“Diversity is where everyone is invited to the party. Equity means that everyone gets to contribute to the playlist. And inclusion means that everyone has the opportunity to dance.”Defining DEI, University of Michigan
We all view the world through specific lenses, and those lenses change over time. Based on work I’ve been doing with Peralta Community College District over the past two years, equity 1If you’re not clear on the definition, Peralta uses the term equity to mean “freedom from bias or assumptions that negatively impact online learners’ motivations, opportunities, or accomplishments.” See more details at https://web.peralta.edu/de/peralta-online-equity-initiative/equity/ is one of my lenses. When searching the the recent Educause Conference (#EDU19) schedule, I was pleasantly surprised to find several sessions that used the word “equity” in the title or description – 3 breakout sessions and 5 repeating poster sessions on Tuesday, 1 featured session and 3 breakout sessions on Wednesday, and 2 breakout sessions on Thursday.2NOTE to the #EDU19 organizers: With so few breakout sessions devoted to equity topics, it was disappointing that several of them were scheduled at the same time as another. While it was a pretty small percentage of the overall program–I counted over 90 breakout sessions on Tuesday alone–hopefully that percentage will grow over time.3The number of sessions did increase a little if you also searched for “diversity” and “inclusion.”
In our October 21 post, “Educause 2019: Multiple views into educational technology conference,” Phil Hill and I described a heavy emphasis on data and analytics at this year’s conference. Interestingly, when viewed side-by-side two institutions from Michigan presented an emerging dichotomy related to how using data affects equity and inclusion. A breakout session described data-driven decision-making to increase inclusion, while a featured session warned against (mis)using data in ways that decrease equity. More interestingly, both presentations’ different perspectives made sense.
A Tale of Two Sessions
The breakout session about data-driven decision-making was standing-room-only–no surprise for a conference serving over 8,000 people, a quarter of whom were first-time attendees. I was able to catch some of the presentation while standing in the hallway at the back of a long room. As the first of three presenters to speak in this session, a representative from Grand Valley State University (Allendale, MI) described how that school uses data from Blackboard’s Ally tool to determine how many learning objects or materials need to be or have been remediated after identifying accessibility issues. They are just getting started, but the data fosters an awareness of just how many learning objects are inaccessible to some extent. Accessibility accommodations for people with disabilities comprise a key set of equity-based practices.
At the human level, I’ve taught online teaching workshops that provide accessibility strategies. Faculty can get overwhelmed by the facts that a) their materials are not accessible and b) they may have to fix between 50 and 100 files for any given course that they teach. To their credit the Educause conference organizers provide “Accessibility Information for Presenters,” which contributes to the ongoing awareness campaign.
The featured session by Chris Gilliard from Macomb Community College (Detroit, MI) was my favorite session at the conference this year. EdSurge has already posted an article outlining Gilliard’s session about “Digital Redlining,” summing up the session’s premise in the first sentence:
“Colleges are increasingly using Big Data to monitor students, control their access to information and set them on learning paths they may not have chosen.”
I encourage you to go read the entire article. The author, Rebecca Koenig, paid close attention to Gilliard’s cautions about predictive analytics, his challenges to the joint Educause-AIR-NACUBO publication that states “Analytics can save higher education,” and his issues with the use of digital surveillance. Rather than cover the same ground, I’ll share some practical takeaways.
The Old Takeaway Shop
One, just as historical policies and practices enforced redlining to discriminate against certain populations in a community or housing context, higher ed policies and practices can cause digital redlining that discriminates against certain populations in an educational context. Here are two examples Gilliard presented:
- IT units should consider how campus-wide Internet filters may block learners from accessing otherwise public content. This has a disproportionate impact on learners who do not have broadband connections at home.
- Given limited funding, libraries should include equity-related criteria when selecting the resources (e.g., electronic journals) they purchase and provide.
And two, just as faculty need to gain awareness of accessibility issues, educational technologists and campus leaders need to gain awareness of potential equity issues, especially when data is involved. National organizations are doing their part–here are just three examples:
- The EDUCAUSE Board and leadership have established diversity, equity, and inclusion (DEI) as a critical priority for the association. At #EDU19 the Educause CEO also addressed “The State of Digital Ethics in 2019,” prescribing both caution and hope.
- The Association for Authentic, Experiential, and Evidence-Based Learning (AAEEBL)4Disclosure: Kevin is currently a member of the AAEEBL Board of Directors.
has created a year-long Community of Inquiry related to digital ethics and ed tech, starting with a global Twitter Chat, “Defining digital ethics and ePortfolios.”
- The Association of American Colleges & Universities (AAC&U) has an entire conference on Diversity, Equity and Student Success.
While the conversations around data and analytics started years ago, the conversations that extend to equity and inclusion are just getting started. The two Educause sessions highlighted here present different scenarios. One involves leveraging data to decrease discrimination, while the other advises approaching data projects with open eyes. Paraphrasing Gilliard, “Do not say your work is like Netflix or Amazon or Google for education. It doesn’t mean what you think it means.”
As campuses begin data-driven decision-making or learner analytics initiatives, those efforts can be made a) more diverse and inclusive by inviting a wide range of different people to participate and by welcoming their perspectives, and b) more equitable by collectively checking for bias or assumptions that could negatively impact learners. The EdSurge article about Gilliard’s conference session made note of recent move in this direction:
“There were signs throughout the conference that educators are trying to strike a better balance with their data practices. …The chief information officer at Dartmouth College, Mitchel Davis, explained how he is working to rewrite campus contracts about what tech vendors can do with student data. At UT Dallas, …administrators collaborated with student government to better inform students about how their data may be collected.”
At MindWires, we bear the same responsibility. As we engage in various research efforts, we need to expand the circle of conversation to include people who can help us check our own biases and assumptions. As I tell my students who are learning how to improve their learning, becoming aware is just the first step in the process. You have to follow that with intentional action, and close the loop with reflection on what worked and what needs further adjustment. Similarly, IT professionals who become aware of equity issues, must make it more than a good idea. They have to apply equity principles to their data practices. To that end, keep an eye out for the development and publication of tools (e.g., rubrics), exemplars, or guidelines to support campuses that are ready to engage in that intentional action–collecting, analyzing and using data in equitable ways.
- If you’re not clear on the definition, Peralta uses the term equity to mean “freedom from bias or assumptions that negatively impact online learners’ motivations, opportunities, or accomplishments.” See more details at https://web.peralta.edu/de/peralta-online-equity-initiative/equity/
- NOTE to the #EDU19 organizers: With so few breakout sessions devoted to equity topics, it was disappointing that several of them were scheduled at the same time as another.
- The number of #EDU19 sessions did increase a little if you also searched for “diversity” and “inclusion.”
- Disclosure: Kevin is currently a member of the AAEEBL Board of Directors.