Explaining the gap: Visualizing one's predictions improves recall and comprehension of data

Yea Seul Kim, Katharina Reinecke, Jessica Hullman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

Information visualizations use interactivity to enable user-driven querying of visualized data. However, users' interactions with their internal representations, including their expectations about data, are also critical for a visualization to support learning. We present multiple graphically-based techniques for eliciting and incorporating a user's prior knowledge about data into visualization interaction. We use controlled experiments to evaluate how graphically eliciting forms of prior knowledge and presenting feedback on the gap between prior knowledge and the observed data impacts a user's ability to recall and understand the data. We find that participants who are prompted to reflect on their prior knowledge by predicting and self-explaining data outperform a control group in recall and comprehension. These effects persist when participants have moderate or little prior knowledge on the datasets. We discuss how the effects differ based on text versus visual presentations of data. We characterize the design space of graphical prediction and feedback techniques and describe design recommendations.

Original languageEnglish (US)
Title of host publicationCHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
PublisherAssociation for Computing Machinery
Pages1375-1386
Number of pages12
ISBN (Electronic)9781450346559
DOIs
StatePublished - May 2 2017
Event2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States
Duration: May 6 2017May 11 2017

Publication series

NameConference on Human Factors in Computing Systems - Proceedings
Volume2017-May

Other

Other2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
CountryUnited States
CityDenver
Period5/6/175/11/17

Keywords

  • Information visualization
  • Internal representations of data
  • Mental models
  • Prediction
  • Self-explanation

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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    Kim, Y. S., Reinecke, K., & Hullman, J. (2017). Explaining the gap: Visualizing one's predictions improves recall and comprehension of data. In CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire (pp. 1375-1386). (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2017-May). Association for Computing Machinery. https://doi.org/10.1145/3025453.3025592