Mira Dontcheva, Robert R. Morris, Joel R. Brandt, and Elizabeth M. Gerber. 2014. Combining crowdsourcing and learning to improve engagement and performance. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14). Association for Computing Machinery, New York, NY, USA, 3379–3388. https://doi.org/10.1145/2556288.2557217

Abstract

Crowdsourcing complex creative tasks remains difficult, in part because these tasks require skilled workers. Most crowdsourcing platforms do not help workers acquire the skills necessary to accomplish complex creative tasks. In this paper, we describe a platform that combines learning and crowdsourcing to benefit both the workers and the requesters. Workers gain new skills through interactive step-by-step tutorials and test their knowledge by improving real-world images submitted by requesters. In a series of three deployments spanning two years, we varied the design of our platform to enhance the learning experience and improve the quality of the crowd work. We tested our approach in the context of LevelUp for Photoshop, which teaches people how to do basic photograph improvement tasks using Adobe Photoshop. We found that by using our system workers gained new skills and produced high-quality edits for requested images, even if they had little prior experience editing images. Author Keywords Crowdsourcing; training; games.