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      Designing for Collaborative Sensemaking: Using Expert & Non-Expert Crowd

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          Abstract

          Crime solving is a domain where solution discovery is often serendipitous. Unstructured mechanisms, like Reddit, for crime solving through crowds have failed so far. Mechanisms, collaborations, workflows, and micro-tasks necessary for successful crime solving might also vary across different crimes. Cognitively, while experts might have deeper domain knowledge, they might also fall prey to biased analysis. Non-experts, while lacking formal training, might instead offer non-conventional perspectives requiring direction. The analytical process is itself an iterative process of foraging and sensemaking. Users would explore to broaden solution space and narrow down to a solution iteratively until identifying the global maxima instead of local maxima. In this proposal, my research aims to design systems for enabling complex sensemaking tasks that require collaboration between remotely located non-expert crowds with expert crowds to compensate for their cognitive challenges and lack of training. This would require better understanding of the structure, workflow, and micro-tasks necessary for successful collaborations. This proposal builds upon previous work on collaborative sensemaking between remote partners in lab experiments and endeavors to scale it across multiple team members, with varying expertise levels.

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          Human computation tasks with global constraints

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            Effects of visualization and note-taking on sensemaking and analysis

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              Effects of implicit sharing in collaborative analysis

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                Author and article information

                Journal
                2015-11-18
                2015-11-23
                1511.06053

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                conference in Companion of The Third AAAI Conference on Human Computation and Crowdsourcing (HCOMP-2015). arXiv admin note: substantial text overlap with arXiv:1511.05737
                cs.HC

                Human-computer-interaction

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