Images and data visualization
At a minimum, ensure that all images in your content include alt-text or a descriptive caption. The terms “image description” and “alt-text” are sometimes used interchangeably, but “alt-text” refers to a tag that screen reading software can interpret, and which users can select/view; “image description” refers to captions added below an image, or descriptions provided elsewhere in the written content. When generating alt text or image descriptions for course content, make sure you include any elements of the image which students need for understanding, evaluation, or interpretation.
Images that are “decorative,” like illustrations, can often be described briefly. Images that are repetitive and do not directly convey meaning, like chapter headings or clip art, can be omitted. Images that contain important content will need full descriptions to ensure students get the information that they need. A helpful resource is Accessible Publishing’s guide to image descriptions for academic work.
Tables and lists
Complex tables can be difficult for screen readers to parse – but many users find complex tables difficult to use, particularly if they span multiple pages or have multiple lines of text in each cell. Consider using simpler formatting, where feasible: lists with headings can be easier for all readers to use.
When providing data that students need for assignments or evaluations, provide the full data file, where possible.
Charts and graphs
Sometimes complex images are required to fully convey your content. The best way to make such images accessible can vary – ideally, try to provide the information in several different formats, where possible. For example, include a detailed caption which highlights important trends in the data your graph shows, while also ensuring your graph meets colour accessibility guidelines, omits any extraneous data, uses clear and legible labels/text, and that users can zoom in/enlarge the image.
Data visualizations
A great introductory explainer is available here:
https://material.io/blog/data-visualization-accessibility
If you’re looking for something more in-depth, the University of Wisconsin-Madison has a detailed guide on accessible data visualizations.