Window Shopping: A Study of Desktop Window Switching
Designing for Attention and Multitasking
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Warr, Andrew
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Chi, Ed H.
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Harris, Helen
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Kuscher, Alexander
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Chen, Jenn
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Flack, Robert
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Jitkoff, Nicholas
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.3335-3338
© Copyright 2016 ACM
Summary: Desktop users frequently open and switch between multiple windows. Here we
present an experiment comparing 3 window switching interfaces: the Cards
interface spreads windows out like a vertical stack of cards with the most
recent window at the front; the Mosaic interface places each window in a grid
ordered by recency; and, the Exposé interface provides an map-like
overview based on the relative size and position of windows. Experimental
results suggest that the Mosaic interface scales, enabling faster window
selection than the Cards interface and less erroneous window selection than the
Exposé interface.
YouPivot: improving recall with contextual search
Search & stuff
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Hailpern, Joshua
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Jitkoff, Nicholas
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Warr, Andrew
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Karahalios, Karrie
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Sesek, Robert
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Shkrob, Nik
Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems
2011-05-07
v.1
p.1521-1530
© Copyright 2011 ACM
Summary: According to cognitive science literature, human memory is predicated on
contextual cues (e.g., room, music) in the environment. During recall tasks, we
associate information/activities/objects with contextual cues. However,
computer systems do not leverage our natural process of using contextual cues
to facilitate recall. We present a new interaction technique, Pivoting, that
allows users to search for contextually related activities and find a target
piece of information (often not semantically related). A sample motivation for
contextual search would be, 'what was that website I was looking at when
Yesterday by The Beatles was last playing?' Our interaction technique is
grounded in the cognitive science literature, and is demonstrated in our system
YouPivot. In addition, we present a new personal annotation method, called
TimeMarks, to further support contextual recall and the pivoting process. In a
pilot study, participants were quicker to identify websites, and preferred
using YouPivot, compared to current tools. YouPivot demonstrates how principles
of human memory can be applied to enhance the search of digital information.
The CLOTHO project: predicting application utility
Perspectives on design research
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Hailpern, Joshua
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Jitkoff, Nicholas
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Subida, Joseph
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Karahalios, Karrie
Proceedings of DIS'10: Designing Interactive Systems
2010-08-16
p.330-339
Keywords: application importance, application utility, interruptions, modeling, task
analysis, workflow analysis
© Copyright 2010 ACM
Summary: When using the computer, each user has some notion that "these applications
are important" at a given point in time. We term this subset of applications
that the user values as high-utility applications. Identifying high-utility
applications is a critical first step for Task Analysis, Time
Management/Workflow analysis, and Interruption research. However, existing
techniques fail to identify at least 57% of these applications. Our work
directly associates measurable computer interaction (CPU consumption, window
area, etc.) with the user's perceived application utility without identifying
task. In this paper, we present an objective utility function that accurately
predicts the user's subjective impressions of application importance, improving
existing techniques by 53%. This model of computer usage is based upon 321
hours of real-world data from 22 users (both professional and academic). Unlike
existing approaches, our model is not limited by a pre-existing set of
applications or known tasks. We conclude with a discussion of the direct
implications for improving accuracy in the fields of interruptions, task
analysis, and time management systems.
On improving application utility prediction
Work-in-progress, April 12-13
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Hailpern, Joshua
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Jitkoff, Nicholas
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Subida, Joseph
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Karahalios, Karrie
Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems
2010-04-10
v.2
p.3421-3426
Keywords: application importance, application utility, modeling
© Copyright 2010 ACM
Summary: When using the computer, each user has some notion that "these applications
are important" at a given point in time. We term this subset of applications
that the user values as high-utility applications. Identifying these
high-utility applications is critical to the fields of Task Analysis, User
Interruptions, Workflow Analysis, and Goal Prediction. Yet, existing techniques
to identify high-utility applications are based upon task identification,
conglomeration of related windows, limited qualitative observation, or common
sense. Our work directly associates measurable computer interaction (CPU
consumption, window area, etc.) with the user's perceived application utility.
In this paper, we present an objective utility function that accurately
predicts the user's subjective impressions of application importance. Our work
is based upon 321 hours of real-world data from 22 users (both professional and
academic) improving existing techniques by over 53%.
