<mck> http://a11y.pro/csun2019/
<scribe> scribe: MF
mck: gave an overview of the CSUN
presentation. Discussed each slide.
... slide 19 - our initial target browser set
... side 20 - for every observed behavior, is the level
(quality) of support appropriate, missing, or incorrect?
... example: radio groups and the behavior around aria-checked.
it would be a "must have" that the state be conveyed. If JAWS
says "checked" for native radio buttons, then it should
announce the state the same way for ARIA. "appropriate",
"missing", or "incorrect" describes the quality of the
information is conveyed. For example, if the state of a
checkbox is conveyed by JAWS as "selected", the importance
would be met, but the
quality would be incorrect (should be "checked"), but at least a state was conveyed.
mck: discussed how scoring of
these factors might work. 100% would be if all of the minimal
requirements were met. You might get a higher score if "should"
or "may" assertions are met. Another approach might be to
convey an independent score for each group of assertions (one
score for "must", another another score for "should")
... we could give negative scores for assertions not being met,
or incorrect support. We could also floor to zero.
... a single failing assertion could result in a zero score
<shimizuyohta> Matt: Concern is after spending time dividing scoring rule, finding screen algorithm doesn’t represent reality.
<shimizuyohta> Michael:Needs some specific example would be needed to test and validate the scoring algorithm
<shimizuyohta> Matt:[p.23] Two important precondition are Screen reader 1.mode 2.setting 99% are a response to keyboard command
matt: discuss next steps. Wrap up exploration.
Matt and Michael: focus on applying the methodology and scoring to a simple example like a checkbox so that we can create an informed data model
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