Communication Dans Un Congrès Année : 2021

Task Assignment Strategies for Crowd Worker Ability Improvement

Résumé

Workers are the most important resource in crowdsourcing. However, only investing in worker-centric needs, such as skill improvement, often conflicts with short-term platform-centric needs, such as task throughput. This paper studies learning strategies in task assignment in crowdsourcing and their impact on platform-centric needs. We formalize learning potential of individual tasks and collaborative tasks, and devise an iterative task assignment and completion approach that implements strategies grounded in learning theories. We conduct experiments to compare several learning strategies in terms of skill improvement, and in terms of task throughput and contribution quality. We discuss how our findings open new research directions in learning and collaboration. CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing.
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Dates et versions

hal-03379748 , version 1 (19-10-2021)

Identifiants

  • HAL Id : hal-03379748 , version 1

Citer

Masaki Matsubara, Ria Mae Borromeo, Atsuyuki Morishima, Sihem Amer-Yahia. Task Assignment Strategies for Crowd Worker Ability Improvement. The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, Oct 2021, Virtual, France. ⟨hal-03379748⟩
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