By Shalin Hai-Jew, Kansas State University 

 

Figure 1:  The Open-Source UDOIT Accessibility Scan Tool in Canvas LMS at K-State 

Those teaching online have long struggled with federal guidelines requiring that the courses that they teach be accessible.  What this usually meant was a difficult review of the online learning contents and the necessary retrofitting work (or removal of the inaccessible digital objects).  

The idea is to provide multi-modal approaches to all learning contents to reach the widest range of learners based on perception and symbolic processing (like reading).  Further accessibility aspects may include designing for diversity and inclusiveness—by considering learning preferences, culture, language, and other aspects of the learning.  A simpler version is available on Adobe Spark.

UDOIT (Universal Design Online content Inspection Tool) is an open-source and free software that is an add-on to the Canvas LMS (Instructure).  This was created by Jacob Bates (project lead), Eric Colon, Fenel Joseph, and Emily Sachs, at the University of Central Florida from 2014 – 2015.  It was released to the UCF faculty in May 2015 and to the general public in June 2015 (Swenson, 2015).  This tool enables an automated scan of all or any combination of the following:  announcements, assignments, discussions, files, pages, syllabus, and module URLs.  According to the in-tool documentation, the software is designed to identify the following:  

Beyond those main focuses, there are suggestions in the in-software documentation:  

To run UDOIT, one generally has to find it in the left menu.  Select the desired content to be scanned.  (The default is “all.”)  Then, click “Run scanner.”  Do not navigate away from the page while the scan is being achieved.  Once the scan is done, there is a short report about what the system found…and then it is time to use the UFIXIT.   

 

Figure 2:  Calling out Internal Features of Canvas LMS  

One early and unwelcome surprise is that various elements of the Canvas LMS itself have been called out as being inaccessible.  Certain glyphs and buttons have insufficient or no alt texting.  This is not something that is particularly helpful to those teaching online because this means that the system itself is not considered accessible.  It would be easier to have the system set to be accessible…and the UDOIT set to not deal with the innards of the Canvas LMS.  

Also, at least in our local install, the PDF report printing does not work.  

Ideally, the UDOIT would provide clear analysis and a one-stop place to fix the issues.  After all, there can be a master course created that can be used again and again…as long as it is to quality and updated.  

Another shortcoming is that the updates are to Canvas contents. While the courses are easy to download and re-upload and to transfer, if the learning objects are used in other contexts, they will not necessarily have the included updates.  For example, if there is work on alt-texting a PowerPoint slideshow, the original PowerPoint would benefit by having the alt-texting available at the image level or the slide level. Inside the LMS is the HTML version of the slideshow, and any changes are made to the HTML code there.  Ideally, accessibility should ride with the lowest common denominator, at the object level. 

It is important to note that UDOIT does not capture whether the writing is in simple and clear English.  It does not capture information accuracy.  It does not capture whether videos hosted elsewhere have transcription.  It has no capability to ensure that live events are accessible.    

It is important to note that the developers of the tool and the open-source developers who’ve come alongside them are working on other features.  There is work on an “updated color picker,” more UFIXIT features, reporting statistics, and others (Swenson, 2015).   

In terms of an instructional design point-of-view, it is better to build accessibility in at the beginning, from the planning phases onwards.  It helps to budget the time and expertise for accessibility work (whether the model is a universal design one or not).  It is critical to develop accessible learning—alt texting images, auto-transcribing videos (with Google’s YouTube and downloading the .srt files), and so on—from the start…and head off the potential cost of retrofitting later.  Often, when accessibility is a requirement early on, this does not feel like extra work.  

Having the ability to find relevant issues within a Canvas course…and being able to address them intelligently is important.  In the current state, though, UDOIT would require a fair amount more work to make it sufficiently  nuanced and finessed to be relevant in this critical endeavor.  This tool may benefit from feedback from users, so the tool does not evolve in isolation.  Some of the accessibility settings seem somewhat arbitrary (3,000 words per page only, alt text at 100 words minimum?).  

The acronym for this tool is well conceptualized, with the tone understood as an encouraging one, full of self-efficacy and get-up-and-go!  However, UDOIT can also be read as:  “I don’t feel like it.  You do it!”  Ideally, this tool should lean towards the first tone and not the latter.  Right now, based on functionalities alone, it may sound more like the latter.  

Swenson, N. (2015).  UDOIT – Instructure Canvas Course Accessibility Checker.  University of Central Florida – Center for Distributed Learning.  http://accessinghigherground.org/wp/wp-content/uploads/2015/05/AHG_2015_Swenson_UDOIT_virtual_thurs.pdf.    

UDOIT.  (2017).  GitHub.  https://github.com/ucfopen/UDOIT.  

UDOIT Canvas Community. (2017).  https://community.canvaslms.com/ideas/3651.  

Shalin Hai-Jew works as an instructional designer at Kansas State University.  Her email is shalin@ksu.edu.