Human-Computer Interaction Institute
School of Computer Science, Carnegie Mellon University
Using Microtask Continuity to Improve Corwdsourcing
Walter S. Lasecki*, Adam Marcus**, Jeffrey M. Rzeszotarski, Jeffrey P. Bigham
A rich body of cognitive science literature suggests that workers who focus on a single task in a large workflow leverage task specialization to improve the overall performance of the workflow, such as in an assembly line. However, crowdsourcing workflows often ignore worker growth over time, instead treating them as homogeneous computational units that can effortlessly move between small microtasks of different types. In this paper, we validate that workers often mix different task types via a survey, and then study the effects of such task type mixing. We collect empirical evidence from 338 crowd workers that suggests task interruptions significantly decrease worker performance. Specifically, we show that temporal interruptions, where there is a large delay between two tasks, can cause up to a 102% slowdown in task completion time, and contextual interruptions, where workers are asked to perform different tasks in sequence, can slow down completion time by 57%. Our results demonstrate the importance of considering continuity in workflow design for both individual worker efficiency and overall throughput.