Who benefits from Visualization Adaptations? Towards a better Understanding of the Influence of Visualization Literacy

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

Abstract

The ability to read, understand, and comprehend visual information representations is subsumed under the term visualization literacy (VL). One possibility to improve the use of information visualizations is to introduce adaptations. However, it is yet unclear whether people with different VL benefit from adaptations to the same degree. We conducted an online experiment (n = 42) to investigate whether the effect of an adaptation (here: De-Emphasis) of visualizations (bar charts, scatter plots) on performance (accuracy, time) and user experiences depends on users' VL level. Using linear mixed models for the analyses, we found a positive impact of the De-Emphasis adaptation across all conditions, as well as an interaction effect of adaptation and VL on the task completion time for bar charts. This work contributes to a better understanding of the intertwined relationship of VL and visual adaptations and motivates future research.

Details

Original languageEnglish
Title of host publication2022 IEEE Visualization and Visual Analytics (VIS)
Place of Publication Oklahoma City, OK, USA
PublisherIEEE
Pages90-94
Number of pages5
ISBN (Electronic)978-1-6654-8812-9
ISBN (Print)978-1-6654-8813-6
Publication statusPublished - 16 Oct 2022
Peer-reviewedYes

Publication series

SeriesInternational Conference on Visualisation, VIS

Conference

Title2022 IEEE Visualization Conference, VIS 2022
Duration16 - 21 October 2022
CityVirtual, Online
CountryUnited States

External IDs

Mendeley 4f580078-3651-39c9-b89a-bed3cd2047dc
ORCID /0000-0002-1952-8302/work/142239531
ORCID /0000-0002-4280-6534/work/142251677

Keywords

Keywords

  • Human-centered computing, Information Visualization, online survey, User Experience, User Study, Visualization, Visualization Adaptation, Visualization Competence, Visualization design and evaluation methods, Visualization Literacy, On-line Survey