Strategies for Accessible E-Learning

Why Teach Accessible Classes?

No matter if you’re teaching a full semester class or a one-off workshop, you will be teaching disabled learners, whether they disclose their disabilities or not. All of your students deserve an equitable learning experience and accessible practices eliminate barriers for all users.

man using a braille keyboard writer
Image by Sigmund on Unsplash

Web Content Accessibility Guidelines (WCAG)

One of the most important standards when it comes to accessibility on the web is the Web Content Accessibility Guidelines. These guidelines are split into four components: Perceivable, Operable, Understandable, and Robust, or POUR.

Perceivable

  • Everyone can identify your content not matter how they perceive information.
    • Use text, audio, and video alternatives for content.
    • Make your lessons adaptable for different student needs.

Operable

  • Learners should be able to navigate your course with ease.
    • Have large and obvious navigation buttons.
    • Give enough time or eliminate timed progression counters.
    • Make your content keyboard navigable.

Understandable

  • Content should be clear and concise.
    • Avoid using jargon and keep text content simple.
    • Use specific language: Instead of “click here” use “click next.”

Robust

  • Content can be accessed by assistive technologies (such as screen readers).
    • Make sure your content is compatible with assistive technology.
    • Update any dead links or finicky buttons.
    • Learners should be able to access course materials with reasonably outdated software.
a teacher with a student, pointing to something on a laptop
Image by Desola Lanre-Ologun on Unsplash

Best Practices

Now that I’ve gone over the basic web accessibility standards, here are some practical tips that use can use to make your class materials more accessible.

Course Structure

  • You want your course structure to be easily digestible, so break up lessons into manageable chunks.
  • Asynchronous courses are courses that allow learners to complete work and attend lectures at their own pace. You may want to consider some form of this to allow your students flexibility.

Text and Links

  • Headings and titles should be formatted properly. Instead of just bolding your text, use headings in numerical order. In Word, you can accomplish this by selecting Home > Styles and selecting the heading you want.
heading one and heading two in the Word styles interface

Images

  • Always include alt-text with your images. There will be different ways of doing this in different programs. Alt-text describes the image for users who cannot see it. For instance, in the alt-text I describe the image below as “a beagle with its tongue out.”
  • If the image is purely decorative, you can set it as such.
a beagle with its tongue out
Image by Milli on Unsplash

Videos

  • Videos should have error-free captioning. It can be useful to include a written transcription.
  • Video interfaces should be navigable using a keyboard (spacebar to start and stop).

Tables

  • Avoid using tables if you can, they can be challenging for screen readers to decipher.
  • Tables can be made accessible with proper web design. For a instructions on how to create accessible tables visit WebAIM’s Accessible Tables Guide.

Color Contrast

examples of good and bad color contrast
Image made with dopelycolors
  • Make sure that your content is readable, whatever colors you use. Avoid going wild: dark text on light backgrounds and light text on dark backgrounds are standard.
  • If you want to check your color contrast, try the WCAG Color Contrast Checker.
  • Avoid providing information that solely relies on the student being able to distinguish color i.e. red meaning “stop” and green meaning “go.”

Resources to Learn More

When it comes to accessible practices, there’s a lot of information to cover. If you want to learn more, here are some resources to get you started.

By working to make your classes accessible, you can create a better learning experience for all your students.

Learn Data Analysis: What’s Math Got to do With It?

What’s math got to do with data analysis? Unfortunately, for those of us who are chronic humanities people, math has a lot to do with it. This might seem like a daunting barrier, especially if the last time you looked at a math problem was in a high school algebra class. This is also true for learners who are already skilled with the technological aspect of data analysis but are not familiar with the mathematics side of thing. However, there are so many resources available to help self-directed students learn the basics and get up to speed for the purposes of data analytics! Using the resource platforms described in last week’s blog post, these resources will have even chronic humanities people playing with numbers in no time!  

LinkedIn Learning 

  • Learning Everyday Math 
    • Look, some of us did not absorb or retain the basic math lessons of our early education. That’s okay! This is a no-judgment zone, and this 2 hour course will help users learn how to calculate percentages for tips and taxes, compare prices while shopping, find the area and volume for home-improvement projects, and learn the basics of probability.  
  • Become a Data Scientist 
    • This 21 hour Learning Path is made up of 12 courses that focus more on the statistical side of data analysis than the technical steps of the process. This course is more geared toward users with experience in IT and computers, so it is not the best for people who do not have a strong technical background. However, for those who are familiar with computer science and want to pivot into data analytics, this is an ideal curriculum.   

O’Reilly Books and Videos (Make sure to follow these instructions for logging in!)

  • Essential Math for Data Science 
    • This eBook mixes basic coding skills with math lessons to cover the essential analytical skills needed for data science work. Relevant aspects of calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks are covered in plain English. The chapters include exercises with answers for self-assessment as well as career advice for budding data analysts.   
  • Statistics for Data Science using Python 
    • Besides books, O’Reilly also has expert curated playlists that consist of chapters of several different books, videos and more. This is a great way of getting the most out of several resources to focus on a single skill. This playlist covers the essential statistic concepts found in 11 different resources. Learn about Normal distribution, hypothesis tests, p-values, central limit theorem and more without having to dig for the resources yourself!  
  •   Data Science 101: Methodology, Python, and Essential Math 
    • On top of books and playlists, O’Reilly also has video-based courses. This course covers a lot of data analytics basics, but those who want to focus on the math aspect will benefit from Chapters 15-19. These chapters cover linear algebra, mathematical structures, probability, random variables and multiple variables, and statistical inference.  

