Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations
Volume 21, Issue 2 (2023): Special Issue: Symposium Data Science and Statistics 2022, pp. 428–441
Pub. online: 21 March 2023
Type: Education In Data Science
Open Access
Open Access
Received
31 July 2022
31 July 2022
Accepted
27 February 2023
27 February 2023
Published
21 March 2023
21 March 2023
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.
References
Barata A, Escudeiro P, Duarte V, Lino J (2019). Inclusion through digital arts: Creating a community of practice. In: Multi Conference on Computer Science and Information Systems, MCCSIS 2019-Proceedings of the International Conferences on ICT, Society and Human Beings, (V Kommers, RD Peng, eds.). 145–152.
Barnett R, Parry G, Coate K (2001). Conceptualising curriculum change. Teaching in Higher Education, 6(4): 435–449. https://doi.org/10.1080/13562510120078009
Baumer B (2015). A data science course for undergraduates: Thinking with data. The American Statistician, 69(4): 334–342. https://doi.org/10.1080/00031305.2015.1081105
Canelón S, Hare E (2021). Revealing room for improvement in accessibility within a social media data visualization community. https://www.youtube.com/watch?v=DxLkv2iRdf8&ab_channel=csvconf
Cesal A (2020). Writing alt text for data visualization. https://medium.com/nightingale/writing-alt-text-for-data-visualization-2a218ef43f81
Chang W, Cheng J, Allaire JJ, Sievert C, Schloerke B, Xie Y, et al. (2022). Shiny: Web Application Framework for r. https://shiny.rstudio.com/
Danyluk A, Leidig P, Bari A, Buck S, Cassel L, Doyle Keegan Hines M, et al. (2021). Computing competencies for undergraduate data science curricula. https://doi.org/10.1145/3453538
Godfrey AJR, Warren D, Murrell P, Bilton T, Sorge V (2021). BrailleR: Improved Access for Blind Users. https://github.com/ajrgodfrey/BrailleR
Horst A, Hill A, Gorman K (2022). Palmerpenguins: Palmer Archipelago (Antarctica) Penguin Data. https://CRAN.R-project.org/package=palmerpenguins
Kawas S, Vonessen L, Ko AJ (2019). Teaching accessibility: A design exploration of faculty professional development at scale. In: Proceedings of the 50th ACM Technical Symposium on Computer Science Education, SIGCSE ’19, 983–989. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3287324.3287399
Kim NW, Joyner SC, Riegelhuth A, Kim Y (2021). Accessible visualization: Design space, opportunities, and challenges. Computer Graphics Forum, 40(3): 173–188. https://doi.org/10.1111/cgf.14298
Kirk D, MacDonald D (2001). Teacher voice and ownership of curriculum change. Journal of Curriculum Studies, 33(5): 551–567. https://doi.org/10.1080/00220270010016874
Ko AJ, Ladner RE (2016). AccessComputing promotes teaching accessibility. ACM Inroads, 7(4): 65–68. https://doi.org/10.1145/2968453
Lee B, Choe EK, Isenberg P, Marriott K, Stasko J, Rhyne T-M (2020). Reaching broader audiences with data visualization. IEEE Computer Graphics and Applications, 40(2): 82–90. https://doi.org/10.1109/MCG.2020.2968244
LinkedIn (2020). Emerging jobs report. https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf
Ludi S (2002). Access for everyone: Introducing accessibility issues to students in Internet programming courses. In: 32nd Annual Frontiers in Education, 3:S1C. https://doi.org/10.1109/FIE.2002.1158617
Ludi S (2007). Introducing accessibility requirements through external stakeholder utilization in an undergraduate requirements engineering course. In: Proceedings of the 29th International Conference on Software Engineering, ICSE ’07, 736–743. IEEE Computer Society, USA. https://doi.org/10.1109/ICSE.2007.46
Lundgard A, Satyanarayan A (2022). Accessible visualization via natural language descriptions: A four-level model of semantic content. IEEE Transactions on Visualization and Computer Graphics, 28(1): 1073–1083. https://doi.org/10.1109/TVCG.2021.3114770
MacFarlane J ([2010] 2022). Pandoc. https://github.com/jgm/pandoc
Marriott K, Lee B, Butler M, Cutrell E, Ellis K, Goncu C, et al. (2021). Inclusive data visualization for people with disabilities: A call to action. Interactions, 28(3): 47–51. https://doi.org/10.1145/3457875
Martin-Escalona I, Barcelo-Arroyo F, Zola E (2013). The introduction of a topic on accessibility in several engineering degrees. In: 2013 IEEE Global Engineering Education Conference (EDUCON), 656–663. https://doi.org/10.1109/EduCon.2013.6530177
McWhite CD, Wilke CO (2022). Colorblindr: Simulate Colorblindness in r Figures. https://github.com/clauswilke/colorblindr
Okabe M, Ito K (2008). Color Universal Design (CUD) – How to make figures and presentations that are friendly to colorblind people –. https://jfly.uni-koeln.de/color/
Putnam C, Dahman M, Rose E, Cheng J, Bradford G (2015). Teaching accessibility, learning empathy. In: Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility, ASSETS ’15, 333–334. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2700648.2811365
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Rosmaita BJ (2006). Accessibility first! A new approach to web design. ACM SIGCSE Bulletin, 38(1): 270–274. https://doi.org/10.1145/1124706.1121426
Schwab-McCoy A, Baker CM, Gasper RE (2021). Data science in 2020: Computing, curricula, and challenges for the next 10 years. Journal of Statistics and Data Science Education, 29(sup1): S40–S50. https://doi.org/10.1080/10691898.2020.1851159
Seo J (2022). tactileR: Converting r Graphics into a Braille Ready-to-Print PDF. https://github.com/jooyoungseo/tactileR
Shinohara K, Bennett CL, Wobbrock JO (2016). How designing for people with and without disabilities shapes student design thinking. In: Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS ’16, 229–237. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2982142.2982158
Shinohara K, Bennett CL, Wobbrock JO, Pratt W (2017). Teaching accessibility in a technology design course. In: Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) (V Smith, F Borge, F Mercier, RD Lim, eds.). Philadelphia, PA: International Society of the Learning Sciences.
Shinohara K, Kawas S, Ko AJ, Ladner RE (2018). Who teaches accessibility?: A survey of U.S. computing faculty. In: Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 197–202. ACM, Baltimore Maryland USA. https://doi.org/10.1145/3159450.3159484
Siegert S, Williams R (2017). Sonify: Data Sonification – Turning Data into Sound. https://CRAN.R-project.org/package=sonify
The Partnership on Employment & Accessible Technology (2018). The Accessible Technology Skills Gap. https://www.peatworks.org/infographic-the-accessible-technology-skills-gap
Yan D, Davis GE (2019). A first course in data science. Journal of Statistics Education, 27(2): 99–109. https://doi.org/10.1080/10691898.2019.1623136
