Improving Equity in Data Science

Regular price €167.40
Age Group_Uncategorized
Age Group_Uncategorized
automatic-update
B01=Colby Tofel-Grehl
B01=Emmanuel Schanzer
Category1=Kids
Category1=Non-Fiction
Category=JNAM
Category=JNM
Category=JNU
Category=YPM
Category=YQS
Computer Science
COP=United Kingdom
Data Literacy
Data Science
Data science education
Delivery_Delivery within 10-20 working days
eq_isMigrated=2
eq_non-fiction
eq_society-politics
Equity
K-12
K-16
Language_English
PA=Available
Price_€100 and above
PS=Active
softlaunch
STEM
STEM education

Product details

  • ISBN 9781032428666
  • Weight: 453g
  • Dimensions: 152 x 229mm
  • Publication Date: 03 Jun 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
  • Language: English
Delivery/Collection within 10-20 working days

Our Delivery Time Frames Explained
2-4 Working Days: Available in-stock

10-20 Working Days
: On Backorder

Will Deliver When Available
: On Pre-Order or Reprinting

We ship your order once all items have arrived at our warehouse and are processed. Need those 2-4 day shipping items sooner? Just place a separate order for them!

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.

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.