Plain language (AAA)

Plain language:  Allow the user to use plain language and provide clear and simple language in all content so that all of the following are true:

 

 

Exceptions:

What Principle and Guideline the SC falls within.

Under WCAG 3.1

Suggestion for Priority Level

AAA or AA depending on whether technology can ensure that this is reasonable via alternative content.

Related Glossary additions or changes

concrete wording
concrete wording uses literal language, is specific and describes things you experience through your senses: smoke, mist, a shout.
 
 word frequencies
word frequency are lists of a language's words grouped by frequency of occurrence within some given text corpus. Word lists should also give the meaning of the usage
 
non-literal language
non-literal is language that uses words or expressions with a meaning that is different from the literal interpretation. Figurative language includes, but is not limited to, metaphor, sarcasm, simile, personification, hyperbole, symbolism, idioms, and cliché. For example:
 

 

Description

The intent of this Success Criterion is to ensure people can understand and use all content. For language to be usable by many people with cognitive disabilities it needs to be written in plain, clear language. This is an important accessibility principle.

It should be noted that the exceptions ensure it is widely applicable.

 

Benefits

This success criterion supports those who have reading difficulties, language disabilities, and some visual perceptual difficulties. It can include people with intellectual disabilities, receptive aphasia, and/or acquired dyslexia, as well as those with general cognitive learning disabilities. This supports those who have dementia, and/or acquire cognitive disabilities as they age.

Related Resources

Stroke Association Accessible Information Guidelines http://www.stroke.org.uk/professionals/accessible-information-guidelines

Computers helping people with special needs, 14 international conference ICCHP 2014 Eds. Miesenberger, Fels, Archambault, et al. Springer (pages 401). Paper: Never Too Old to Use a Tablet, L. Muskens, et al. pages 392 - 393.


[i.49]    Vogindroukas, I. & Zikopoulou, O. (2011). Idiom understanding in people with Asperger syndrome/high functioning autism. Rev. soc. bras. fonoaudiol. Vol.16, n.4, pp.390-395.
NOTE:    Available at http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-80342011000400005&lng=en&nrm=iso .
[i.50]    Oi, M., Tanaka, S. & Ohoka, H. (2013). The Relationship between Comprehension of Figurative Language by Japanese Children with High Functioning Autism Spectrum Disorders and College Freshmen's Assessment of Its Conventionality of Usage, Autism Research and Treatment, vol. 2013, Article ID 480635, 7 pages, 2013. doi:10.1155/2013/480635.
NOTE:    Available at http://www.hindawi.com/journals/aurt/2013/480635 /.
[i.51]    de Villiers, P. A. et al. (2011). Non-Literal Language and Theory of Mind in Autism Spectrum Disorders. Poster presented at the ASHA Convention, San Diego.
NOTE:    Available at http://www.asha.org/Events/convention/handouts/2011/de-Villiers-de-Villiers-Diaz-Cheung-Alig-Raditz-Paul/ .
[i.52]    Norbury, C. F. (2005). The relationship between theory of mind and metaphor: Evidence from children with language impairment and autistic spectrum disorder.; Oxford Study of Children's Communication Impairments, University of Oxford, UK; British Journal of Developmental Psychology, 23, 383-39.
NOTE:      Available at http://www.pc.rhul.ac.uk/sites/lilac/new_site/wp-content/uploads/2010/04/metaphor.pdf.

[i.53]     Language and Understanding Minds: Connections in Autism; Helen Tager-Flusberg, Ph.D; Chapter for: S. Baron-Cohen, H. Tager-Flusberg, & D. J. Cohen (Eds.), Understanding other minds: Perspectives from autism and developmental cognitive neuroscience. Second Edition. Oxford: Oxford University Press.

NOTE:      Available at http://www.ucd.ie/artspgs/langimp/TAG2.pdf.

 

Phiriyapkanon. Is big button interface enough for elderly users, P34, Malardardalen University Press Sweden 2011.

Neilson-aging

Top Five Instructional Tips for Students with Down Syndrome"http://specialedpost.org/2013/01/31/top-five-instructional-strategies-for-students-with-down-syndrome/

http://www.autism.org.uk/working-with/autism-friendly-places/designing-websites-suitable-for-people-with-autism-spectrum-disorders.aspx (downloaded 08/2015)

Students with Down Syndrome, http://www.downssa.asn.au/__files/f/3203/A%20Student%20with%20Down%20Syndrome%202014.pdf

 

Task force links

Issue papers

 

Testability

The success criterion is testable if each of the bullet points are testable. If all the content fails any bullet point, it is not conformant to this success criterion. If it passes all of the bullet points, it is conformant.

Bullet points:

Tense and voice are objective, and hence are verifiable. Also, it is expected that natural language processing algorithms will be able to confirm this automatically with reasonable accuracy.

Testing for exceptions:

If present tense and active voice have not been used, the tester will need to confirm if one of the exceptions is relevant. If an exception is not relevant, and present tense and active voice have not been used, then the content fails this success criterion.

Even languages with a small number of users have published lists of the most frequent words (such as Hebrew). If there is a natural language that does not have one, algorithms exist that calculate these lists for a language, or for specific contexts. Testing content against these word lists can be done manually. However, it is expected there will be a natural language processing testing tool by the time this goes to CR. (It is already integrated into a tool by IBM.)

Testing for exceptions is as discussed above.

Use of double negatives is a fact, and hence is verifiable. It is assumed a natural language processing tool will also test for this. Testing for exceptions is as discussed above.

 

Non-literal text and metaphors can be identified when the meaning of the sentence is something other than the meaning of the individual words. This is human testable. Cognitive computing algorithms can test for this as well.

If the text is not literal, then the tester must confirm that personalization and an easy user setting enables it to be replaced, such that all meaning is retained.

 

Techniques