Maintaining an awareness of collaborators' actions is critical during collaborative
work, including during collaborative visualization activities. Particularly when collaborators are located at a distance, it is important to know what everyone is working
on in order to avoid duplication of effort, share relevant results in a timely manner
and build upon each other's results. Can a person's brushing actions provide an indication of their queries and interests in a data set? Can these actions be revealed
to a collaborator without substantially disrupting their own independent work? I
designed a study to answer these questions in the context of distributed collaborative visualization of tabular data. Participants in my study worked independently
to answer questions about a tabular data set, while simultaneously viewing brushing actions of a fictitious collaborator, shown directly within a shared workspace. I
compared three methods of presenting the collaborator's actions: brushing & linking
(i.e. highlighting exactly what the collaborator would see), selection (i.e. showing
only a selected item), and persistent selection (i.e. showing only selected items but
having them persist for some time). My results demonstrated that persistent selection enabled some awareness of the collaborator's activities while causing minimal
interference with independent work. Other techniques were less effective at providing
awareness, and brushing & linking caused substantial interference. These findings
suggest promise for the idea of exploiting natural brushing actions to provide awareness in collaborative work. / Graduate / 0984 / amirhos.hajiz@gmail.com
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5046 |
| Date | 27 November 2013 |
| Creators | Hajizadeh, Amir Hossein |
| Contributors | Tory, Melanie |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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