Crowd Coach: Peer Coaching for Crowd Workers' Skill Growth
This addresses the problem of skill growth for crowd workers, offering a scalable alternative to expert-dependent solutions, though it is incremental in building on existing research.
The paper tackles the lack of skill development support for crowd workers on platforms like Amazon Mechanical Turk by introducing Crowd Coach, a peer coaching system that enhances workers' speed without sacrificing quality, particularly in audio transcription tasks.
Traditional employment usually provides mechanisms for workers to improve their skills to access better opportunities. However, crowd work platforms like Amazon Mechanical Turk (AMT) generally do not support skill development (i.e., becoming faster and better at work). While researchers have started to tackle this problem, most solutions are dependent on experts or requesters willing to help. However, requesters generally lack the necessary knowledge, and experts are rare and expensive. To further facilitate crowd workers' skill growth, we present Crowd Coach, a system that enables workers to receive peer coaching while on the job. We conduct a field experiment and real world deployment to study Crowd Coach in the wild. Hundreds of workers used Crowd Coach in a variety of tasks, including writing, doing surveys, and labeling images. We find that Crowd Coach enhances workers' speed without sacrificing their work quality, especially in audio transcription tasks. We posit that peer coaching systems hold potential for better supporting crowd workers' skill development while on the job. We finish with design implications from our research.