Resources for JEDI-Informed Teaching of Statistics
Pedagogy, research, and professional development
Despite the dearth of literature specifically on teaching statistics using social justice, there is precedent in the more general realm of teaching using social justice, or even in teaching mathematics using social justice. This article offers an overview of content examples, resources, and references that can be used in the specific area of statistics education. Philosophical and pedagogical references are given, definitional issues are discussed, potential implementation challenges are addressed, and a substantial bibliography of print and electronic resources is provided.
Course syllabus for "Statistics For Social Justice" at Coachella Valley Unified School District.
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.
Some research has suggested that groups of people working on a task can do better if the group is more diverse, since diverse group members can suggest more creative ideas and make better decisions. At the same time, diverse groups can have more conflict than less diverse groups, possibly eliminating those benefits. This dataset is from a study that attempted to understand these factors and how they relate to the testosterone levels of members of the group.
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.
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.