Premium
Information Visualization Evaluation Using Crowdsourcing
Author(s) -
Borgo R.,
Micallef L.,
Bach B.,
McGee F.,
Lee B.
Publication year - 2018
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13444
Subject(s) - crowdsourcing , visualization , computer science , data science , sample (material) , task (project management) , quality (philosophy) , information visualization , world wide web , data mining , philosophy , chemistry , management , epistemology , chromatography , economics
Abstract Visualization researchers have been increasingly leveraging crowdsourcing approaches to overcome a number of limitations of controlled laboratory experiments, including small participant sample sizes and narrow demographic backgrounds of study participants. However, as a community, we have little understanding on when, where, and how researchers use crowdsourcing approaches for visualization research. In this paper, we review the use of crowdsourcing for evaluation in visualization research. We analyzed 190 crowdsourcing experiments, reported in 82 papers that were published in major visualization conferences and journals between 2006 and 2017. We tagged each experiment along 36 dimensions that we identified for crowdsourcing experiments. We grouped our dimensions into six important aspects: study design & procedure, task type, participants, measures & metrics, quality assurance, and reproducibility. We report on the main findings of our review and discuss challenges and opportunities for improvements in conducting crowdsourcing studies for visualization research.