Foundations of K–12 Data Science Education

Session Type: Symposium

Abstract

This symposium aims to build upon a workshop held by the National Academies of Sciences, Engineering, and Medicine on the Foundations of K-12 Data Science Education by offering insights into research, policy, and practice with a focus on how our youngest learners come to use and reason with data. In particular, this session will explore what is known about critical data literacy and what this looks like in the enactment of data science projects; unpack what we know about data science learning including assumptions and challenges; examine the implementation of data science programs and pathways across states; and provide an overview of the tools and resources available to support data science learning in grades K-12.

Sub Unit

• Division C - Learning and Instruction / Division C - Section 3b: Technology-Based Environments

Chair

• Amy Stephens, National Academies of Sciences, Engineering, and Medicine

Participants

• Michelle Hoda Wilkerson, University of California - Berkeley

• Nicholas Horton

Papers

• Critical Data Literacy for Grades K–12 - Josephine K. Louie, Education Development Center, Inc.

• Tools for Learning and Doing Data Science at the K–12 Level - Daniel Pimentel, University of Alabama; Michelle Hoda Wilkerson, University of California - Berkeley; Nicholas Horton

• Reviewing the Landscape of K–12 Data Science Implementation - Zarek Drozda, University of Chicago; Davis Johnstone; Brooke Van Horne; Ava Severts

• K–12 Data Science Learning - Joshua M. Rosenberg, University of Tennessee; Ryan Seth Jones, Middle Tennessee State University

Discussant

• Andrés Henriquez, Education Development Center, Inc.