A Review of Research and Practices on Teaching Data Visualizations for Blind and Visually Impaired Students

Authors

Shiya Cao, Smith CollegeFollow

Document Type

Article

Publication Date

10-3-2025

Publication Title

Chance

Volume

38

Issue

3

Abstract

Around 36 million people in the world are blind and an additional 217 million have moderate to severe vision impairment. In higher education, four percent of 54,204 undergraduates who participated in the 2022 American College Health Association survey reported to be blind or have low vision. Those students frequently do not have access to data visualizations we generally teach and use in postsecondary statistics and data science classes. The design of those visualizations is premised on implicit assumptions about the user’s visual ability. Making data visualizations accessible to blind and visually impaired (BVI) people would help improve equity in higher education and benefit them with data driven reasoning and communication. The Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report lists creating and interpreting graphical displays as one of the nine central goals for introductory statistics. We, as statistics and data science educators, researchers, or practitioners, should practice how to design accessible data visualizations, teach and use them in a way that is inclusive to the BVI community. This short article is a review of research and practices on teaching data visualizations for BVI students. I collected resources from related email threads in the Isolated Statisticians email list server and published articles over time. While this article pays particular attention to the BVI community, I acknowledge that other types of disabilities such as cognitive and motor disabilities may affect access to data visualizations as well.

First Page

28

Last Page

34

Recommended Citation

Cao, Shiya, "A Review of Research and Practices on Teaching Data Visualizations for Blind and Visually Impaired Students" (2025). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/85

Digital Object Identifier (DOI)

10.1080/09332480.2025.2560278

Rights

Licensed to Smith College and distributed CC-BY 4.0 under the Smith College Faculty Open Access Policy.

Version

Author's Accepted Manuscript

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.