@article{Kobayashi_Morita_Matsubara_Shimizu_Morishima_2021, title={Empirical Study on Effects of Self-Correction in Crowdsourced Microtasks}, volume={8}, url={http://thebartonmethod.com/index.php/jhc/article/view/122}, DOI={10.15346/hc.v8i1.1}, abstractNote={<p>Self-correction for crowdsourced tasks is a two-stage setting that allows a crowd worker to review the task results of other workers; the worker is then given a chance to update their results according to the review.<br />Self-correction was proposed as a complementary approach to statistical algorithms, in which workers independently perform the same task.<br />It can provide higher-quality results with low additional costs. However, thus far, the effects have only been demonstrated in simulations, and empirical evaluations are required.<br />In addition, as self-correction provides feedback to workers, an interesting question arises: whether perceptual learning is observed in self-correction tasks.<br />This paper reports our experimental results on self-corrections with a real-world crowdsourcing service.<br />We found that:<br />(1) Self-correction is effective for making workers reconsider their judgments.<br />(2) Self-correction is effective more if workers are shown the task results of higher-quality workers during the second stage.<br />(3) A perceptual learning effect is observed in some cases. Self-correction can provide feedback that shows workers how to provide high-quality answers in future tasks.<br />(4) A Perceptual learning effect is observed, particularly with workers who moderately change answers in the second stage. This suggests that we can measure the learning potential of workers.<br />These findings imply that requesters/crowdsourcing services can construct a positive loop for improved task results by the self-correction approach.<br />However, (5) no long-term effects of the self-correction task were transferred to other similar tasks in two different settings.</p>}, number={1}, journal={Human Computation}, author={Kobayashi, Masaki and Morita, Hiromi and Matsubara, Masaki and Shimizu, Nobuyuki and Morishima, Atsuyuki}, year={2021}, month={Mar.} }