Jana Asher is an assistant professor and the director of statistics education at Slippery Rock University. She has been an academic for a relatively short time; previously, her work focused on the collection and analysis of human rights violations data.

The Justice, Equity, Diversity, and Inclusion Outreach Group (JEDI) Corner is a regular component of Amstat News in which statisticians write about and educate our community about JEDI-related matters. If you have an idea or article for the column, email JEDI Outreach Group member Cathy Furlong.

For many years, a small group of dedicated faculty members at Slippery Rock University, in Pennsylvania, have been lobbying for a diversity education requirement for undergraduate students. This diversity requirement would be fulfilled through completion of one of several specially designated courses to ground student learning in principles related to equity and inclusion. I joined this faculty group just as success was on the horizon, and I helped structure an online training for the faculty that would be creating and teaching the new “diversity designated” courses.

I was included in this effort because I represented two marginalized groups that weren’t yet part of the dialogue. However, my status as a statistician made me more of an “outsider” than my Judaism or neurodivergence. In a group of experts on social science, gender studies, and modern languages, I was the person teaching the course who didn’t seem to match the objectives for the diversity requirement. And, at first, I thought maybe everyone else was right. How could I include all the topics they wanted to see in these courses and still cover the many learning outcomes already required in an elementary statistics class?

The pilot training occurred during the summer of 2021. It went well, and the group prepared for a second trial over the winter break (December 2021 to January 2022). This time, a larger cohort of faculty needed to be trained if the diversity requirement was to go live during the 2022–2023 academic year. And the group was finally willing to take a chance on a faculty member who teaches statistics. After three exhausting weeks, I had developed my new curriculum and added the following four diversity, equity, and inclusion (DEI) learning outcomes to the goals for the course:

• Students will articulate the connection between the statistical concepts of demographics, independence, assumptions, bias, causation, correlation, data visualization, and hypothesis testing and the DEI concepts of social identity, diversity, intersectionality, marginalization, discrimination, implicit bias, structural privilege, structural oppression, cultural competence, and social justice.
• Students will cite examples of how an individual’s social and cultural identity can lead to biased scientific conclusions that exacerbate structural oppression.
• Students will analyze examples of poorly designed/unethical and well-designed/ethical methods for data collection and analysis in the context of structural oppression and/or privilege.
• Students will articulate the connections between demographic data collection and human diversity and how statistics can create or exacerbate structural oppression of demographic groups.

So, how does one use critical pedagogy to restructure a course defined by problem sets and hypothesis testing? It helped that I’ve spent most of my career working with human rights violations data, so I had some idea of where to find connections between the statistics field and JEDI principles. But I still spent many days looking for course material that would solidify those connections.

I started with this column—using the first articles written after the JEDI Corner’s October 2021 debut. From there, I found the transcript of a JSM panel on implicit bias. This was a good start, but I wanted to pair readings related to JEDI with the statistical topics we covered. Statistical study design naturally paired with information about the Japanese internment and articles about how test subjects in randomized experiments have been white men primarily. Graphical summaries of data paired nicely with blogs from the Journal of Data Visualization and a recent publication by the Urban Institute on equity in data displays. Bit by bit, I built 11 sets of readings that would roughly map to the existing course structure and cover the DEI learning outcomes listed above.

Then came the toughest part: figuring out how to fit all this additional material into an already-full course. Something would have to give. Since the schedule for the course is based on three 50-minute periods, I decided to devote Mondays to the readings. Students would be assigned a reading module each week. Each reading module would be accompanied by a set of discussion questions for the students to consider as they read and prepared for Monday’s class period. On Monday, they would participate in group discussions led by undergraduate course assistants. Then, the students would be given reflection questions to guide a written response that would be due at the start of Wednesday’s class period. I would lecture on Wednesday about that week’s topics, and the students would complete an in-class laboratory on Friday.

I have just finished the fifth week of teaching this restructured course and am floored by just how different the course dynamic has been this semester. I have opened a floodgate; my students are desperate to talk to someone, anyone, about issues related to race, LGBT [lesbian, gay, bisexual, and transexual] experiences, and what is going on in the world around them. But they haven’t had a safe space to do so.

I read the first few sets of reflections and learned just what my students have been through over the past few years. Some have heart-wrenching stories of being sexually harassed, bullied, or otherwise mistreated. To those students, I am providing a vocabulary and structure for framing their traumas, and they are learning they are not alone in what has happened to them. Other students are seeing the inequities in society for the first time and are shocked by how little they understood about what life is like for people who aren’t their religion, race, or gender.

But what about statistics? Although I spend less time lecturing, my students are still learning the material, completing the homework, and coming to class. They are engaged in a way I never imagined was possible. I won’t know if or how their learning of the statistics material has been affected by the additional reading and discussion until the semester is over, but so far, the readings have been enhancing their statistical learning rather than detracting from it.

In the past, I would have several students who weren’t sure how to distinguish a census from a survey. After reading about the differential undercount, my students have a much more sophisticated understanding of that difference. The course now engages their emotions and their intellect, and that can be a powerful learning cocktail.

I plan on administering a mid-semester survey in a few weeks, but I have already gotten some indirect feedback. Mustafa Casson, one of the faculty members who facilitated the winter break training recently wrote to me and reported the following:

This initial feedback leaves me hopeful that this course might become a form of statistics education that works for a broad range of students.

If you are interested in learning more about DEI-infused statistics education, I will be presenting during the Joint Statistical Meetings in August about how my elementary statistics curriculum has evolved over the past five years. My sincere hope is I will be able to report back that this first semester teaching a DEI-designated elementary statistics course was a success.