Improving Equity in Data Science

Re-Imagining the Teaching and Learning of Data in K-16 Classrooms
1. Auflage 2024
ISBN: 978-1-032-42862-8
Verlag: Routledge

Buch, Englisch, 206 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 307 g

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 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.

Zielgruppe

Postgraduate and Professional Reference

Autoren/Hrsg.

Tofel-Grehl, Colby

Schanzer, Emmanuel

Fachgebiete

• Sozialwissenschaften Pädagogik Schulen, Schulleitung Universitäten, Hochschulen
• Sozialwissenschaften Pädagogik Lehrerausbildung, Unterricht & Didaktik Allgemeine Didaktik Naturwissenschaften, Mathematik (Unterricht & Didaktik)

Inhaltsverzeichnis

1. Overview 2. Perspectives on Research and Practice In and Around Cultural Relevance for Pre-College Data Science in Computing 3. Shrinking Lands and Growing Perspectives: Affordances of Data Science Literacy During a Culturally-Responsive Maker Project 4. Design of Tools and Learning Environments for Equitable Computer Science + Data Science Education 5. The Case For Community Centered Data Science 6. Humanistic Pre-Service Data Science Teacher Education Across the Disciplines 7. Everyday Equitable Data Literacy is Best in Social Studies: STEM Can’t Do What We Can Do 8. The Utility of Designing Data Science Education Programs from a Framework of Identity 9. Building the Infrastructure for Quantitative Criticalism in Research Methods Courses 10. Closing Thoughts and Future Directions

Über Autor(innen)

Colby Tofel-Grehl is an associate professor of STEM teacher education and learning at Utah State University, USA.

Emmanuel Schanzer is a math and CS-Education researcher, and the co-founder and chief curriculum architect at Bootstrap.