The document discusses the importance of accessible learning analytics for students with disabilities, emphasizing a social model that focuses on societal factors rather than individual deficits. It highlights the need for tools to be designed inclusively, considering color blindness, layout, and the reduction of cognitive load to enhance usability. Overall, it advocates for the adaptation of tools to ensure that all learners can effectively engage with learning analytics.

1. Rebecca Ferguson
The Open University, UK
Accessible learning
Accessible analytics
Photo by Dima Pechurin on Unsplash

2. Medical model – problems are due to the individual
Photo by ahmet arslan on Unsplash

3. Social model – problems are created by society

4. We can use and adapt tools to so environments are accessible to users

5. Photo by Dorothea OLDANI on Unsplash
There is no need to take a deficit view of disabled students
With the right tools, they may be able to outperform others

6. Learning analytics accessibility
Learning analytics tools
and dashboards can be
considered accessible if
people with disabilities
can perceive,
understand, navigate,
interact with them and
contribute to them.
Based on W3C
definition of web
accessibility
Photo by Clément Falize on Unsplash

7. Colour choices
https://www.color-blindness.com/coblis-color-blindness-simulator/
Normal
Red-blind Protanopia
Blue-blind Tritanopia
Red–green color
blindness affects up to
8% of males and 0.5%
of females of Northern
European descent

8. Layout choices
Does the tool
cater for learners
who need extra
time to make
responses;
reduce cognitive
load as far as
possible; and
remove triggers of
anxiety?
Photo by Patrick Hendry on Unsplash

10. Jargon and acronyms can increase anxiety, decrease understanding
https://www.sciencegeek.net/lingo.html
Too much text, overcrowded layout confuse users

11. Cooper, M., Ferguson, R., & Wolff, A. (2016). What can analytics contribute to
accessibility in e-learning systems and to disabled students’ learning? LAK16,
Edinburgh, UK.

12. Lister, K., Coughlan, T., Iniesto, F., Freear, N., & Devine, P. (2020). Accessible
conversational user interfaces: considerations for design. Web for All 2020, 20-21
April, Taipei, Taiwan.

13. Nguyen, A., Gardner, L. A., & Sheridan, D. (2018). A framework for applying learning
analytics in serious games for people with intellectual disabilities. BJET, 49(4).