Improving equity in data science :  re-imagining the teaching and learning of data in K-16 classrooms / 
edited by Colby Tofel-Grehl and Emmanuel Schanzer. 
 - New York, NY :  Routledge,  2024. 
 - 1 online resource 
<br/><br/>Foreword / Colby Tofel-Grehl and Emmanuel Schanzer -- Overview / Emmanuel Schanzer and Colby Tofel-Grehl -- Perspectives on research and practice in and around cultural relevance for pre-college data science in computing / Justice T. Walker, Amanda Barany, Alan Barrera, Michael A. Johnson and Sayed Moshin Reza -- Shrinking lands and growing perspectives: affordances of data science literacy during a culturally-responsive maker project / Tyler Hansen, Kristin Searle, Mengying Jiang, and Melissa Barker -- Design of tools and learning environments for equitable computer science + data science education / Shuchi Grover, Devin Jean, Brian Broll, Veronica Cateté, Isabelle Gransbury, Akos Ledeczi, and Tiffany Barnes -- The case For community centered data science / Colby Tofel-Grehl, Tyler Hansen, Emily Slater, and David Feldon -- Humanistic pre-service data science teacher education across the disciplines / Victor R. Lee -- Everyday equitable data literacy is best in social studies : STEM can't do what we can do / Tamara L. Shreiner and Mark Guzdial -- The utility of designing data science education programs from a framework of identity / June Ahn, Seth Van Doren, Jessica Cai, Ha Nguyen, Fernando Rodriguez, Christopher Martinez, and Jenny Han -- Building the infrastructure for quantitative criticalism in research methods courses / Mario I. Suárez -- Closing thoughts and future directions / Colby Tofel-Grehl and Emmanuel Schanzer. 
<br/><br/> "Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. Authors will share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work. Practitioners and teacher educators will be able to answer the question, "how can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?" whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms"-- 
<br/><br/><label>ISBN: </label>9781003364634 1003364632 9781040030158 1040030157 9781040030110 1040030114 
<br/><br/><label>Standard No.: </label>10.4324/9781003364634  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Electronic data processing--Study and teaching--United States.<br/>Computer science--Study and teaching--United States.<br/>Culturally relevant pedagogy--United States.<br/>Educational equalization--United States.<br/>EDUCATION / Higher<br/>EDUCATION / Teaching Methods & Materials / Science & Technology
<br/><br/><label>Dewey Class. No.: </label>004.071/073