Exploring Trade-Offs Between Learning and Productivity in Crowdsourced History

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書誌事項

公開日
2018-11
権利情報
  • https://www.acm.org/publications/policies/copyright_policy#Background
DOI
  • 10.1145/3274447
公開者
Association for Computing Machinery (ACM)

説明

<jats:p>Crowdsourcing more complex and creative tasks is seen as a desirable goal for both employers and workers, but these tasks traditionally require domain expertise. Employers can recruit only expert workers, but this approach does not scale well. Alternatively, employers can decompose complex tasks into simpler micro-tasks, but some domains, such as historical analysis, cannot be easily modularized in this way. A third approach is to train workers to learn the domain expertise. This approach offers clear benefits to workers, but is perceived as costly or infeasible for employers. In this paper, we explore the trade-offs between learning and productivity in training crowd workers to analyze historical documents. We compare CrowdSCIM, a novel approach that teaches historical thinking skills to crowd workers, with two crowd learning techniques from prior work and a baseline. Our evaluation (n=360) shows that CrowdSCIM allows workers to learn domain expertise while producing work of equal or higher quality versus other conditions, but efficiency is slightly lower.</jats:p>

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