Design Features for Teaching Data Science for Social Justice (Poster 4)

• In Event: Promising Scholarship in Education Research: Dissertation Fellows and Their Research

Abstract

This poster brings together a discussion about statistics and data science education, teaching for social justice, and racial justice. In Particular, I discuss and illustrate six design features used to design a data science for social justice class that aim to foster statistical and critical literacies about the role of race and racism throughout the statistical and data investigation process. I am guided by the following research questions: What design features support students' understandings of race racism in the context of statistics and data science? How were the design features enacted in the curriculum? Drawing on Freire's notion of praxis (reflection and action) and Quantitative Critical Race Theory, there were six design features that were used in the class: opportunities for students to (a) reflect on the structural contexts, (b) engage in dialogue, (c) deepen and revise thinking, (d) use relevant data that may help foster agency, (e) engage in all phases of the data and statistical investigation cycle, and (d) interweave a course project throughout the course. The first three design features are themed around the reflection notion of praxis and the last three are themed around the action notion of praxis. The course was taught virtually during the Summer 2021 term at a four-year public institution in the US-Mexico borderlands of Southern California. There were 14 students enrolled in the class. Data used to illustrate the design features include lesson plans and activities, whole class interactions, classroom created artifacts, and individual student work.

Author

• Kevin Pelaez, Duo Security