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| Content Provider | ACM Digital Library |
|---|---|
| Author | Dietze, Stefan Gadiraju, Ujwal |
| Abstract | Crowdsourcing has become an increasingly popular means to acquire human input on demand. Microtask crowdsourcing market-places facilitate the access to millions of people (called workers) who are willing to participate in tasks in return for monetary rewards or other forms of compensation. This paradigm presents a unique learning context where workers have to learn to complete tasks on-the-fly by applying their learning immediately through the course of tasks. However, most workers typically dropout early in large batches of tasks, depriving themselves of the opportunity to learn on-the-fly through the course of batch completion. By doing so workers squander a potential chance at improving their performance and completing tasks effectively. In this paper, we propose a novel method to engage and retain workers, to improve their learning in crowdsourced information finding tasks by using achievement priming. Through rigorous experimental findings, we show that it is possible to retain workers in long batches of tasks by triggering their inherent motivation to achieve and excel. As a consequence of increased worker retention, we find that workers learn to perform more effectively, depicting relatively more stable accuracy and lower task completion times in comparison to workers who drop out early. |
| Starting Page | 105 |
| Ending Page | 114 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450348706 |
| DOI | 10.1145/3027385.3027402 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-03-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Crowdsourcing Learning Information finding Microtasks Achievement priming Retention Crowd workers |
| Content Type | Text |
| Resource Type | Article |
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