AI summary · arXiv: 2202.11441v1 · cs.HC

Understanding How Older Adults Comprehend COVID-19 Interactive Visualizations via Think-Aloud Protocol

Mingming Fan, Yiwen Wang, Yuni Xie, Franklin Mingzhe Li, Chunyang Chen

Original paper · DOI Submitted to arXiv on 23 Feb. 2022 International Journal of Human-Computer Interaction License: CC BY-NC-SA 4.0

The essentials

  • COVID-19 pandemic has disproportionately affected older adults
  • Online interactive visualizations are increasingly popular for presenting information related to the pandemic
  • A user-centered approach was adopted to ensure accessibility of visualizations to older adults
  • Four types of thought processes and seven common interaction techniques used by the visualizations were identified through analyzing verbalizations of older adults interacting with the visualizations
  • Design guidelines were presented for making interactive visualizations more accessible to older adults, including increasing legibility through sub-guidelines such as having sufficient contrast in color schemes and text size as well as spacing out important elements so that navigating them does not require fine motor skills or good eyesight.
  • A taxonomy of COVID-19 information was constructed from captions and labels of axes from a data set of COVID-19 visualizations, revealing two themes related to COVID-19: expressions and impacts which contained several sub-themes each.
  • -Tasks for the visualizations were designed based on different levels of complexity and covering all common interaction types in Section 3.2.
  • -The findings provide valuable insights into designing online interactive COVID-19 visualizations that are accessible to older adults while providing important observations about the pandemic's impact on society at large.

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: COVID-19 Interactive Visualizations Older Adults Design Guidelines Taxonomy

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  • Original abstract
The COVID-19 pandemic has disproportionately affected older adults, making it critical for them to stay informed about the latest information. Online interactive visualizations such as live dashboards and websites have become increasingly popular for presenting this information. To ensure these visualizations are accessible to older adults, a user-centered approach was adopted by inviting them to interact with the visualizations while verbalizing their thought processes using a think-aloud protocol. Through analyzing their verbalizations, four types of thought processes were identified representing how older adults comprehended the visualizations and the challenges they encountered. Additionally, seven common types of interaction techniques used by the visualizations were also identified. Based on these findings, design guidelines were presented for making interactive visualizations more accessible to older adults. These guidelines include increasing legibility through sub-guidelines such as having sufficient contrast in color schemes and text size as well as spacing out important elements so that navigating them does not require fine motor skills or good eyesight. Furthermore, a taxonomy of COVID-19 information was constructed from captions and labels of axes from a data set of COVID-19 visualizations. This taxonomy revealed two themes related to COVID-19: expressions and impacts which contained several sub-themes each. Tasks for the visualizations were designed based on different levels of complexity and covering all common interaction types in Section 3.2. Overall, these findings provide valuable insights into designing online interactive COVID-19 visualizations that are accessible to older adults while providing important observations about the pandemic's impact on society at large.

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