Training Workers for Improving Performance in Crowdsourcing Microtasks
Ujwal Gadiraju, Besnik Fetahu, Ricardo Kawase · Lecture notes in computer science · 2015
With the advent and growing use of crowdsourcing labor markets for a variety of applications, optimizing the quality of results produced is of prime importance. The quality of the results produced is typically a function of the performance of crowd workers. In this paper, we investigate the notion of treating crowd workers as ‘ learners ’ in a novel learning environment. This learning context is characterized by a short-lived learning phase and immediate application of learned concepts. We draw motivation from the desire of crowd workers to perform well in order to maintain a good reputation, while attaining monetary rewards successfully. Thus, we delve into training workers in specific microtasks of different types. We exploit (i) implicit training , where workers are provided training when they provide erraneous responses to questions with priorly known answers, and (ii) explicit training , where workers are required to go through a training phase before they attempt to work on the task itself. We evaluated our approach in 4 different types of microtasks with a total of 1200 workers, who were subjected to either one of the proposed training strategies or baseline case of no training . The results show that workers who undergo training depict an improvement in performance upto 5 %, and a reduction in the task completion time upto 41 %. Additionally, crowd training led to the elimination of malicious workers and a costs-benefit gain upto nearly 15 %.