Task Assignment Strategies for Crowd Worker Ability Improvement

Abstract

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.