1D: Using Social Justice Data Investigations to Expand Opportunities for Multivariable Thinking in Diverse High School Classes


With Soma Roy (California Polytechnic State University, San Luis Obispo), Josephine Louie (Education Development Center, Waltham, MA), Emily Fagan (EDC), & Jennifer Stiles (EDC)


Abstract

In a world awash with data, there is a need in PreK-12 education to foster students’ multivariable thinking, and to engage diverse student populations, including traditionally marginalized groups, in statistics and data science. The Strengthening Data Literacy across the Curriculum (SDLC) project has been developing and researching curriculum modules utilizing large multivariable datasets to use investigations of social justice issues in high school non-AP statistics. In these modules, students download and explore person-level data from the U.S. Census Bureau to investigate questions related to income inequality and immigration in the U.S. In this session, we share findings on how students engaged with the data, and how we supported instructors to facilitate social justice discussions in their statistics classes and develop and broaden students’ multivariable thinking. Participants will engage in 10-15 minute small-group discussions to exchange ideas about (a) incorporating social justice contexts and relevant datasets to help students see how statistics relates to their daily lives and the world around them, and to motivate the need to investigate claims with data; (b) encouraging multivariable thinking among students in high school statistics classes and beyond, using technology to support their data investigations; and (c) finding the balance between open-ended and scaffolded exploration, and between messy datasets and clean curated datasets, to provide opportunities for students’ productive struggle. After small group discussions, participants will return to whole group to report out ideas and key take-aways. We will share a link to the web-based free educational software CODAP during the session.


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