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.
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.
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.
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.
Content should be clear and concise.
Avoid using jargon and keep text content simple.
Use specific language: Instead of “click here” use “click next.”
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Learners should be able to access course materials with reasonably outdated software.
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.
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.
Use simple, bold fonts. Non-serif styles are especially dyslexic-friendly.
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.
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).
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.
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.
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!
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.
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.
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.
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!
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.
At some point, people interested in data analysis have to, unfortunately, learn the math aspect of data handling. Statistics can be complex, but this volume makes it approachable to those of us who cried during high school Stats class.
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!
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.
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.
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.
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.
Nothing will awe non-datatists like a mastery of pivot tables. This 5 hour guide will take true Excel Beginners and turn them into Pivot Table masters.
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.
As the title promises, this introduction to data science is geared towards those of us who have a fear of math. It is highly approachable and intuitively organized to give true data babies the foundation they need to move forward in learning data analytics.
This practical, step by step guide starts users in the field of data analytics with publicly available data and Excel spreadsheets. Herzog provides a gentle yet useful start to data science which can give users the confidence to dive deeper.
Be sure to check back here next week for our next installment, “What’s Math got to do with it?”!
Wikipedia is a central player in online knowledge production and sharing. Since its founding in 2001, Wikipedia has been committed to open access and open editing, which has made it the most popular reference work on the web. Though students are still warned away from using Wikipedia as a source in their scholarship, it presents well-researched information in an accessible and ostensibly democratic way.
Most people know Wikipedia from its high ranking in most internet searches and tend to use it for its encyclopedic value. The Wikimedia Foundation—which runs Wikipedia—has several other projects which seek to provide free access to knowledge. Among those are Wikimedia Commons, which offers free photos; Wikiversity, which offers free educational materials; and Wikidata, which provides structured data to support the other wikis.
Wikidata provides structured data to support Wikimedia and other Wikimedia Foundation projects
Wikidata is a great tool to study how Wikipedia is structured and what information is available through the online encyclopedia. Since it is presented as structured data, it can be analyze quantitatively more easily than Wikipedia articles. This has led to many projects that allow users to explore data through visualizations, queries, and other means. Wikidata offers a page of Tools that can be used to analyze Wikidata more quickly and efficiently, as well as Data Access instructions for how to use data from the site.
The home page for the Wikidata Human Gender Indicators project
An example of a project born out of Wikidata is the Wikidata Human Gender Indicators (WHGI) project. The project uses metadata from Wikidata entries about people to analyze trends in gender disparity over time and across cultures. The project presents the raw data for download, as well as charts and an article written about the discoveries the researchers made while compiling the data. Some of the visualizations they present are confusing (perhaps they could benefit from reading our Lightning Review of Data Visualization for Success), but they succeed in conveying important trends that reveal a bias toward articles about men, as well as an interesting phenomenon surrounding celebrities. Some regions will have a better ratio of women to men biographies due to many articles being written about actresses and female musicians, which reflects cultural differences surrounding fame and gender.
Of course, like many data sources, Wikidata is not perfect. The creators of the WHGI project frequently discovered that articles did not have complete metadata related to gender or nationality, which greatly influenced their ability to analyze the trends present on Wikipedia related to those areas. Since Wikipedia and Wikidata are open to editing by anyone and are governed by practices that the community has agreed upon, it is important for Wikipedians to consider including more metadata in their articles so that researchers can use that data in new and exciting ways.
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.”
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