Building Statistical and Multivariable Thinking with Social Justice Investigations

• Activity Number: 447 - Multivariable Thinking Across the Curriculum
• Type: Topic Contributed
• Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistics and Data Science Education
• Author(s): Beth L. Chance* and Josephine Louie and Soma Roy and Emily Fagan and Jennifer Stiles and William Finzer
• Companies: Cal Poly - San Luis Obispo and Education Development Center and California Polytechnic State University - San Luis Obispo and Education Development Center and Education Development Center and Concord Consortium
• Keywords: Education; Statistical thinking; Underrepresented minorities

Abstract

Demands for data fluency and multivariable thinking are increasing but the number of graduates with these skills are not currently meeting demands (e.g., McKinsey Report, 2016). Researchers have conjectured that more students, particularly from underrepresented groups in STEM, will find statistics compelling if they can use authentic data to explore questions related to social justice (Gutstein, 2003; Lesser, 2007; Voss & Rickards, 2016). In this talk, we describe our collaborative project to develop statistics lessons for use in non-AP statistics classes in racially and ethnically diverse urban high schools. We aim to increase interest and understanding of key statistical ideas (e.g., distributions, conditional proportions, margin of error, multivariable thinking), by focusing on income inequality and myths related to immigration. Students use a free online data visualization tool (CODAP) to extract, visualize, and analyze microdata from the U.S. Census and American Community Survey. The culminating experience of each two-week module is an independent data investigation task. We will describe our development process, initial implementations, and preliminary research findings.