Improving Equity in Data Science (eBook, ePUB)

Re-Imagining the Teaching and Learning of Data in K-16 Classrooms
Redaktion: Tofel-Grehl, Colby; Schanzer, Emmanuel

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 216
• Erscheinungstermin: 3. Juni 2024
• Englisch
• ISBN-13: 9781040030158
• Artikelnr.: 70323368

Autorenporträt

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.

Inhaltsangabe

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