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Task Routing and Assignment in Crowdsourcing based on Cognitive Abilities


Goncalves, Jorge; Feldman, Michael; Hu, Subingqian; Kostakos, Vassilis; Bernstein, Abraham (2017). Task Routing and Assignment in Crowdsourcing based on Cognitive Abilities. In: World Wide Web Conference - Web Science Track, Perth, Australia, 3 April 2017 - 7 April 2017.

Abstract

Appropriate task routing and assignment is an important, but often overlooked, element in crowdsourcing research and practice. In this paper, we explore and evaluate a mechanism that can enable matching crowdsourcing tasks to suitable crowd-workers based on their cognitive abilities. We measure participants’ visual and fluency cognitive abilities with the well-established Kit of Factor- Referenced Cognitive Test, and measure crowdsourcing performance with our own set of developed tasks. Our results indicate that participants’ cognitive abilities correlate well with their crowdsourcing performance. We also built two predictive models (beta and linear regression) for crowdsourcing task performance based on the performance on cognitive tests as explanatory variables. The model results suggest that it is feasible to predict crowdsourcing performance based on cognitive abilities. Finally, we discuss the benefits and challenges of leveraging workers’ cognitive abilities to improve task routing and assignment in crowdsourcing environments.

Abstract

Appropriate task routing and assignment is an important, but often overlooked, element in crowdsourcing research and practice. In this paper, we explore and evaluate a mechanism that can enable matching crowdsourcing tasks to suitable crowd-workers based on their cognitive abilities. We measure participants’ visual and fluency cognitive abilities with the well-established Kit of Factor- Referenced Cognitive Test, and measure crowdsourcing performance with our own set of developed tasks. Our results indicate that participants’ cognitive abilities correlate well with their crowdsourcing performance. We also built two predictive models (beta and linear regression) for crowdsourcing task performance based on the performance on cognitive tests as explanatory variables. The model results suggest that it is feasible to predict crowdsourcing performance based on cognitive abilities. Finally, we discuss the benefits and challenges of leveraging workers’ cognitive abilities to improve task routing and assignment in crowdsourcing environments.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:7 April 2017
Deposited On:04 Jan 2018 08:36
Last Modified:19 Feb 2018 09:57
OA Status:Green
Publisher DOI:https://doi.org/10.1145/3041021.3055128
Other Identification Number:merlin-id:14648

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