CrowdMuse: An Adaptive Crowd Brainstorming System
Pages 93 - 95
Abstract
Online crowds, with their large numbers and diversity, show great potential for creativity, particularly during large-scale brainstorming sessions. Research has explored different ways of augmenting this creativity, such as showing ideators some form of inspiration to get them to explore more categories or generate more ideas. The mechanisms used to select which inspirations are shown to ideators thus far have been focused on characteristics of the inspirations rather than on ideators. This can hinder their effect, as creativity research has shown that ideators have unique cognitive structures and may therefore be better inspired by some ideas rather than others. We introduce CrowdMuse, an adaptive system for supporting large scale brainstorming. The system models ideators based on their past ideas and adapts the system views and inspiration mechanisms accordingly. An evaluation of this system could inform how to better individually support ideators.
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Index Terms
- CrowdMuse: An Adaptive Crowd Brainstorming System
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Published In
October 2018
251 pages
ISBN:9781450359498
DOI:10.1145/3266037
- General Chairs:
- Patrick Baudisch,
- Albrecht Schmidt,
- Program Chair:
- Andy Wilson
Copyright © 2018 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Published: 11 October 2018
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UIST '18: The 31st Annual ACM Symposium on User Interface Software and Technology
October 14, 2018
Berlin, Germany
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