Information Visualization Evaluation Using Crowdsourcing

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)573-595
Number of pages23
JournalComputer Graphics Forum
Volume37
Issue number3
StatePublished - 2018
MoE publication typeA1 Journal article-refereed

Researchers

Research units

  • University of Edinburgh
  • King’s College London
  • Luxembourg Institute of Science and Technology
  • Microsoft Research

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.

    Research areas

  • Categories and Subject Descriptors (according to ACM CCS), I.3.3 [Computer Graphics]: Picture/Image Generation—Line and curve generation

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