Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses

Abstract

Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. Thus, it is critical that, as statistics and data science educators of the next generation, we center accessibility and inclusion throughout our curriculum, classroom environment, modes of assessment, course materials, and more. Though some common strategies apply across these areas, this article focuses on providing a framework for developing accessible and inclusive course materials (e.g., in-class activities, course manuals, lecture slides, etc.), with examples drawn from our experience co-writing a statistics textbook. In turn, this framework establishes a structure for holding ourselves accountable to these principles.

KEYWORDS:

• Accessibility
• Curriculum
• Inclusion
• Textbooks

Supplementary Materials

We provide the R session information used writing this manuscript.

Notes

1 The authors requested this feature through a GitHub issue.

2 https://mathematicallygiftedandblack.com/

3 https://www.sacnas.org/

4 https://community.amstat.org/cmis/home

5 https://community.amstat.org/cowis/home

6 http://www.datascijedi.org/

7 The Google Form can be found at https://bit.ly/bayesrules-feedback.