A Qualitative Evaluation And Taxonomy Of Student Annotations On Bar Charts

When sharing visualizations, annotations provide valuable insights into the data by focusing attention on important visual elements. As a result, annotations have become an essential part of visualizations, primarily when externalizing data or engaging in collaborative analysis. Therefore, it is crucial to understand how people annotate visualizations. This two-phase study used individual and group settings to investigate how visualization students annotate bar charts when asked to answer high-level questions about the data in the charts. The resulting annotations were coded and summarized into a taxonomy with several interesting findings. For example, we notice that several annotation types were broadly applied, while others were just used in special cases. In addition, ensembles of annotations were required to sufficiently annotate specific tasks. Our findings provide an early framework for contextualizing the usage of annotations, further providing guidance for best practice uses.

Md Dilshadur Rahman, Ghulam Jilani Quadri, and Paul Rosen. A Qualitative Evaluation And Taxonomy Of Student Annotations On Bar Charts. Visualization for Communication (VisComm), 2022.

@inproceedings{mdrahman2022annotation,
  title = {A Qualitative Evaluation and Taxonomy of Student Annotations on Bar Charts},
  author = {Rahman, Md Dilshadur and Quadri, Ghulam Jilani and Rosen, Paul},
  booktitle = {Visualization for Communication (VisComm)},
  year = {2022},
  abstract = {When sharing visualizations, annotations provide valuable insights into the
    data by focusing attention on important visual elements. As a result, annotations have
    become an essential part of visualizations, primarily when externalizing data or
    engaging in collaborative analysis. Therefore, it is crucial to understand how people
    annotate visualizations. This two-phase study used individual and group settings to
    investigate how visualization students annotate bar charts when asked to answer
    high-level questions about the data in the charts. The resulting annotations were coded
    and summarized into a taxonomy with several interesting findings. For example, we notice
    that several annotation types were broadly applied, while others were just used in
    special cases. In addition, ensembles of annotations were required to sufficiently
    annotate specific tasks. Our findings provide an early framework for contextualizing the
    usage of annotations, further providing guidance for best practice uses.}
}