Exploring Trade-Offs Between Learning and Productivity in Crowdsourced History
Exploring Trade-Offs Between Learning and Productivity in Crowdsourced History
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探索众包历史中学习与生产力之间的权衡
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Kurt Luther
中科院分区：
文献类型：
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作者：
Nai;D. Hicks;Kurt Luther
众包更复杂和创造性的任务被视为雇主和工人的理想目标，但这些任务传统上需要领域专业知识。雇主可以只招聘专家员工，但这种方法并不能很好地扩展。或者，雇主可以将复杂的任务分解为更简单的微任务，但某些领域，如历史分析，不能以这种方式轻松模块化。第三种方法是培训工人学习该领域的专业知识。这种方法为工人提供了明显的好处，但对雇主来说被认为是昂贵或不可行的。在本文中，我们探讨了学习和生产力之间的权衡，在培训人群工作者分析历史文献。我们比较了CrowdSCIM，一种新的方法，教历史思维技能的人群工作者，从以前的工作和基线的两个人群学习技术。我们的评估（n=360）表明，CrowdSCIM允许工人学习领域的专业知识，同时生产同等或更高质量的工作与其他条件相比，但效率略低。
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 In this paper, we explore the trade-offs between learning and productivity in training crowd workers to analyze historical documents.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.