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Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations

JooYoung Seo, Mine Dogucu

TL;DR

This work addresses the lack of accessibility in introductory data science education by arguing for early, multi-modal data representations that go beyond traditional visualization. It proposes a practical curricular framework that encompasses data visualization, alt text, sonification, and tactile graphics, demonstrated through R-based teaching materials. The paper provides concrete tools and pedagogy (e.g., colorblindr, Okabe-Ito palettes, BrailleR, sonify, tactileR, Swell Form) and discusses delivery, assessment, and reproducibility to enable widespread adoption. By aligning with ADA requirements and industry needs, the approach aims to broaden participation and produce accessible, reproducible data products across disciplines.

Abstract

Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.

Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations

TL;DR

This work addresses the lack of accessibility in introductory data science education by arguing for early, multi-modal data representations that go beyond traditional visualization. It proposes a practical curricular framework that encompasses data visualization, alt text, sonification, and tactile graphics, demonstrated through R-based teaching materials. The paper provides concrete tools and pedagogy (e.g., colorblindr, Okabe-Ito palettes, BrailleR, sonify, tactileR, Swell Form) and discusses delivery, assessment, and reproducibility to enable widespread adoption. By aligning with ADA requirements and industry needs, the approach aims to broaden participation and produce accessible, reproducible data products across disciplines.

Abstract

Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.
Paper Structure (8 sections, 6 figures)

This paper contains 8 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Learning Goals and Objectives
  3. Data Visualization
  4. Data verbalization (i.e., alt text)
  5. Data sonification using sound
  6. Data tactualization using Swell Form machine
  7. Delivery of Content
  8. Closing Remarks

Figures (6)

  • Figure 1: Sample scatterplot showing the relationship between flipper length (mm) and bill length (mm) of penguins for three different species of penguins
  • Figure 2: Color Blindness Simulation with colorblindr
  • Figure 3: Scatterplot Using Okabe-Ito Color Pallette
  • Figure 4: Sample histogram for automatic data verbalization.
  • Figure 5: Visualizing the relationship of two numerical variables with scatter plot.
  • ...and 1 more figures