state and positive social attribution; design of an attention manager;

  • intro: poorly time interruptions can adversely affect task performance; authors identify moments for interruption utilising task models based on event perception research;
  • results show that predicted points for interruption consistently produced less annoyance, frustration, and time pressure, required less mental effort, deemed by user more respectful of their primary task;

  • discussion: predicted worse condition produced the worst results on the same measures, causing users to rate their experience even more poorly than the random condition
  • implications for design: designers should consider alternate modalities for interruption; multimodal interruption schemes;
  • interruption lag can cue a rehearsal process before onset of an interruption to help in resuming primary task; however, training users in a rehearsal strategy actually decreased task performance [Miller 2007]. (Window of opportunity: Using the interruption lag to manage disruption in complex tasks. HFES '02.)
 
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[Gluck 07]

Gluck J, Bunt A, McGrenere J. Matching attentional draw with utility in interruption. Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '07. 2007:41. Available at: http://portal.acm.org/citation.cfm?doid=1240624.1240631.

  • CPSC 544 notes: One aspect that I felt this article was lacking was the emphasis on the need for interruptions and notifications to be consistent across all applications on a system, and within each application a subset of low, medium, and high-priority interruptions. In addition to this, all applications may not be equally important, which will vary from user to user. For instance, User A may rank their email application program as a high-priority application, while their office suite is ranked as medium-priority, and their media player as a low priority application (a medium-priority interruption from the email application (i.e. incoming mail with no high-priority flag set by the sender) would be more important than a medium-priority interruption from the media player (i.e. notification of a song change)). Meanwhile, User B might not care about email at all, and could have an entirely different ranking of applications (and thus a different ranking of interruptions) - (i.e. they want to receive Facebook Wall Post alerts before anything else...). To a certain extent the application GROWL (Mac / Windows) allows users to set these priorities, and make the interruptions consistent in terms of timing and look-and-feel across all applications.

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  • Fogarty J, Hudson SE, Lai J. Examining the robustness of sensor-based statistical models of human interruptibility. Proceedings of the 2004 conference on Human factors in computing systems - CHI '04. 2004;6(1):207-214. Available at: http://portal.acm.org/citation.cfm?doid=985692.985719.
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On Prospective memory (PM) and interruption/distraction

[Farrimond 06]

 
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