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dc.contributor.authorHettiachchi Mudiyanselage, Danula Eranjith
dc.date.accessioned2021-05-31T00:15:13Z
dc.date.available2021-05-31T00:15:13Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/11343/274837
dc.description© 2021 Danula Eranjith Hettiachchi Mudiyanselage
dc.description.abstractWhile crowd work on crowdsourcing platforms is becoming prevalent, there exists no widely accepted method to successfully match workers to different types of tasks. Previous work has considered using worker demographics, behavioural traces, and prior task completion records to optimise task assignment. However, optimum task assignment remains a challenging research problem, since proposed approaches lack an awareness of workers' cognitive abilities and context. This thesis investigates and discusses how to use these key constructs for effective task assignment: workers' cognitive ability, and an understanding of the workers' context. Specifically, the thesis presents 'CrowdCog', a dynamic online system for task assignment and task recommendations, that uses fast-paced online cognitive tests to estimate worker performance across a variety of tasks. The proposed task assignment method can achieve significant data quality improvements compared to a baseline where workers select preferred tasks. Next, the thesis investigates how worker context can influence task acceptance, and it presents 'CrowdTasker', a voice-based crowdsourcing platform that provides an alternative form factor and modality to crowd workers. Our findings inform how to better design crowdsourcing platforms to facilitate effective task assignment and recommendation, which can benefit both workers and task requesters.
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dc.subjectcrowdsourcing
dc.subjectdata quality
dc.subjecttask assignment
dc.subjecttask recommendation
dc.subjectvoice-based crowdsourcing
dc.subjectcrowd work
dc.subjectworker context
dc.subjectcognitive ability
dc.subjecthuman-centred computing
dc.subjecthuman-computer interaction
dc.titleTask assignment using worker cognitive ability and context to improve data quality in crowdsourcing
dc.typePhD thesis
melbourne.affiliation.departmentComputing and Information Systems
melbourne.affiliation.facultyEngineering and Information Technology
melbourne.thesis.supervisornameJorge Goncalves
melbourne.contributor.authorHettiachchi Mudiyanselage, Danula Eranjith
melbourne.thesis.supervisorothernameVassilis Kostakos
melbourne.tes.fieldofresearch1460806 Human-computer interaction
melbourne.tes.fieldofresearch2460803 Collaborative and social computing
melbourne.accessrightsOpen Access


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