As a way to engage all of the students who pass through our classes, the CURV database profiles statisticians and data scientists with backgrounds that aren't typically seen in our textbooks and histories. With dozens of accounts, you can use the database for a statistician-of-the day activity.

Dr. Rochelle Gutierrez (2002) stated "equity is ultimately about the distribution of power - power in the classroom, power in future schooling, power in one's everyday life, and power in a global society." This presentation unpacks ways in which statistics classrooms can put power in students' hands. 

Impostor Syndrome (IS) is the feeling of inadequacy or self-doubt that individuals experience despite their actual accomplishments or qualifications.

IS and the hidden curriculum are prevalent in various settings, and they can have a significant impact on individuals' confidence, professional growth, and overall well-being. By fostering open discussions, providing support networks, and actively addressing these issues, we can create more inclusive and nurturing environments where individuals feel empowered to thrive.

Does some of your scholarly work have a JEDI focus? Are you worried that the work won’t “count” as much as the other aspects of your research when building your dossier for tenure? Although many institutions are including JEDI criteria for promotion, some places are still hesitant to value JEDI contributions.

The linked content on “Translating Equity-Minded Principles into Faculty Evaluation Reform” (O’Meara et al. 2022, American Council on Education) goes into detail on the “Measurement of Scholarly Impact” (page 8). They provide substantial literature on some of the current issues seen by JEDI researchers, and they suggest alternative ways of evaluating scholarship. In your research statement, you might consider using some of their alternative methods to highlight how your work has made an impact across a variety of settings.

Our recent research focuses on integrating Culturally Relevant Data into statistics and data science education. We’ve developed methods that connect coursework directly to students’ lived experiences, significantly enhancing engagement and understanding, particularly for those from historically marginalized groups. Our approach bridges the gap between theoretical data science and real-world application, making learning both accessible and impactful. Initial results from pilot courses are promising, and we are excited about the potential to further transform how data science is taught. Join us in making data science education more inclusive and effective for everyone

When done effectively, nontraditional grading methods can promote equity and help build an inclusive classroom. As with any shift in pedagogy, there are a number of questions to consider. This article summarizes four types of nontraditional grading and shares experiences from the authors who have applied them to a variety of courses in statistics.