Towards Quality Assessment of Crowdworker Output Based on Behavioral Data
Shigeaki Yuasa, Takumi Nakai, Takanori Maruichi, Manuel Landsmann, Koichi Kise, Masaki Matsubara, Atsuyuki Morishima · 2019
In this paper, we show preliminary results on the quality assessment of crowdworker output based on the movements of the mouse and the eyes while the task is performed. We assume that the mouse and the eyes stop longer if the quality is lower due to the lack of knowledge, or confidence, etc. Because the mouse- and eye-stopping duration follows lognormal distribution, we estimate its parameters (mean and standard deviation) to evaluate the quality. Results of preliminary experiments with 10 participants show that the parameters of correct outputs are different from those of incorrect ones. As compared to the task duration, which is often used as a feature for assessment, we have found that the mouse-and the eyestopping duration is advantageous and complementary for the assessment.