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

TL;DR

This paper advocates for integrating accessibility education into introductory data science courses by teaching multiple data representation methods, including visualizations, to ensure inclusivity for students with disabilities.

Contribution

It introduces the importance of teaching accessibility early in data science education and shares practical examples for instructors to implement inclusive data representations.

Findings

Accessibility should be integrated into early data science curricula

Multiple data representation methods enhance inclusivity

Practical examples support teaching accessibility effectively

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.