Discussion for Ch 3: Using and Communicating Data as a Tool to Advance Equity part 1

Disaggregating data to make inequities visible

Ch 3, Using and Communicating Data as a Tool to Advance Equity, of “From Equity Talk to Equity Walk” (ET2EW) posits that disaggregating data by race is an important first step in being equity-minded and addressing inequities because it allows practitioners to “see” differences in student outcomes.

Questions to ponder:
Does your institution collect/share data on retention and completion? How do they disaggregate data? For example: by race, by gender, by socioeconomic status, or first-generation status? Do they use the problematic URM (underrepresented minority) designation? How do they share this information?

Equity-Minded Sensemaking

Data is not “self-acting” - the value of data depends on how it is used. The Center for Urban Education (CUE) describes Equity-Minded Sensemaking as

  • The process of critical reflection, contextualization, and meaning-making

And says that it

  • Goes beyond examining data and noticing equity gaps in outcomes

And involves

  • Interpreting equity gaps as a signal that practics and not working as intended and asking equity-minded questions about how and why current practices are failing to serve students experiencing inequities

Questions to ponder:
Do you agree with the idea of equity-minded sensemaking? Have you used data to start to process of examing practices at your institution?

Take a look at pages 60-62 of ET2EW for more information on Equity-Minded Sensemaking


Not being part of an institution and also not having the ET2EW book, just as an interested observer I am curious to hear responses to the question.

Based on the phrasing, it seems hard to argue against “equity-minded sensemaking”-- it’s the how we go about this that we hope we can get create some useful discussion here.

I was curious about the Center for Urban Education, so that leads me to looking it up! For those here in the EDI mix, it’s probably well known. Their Racial Equity Tools look very useful in bringing more colleagues to a place of sensemaking (and are CC licensed, yay).

Let’s hear more on sensemaking…


I’ve often heard it referred to as “telling a story with the data.” Data sets can be much more impactful if one uses them to tell a compelling story.


As a librarian working for a college serving residents with ethnic and socioeconomic diversity in the greater Boston area, I have found that my Roxbury Community College has been using “an equity-minded campus culture” for our students, faculty, and staff. The college has been using the disaggregated data of the student profile based on the ethnicity (Black, Latinx, others), gender, age, and neighborhood districts. College Facts
Using these data is beneficial for student recruitment and staff employment.


It is great to hear that Roxbury is disaggregating data to better understand its students’ needs and community and for staff employment. My favorite OER research disaggregated by student demographic compares outcomes from OER classes to traditional classes at the University of Georgia (2018). We definitely need more research in this area for OER. See you tomorrow!!


I find the more disaggregation the better. The most helpful data doesn’t simply classify students as either white and non-white. Lumping all URM (underrepresented minorities - as identified in the book) of different genders into one group doesn’t give the full picture.


So true – and I admit to being guilty of this myself (and of lumping “underserved” students together). I’m pledging to do better!

Thanks to everyone who was able to join us live, here are the links that were shared in the chat window during our discussion.

NYTimes article: Hundreds More Unmarked Graves Found at Former Residential School in Canada

Here is our report from BHCC I am not sure how we get to them Fast Facts - Bunker Hill Community College

US Library of Congress: Subject Heading “illegal aliens” has been changed, they replaced it with “Noncitizens and Unauthorized immigration.” Here is some background. Library of Congress to Cancel the Subject Heading “Illegal Aliens”

Book recommendation that also addresses data and bias: Invisible Women: Data Bias in a World Designed for Men

CCCOER webinars recommended by attendees:
Decolonizing the Course
Tracking Key Program Indicators (KPIs) for OER