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

Seo, JooYoung; Dogucu, Mine; (2023) Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations. Journal of Data Science 10.6339/23-jds1095. (In press). Green open access

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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.

Type: Article
Title: Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations
Open access status: An open access version is available from UCL Discovery
DOI: 10.6339/23-jds1095
Publisher version: https://doi.org/10.6339/23-jds1095
Language: English
Additional information: © 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China. Open access article under the CC BY license.
Keywords: curriculum; data representations; R
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10168367
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