In the Catalog 

Be sure to come back next week for the thrilling continuation with “*hacker voice* I’m In: Coding and Software for Data Analysis! 

Learn Data Analysis Over the Winter Break!

In the last twenty years, humanity has become super proficient in collecting data. Therefore, It is no surprise that the skills to analyze that massive collection of data is in ever increasing demand on the job market. For those of us who are worried about future job prospects, learning these in-demand data analysis skills seems like a logical next step, even if they do not fit into our current degree program. Fortunately, the university has a plethora of self-guided resources available for students looking to build their data skills. What better time to use these resources than during the long winter break?! Over the next few weeks, this blog will delve into the available resources that cover the three main skill areas of data analysis: math, coding and software, and visualization. 

Before diving into those areas, it is wise briefly look at the foundations of data analysis as well as the resources that will be showcased this month. Take this week to get acquainted with these different resource platforms and learn a few starting skills! 

LinkedIn Learning

All UIUC students have access to LinkedIn Learning. Simply login with your NetID credentials, just be sure you are logging into LinkedIn Learning, not the main LinkedIn site.  You will have access to a whole trove of high-quality videos and courses designed to help you learn career-building skills. Not only are the videos professional grade, but they often have accompanying exercise files, learning groups, certificates and exams. The collection ranging from short 5-15 minute videos that teach specific function or skills to dozen hours long courses that are designed to give a comprehensive foundation. The best part of using LinkedIn Learning is that the course and certificates completed here are then displayed on personal LinkedIn pages, showing potential employers that users have the skills they are looking for. 

  • Data Analytics for Students
    • This course is for the true data analytics babies out there. This introduction gives users the basic understanding of what data analytics is, the skills users will need to be successful,  the software and tools common in the field and what careers in data analytics look like. This 1 hour course is well worth the time for those who aren’t sure where to start their data journey.
  • Career Essentials in Data Analysis by Microsoft and LinkedIn
    • Discover the skills needed for a career in data analysis. Learn foundational concepts used in data analysis and practice using software tools for data analytics and data visualization. This is a Learning Path made up of 3 different courses that has about 9 hours of content for students to work through on their own schedule. The courses have exams for self-evaluation as well as a final exam that earns users a professional certificate. 
  • Excel: Managing and Analyzing Data
    • We have all put “proficient in Excel” on a resume, but wouldn’t it be nice if that was actually true? Unlike other data analytics courses, this course focuses on one program that most modern users are already familiar with but do not truly harness the power of. This is ideal for baby data analysts as it doesn’t bombard learners with a whole new software ecosystem but still teaches the transferable skills all data analysts use. Running at just under 4 hours, this course efficiently and comprehensively teaches users impressive data analytics skills. 

O’Reilly Books and Videos

This is a lesser known resource available at UIUC but it has some great online books and videos that tend to focus on the scientific and technical fields. Logging in is not straightforward, unfortunately. The best way to get there is to go to the Library Catalog’s record for a book offered through O’Reilly (Like this book on Python) and then follow the instructions on this LibGuide to log in. Once you are in, you will see a sizable collection of e-books and courses. The materials skew towards the more experienced users, but there are a few resources that will help baby data folks really develop their skills. 

Library Catalog

Learn data science the old fashion way, with books! There are a lot of books available at UIUC libraries for students who want to teach themselves a new skill. Here are a few choices for people looking for an easy introduction to data analysis. The Scholarly Commons collection is easily accessible and found just to the right of the main entrance to the stacks. 

Be sure to check back here next week for our next installment, “What’s Math got to do with it?”!

Spotlight: Open Culture

The Open Culture logo.

The Internet is the world’s hub for culture. You can find anything and everything from high-definition scans of sixteenth-century art to pixel drawings created yesterday. However, actually finding that content — and knowing which content you are free to use and peruse — can prove a difficult task to many. That’s why Open Culture has made it its mission to “bring together high-quality cultural & entertainment media for the worldwide lifelong learning community.”

Run by Lead Editor Dan Colman, director & associate dean of Stanford’s Continuing Studies Program, Open Culture finds cultural resources that include online courses, taped lectures, movies, language lessons, recordings, book lists, syllabi, eBooks, audio books, text books, K-12 resources, art and art images, music and writing tips, among many other resources. The website itself does not host any of the content; rather, Colman and his team scour the Internet looking for these resources, some of which may seem obvious, but also including many resources that are obscure. Posting daily, the Open Culture team writes articles ranging from “Stevie Nicks “Shows Us How to Kick Ass in High-Heeled Boots” in a 1983 Women’s Self Defense Manual” to “John F. Kennedy Explains Why Artists & Poets Are Indispensable to American Democracy (October 26th, 1963”. Open Culture finds content that is useful, whimsical, timely, or all three.

The Open Culture website itself can be a little difficult to navigate. Links to content can seem hidden in the article format of Open Culture, and the various lists on the right side of the screen are clunky and require too much scrolling. However, the content that you find on the site more than makes up for the website design