Proceedings of a Workshop
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International Standard Book Number-13: 978-0-309-69815-3
International Standard Book Number-10: 0-309-69815-4
Digital Object Identifier: https://doi.org/10.17226/26852
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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2023. Foundations of Data Science for Students in Grades K–12: Proceedings of a Workshop. Washington, DC: The National Academies Press. https://doi.org/10.17226/26852.
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PLANNING COMMITTEE FOR FOUNDATIONS OF DATA SCIENCE FOR STUDENTS IN GRADES K–12: A WORKSHOP
NICHOLAS J. HORTON (Co-Chair), Amherst College
MICHELLE H. WILKERSON (Co-Chair), University of California, Berkeley
TAMARA L. CLEGG, University of Maryland
ZAREK D. DROZDA, Data Science 4 Everyone
TIMOTHY E. ERICKSON, Epistemological Engineering
HOLLYLYNNE STOHL LEE, North Carolina State University
CAMILLIA MATUK, New York University
LEIGH F. PEAKE, Gulf of Maine Research Institute
Staff
AMY STEPHENS, Study Director, Board on Science Education
JANET GAO, Program Officer, Board on Science Education
LAUREN RYAN, Senior Program Assistant, Board on Science Education
HEIDI SCHWEINGRUBER, Director, Board on Science Education
BOARD ON SCIENCE EDUCATION
SUSAN R. SINGER (Chair), Vice President for Academic Affairs, Provost, Rollins College
SUE ALLEN, Senior Research Scientist, Maine Mathematics and Science Alliance
MEGAN BANG, Professor of Learning Sciences and Psychology, Northwestern University
VICKI L. CHANDLER, Provost, Minerva Schools at Keck Graduate Institute
SUNITA V. COOKE, Superintendent and President, MiraCosta College
MAYA M. GARCIA, Science Content Specialist, Colorado Department of Education
RUSH HOLT, CEO Emeritus, American Association for the Advancement of Science
TONYA MATTHEWS, CEO, International African American Museum
WILLIAM PENUEL, Professor of Learning Sciences and Human Development, University of Colorado Boulder
STEPHEN L. PRUITT, President, Southern Regional Education Board
K. RENAE PULLEN, K–6 Science Curriculum Instructional Specialist, Caddo Parish Schools, Louisiana
K. ANN RENNINGER, Dorwin P. Cartwright Professor of Social Theory and Social Action, Swarthmore College
MARCY H. TOWNS, Bodner-Honig Professor of Chemistry, Purdue University
DARRYL N. WILLIAMS, Senior Vice President, Science and Education, The Franklin Institute
Staff
HEIDI SCHWEINGRUBER, Director
Acknowledgments
This Proceedings of a Workshop was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published proceedings as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the charge. The review comments and draft manuscript remain confidential to protect the integrity of the process.
We thank the following individuals for their review of this proceedings: Andres Henriquez, Education Development Center, Inc.; Rachel Levy, North Carolina State University; and Andee Rubin, TERC.
Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the content of the proceedings nor did they see the final draft before its release. The review of this proceedings was overseen by Marcia C. Linn, University of California, Berkeley. She was responsible for making certain that an independent examination of this proceedings was carried out in accordance with standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the rapporteur and the National Academies.
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Contents
2 Vision for K–12 Data Science Education and Outcomes
CREATING A VISION OF “HIGH-QUALITY” K–12 DATA SCIENCE EDUCATION
A VISION FOR K–12 DATA SCIENCE LEARNING AND OUTCOMES
3 Current Landscape of Data Science Education
CONTEXTUAL FACTORS FOR K–12 DATA SCIENCE EDUCATION
INTEGRATION INTO OTHER CONTENT AREAS
4 Supporting Implementation: Tools, Resources, and Teacher Preparation
Boxes, Figures, and Table
BOXES
1-2 Workshop Guiding Questions
5-1 List of Participant-identified Priorities for Research, Development, and Resources
5-2 List of Participant-identified Needs Informed by Teaching Practice Discussions
FIGURES
2-1 A simple representation of the data investigation process
3-1 Discrete data science programs
3-2 Frequency of data science programs across subjects in statewide programs
3-3 Critical computational expression
4-1 Two data cards with labels
4-2 Two-step decision tree using data cards
TABLE
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