Consensus Study Report

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This activity was supported by a grant between the National Academy of Sciences and the National Science Foundation (2330859). Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.

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Digital Object Identifier: https://doi.org/10.17226/29303

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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2026. Data and Computing in K–12 Education: Foundational Competencies. Washington, DC: National Academies Press. https://doi.org/10.17226/29303.

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COMMITTEE ON DEVELOPING COMPETENCIES FOR THE FUTURE OF DATA AND COMPUTING: THE ROLE OF K–12

NICHOLAS J. HORTON (Chair), Amherst College

ANNA E. BARGAGLIOTTI, Loyola Marymount University

SHAUNDRA B. DAILY, Duke University

LAURA G. DEMARCO, Harvard University

EMILY E. EDWARDS, Duke University

SUSAN GOMEZ-ZWIEP, BSCS Science Learning

SHUCHI GROVER, Raspberry Pi Foundation

LINDSEY HENDERSON, ExcelinEd

VICTOR R. LEE, Stanford University

JANICE K. MAK, Arizona State University

JOSHUA M. ROSENBERG, University of Tennessee, Knoxville

ANDEE RUBIN, TERC

NANCY B. SONGER, University of Utah

JOHN A. UNDERWOOD, Louisiana Department of Education

SEPEHR VAKIL, Northwestern University

AMAN YADAV, Michigan State University

Study Staff

KERRY BRENNER, Study Director, Senior Program Officer, Board on Science Education (starting March 2025)

AMY STEPHENS, Study Director, Associate Executive Director, Division of Behavioral and Social Sciences and Education (until March 2025)

LETICIA GARCILAZO GREEN, Associate Program Officer, Board on Science Education

LACHELLE THOMPSON, Senior Program Assistant, Board on Science Education

HEIDI SCHWEINGRUBER, Board Director, Board on Science Education

SAMANTHA KORETSKY, Research Associate, Board on Mathematical Sciences and Analytics

MICHELLE SCHWALBE, Board Director, Board on Mathematical Sciences and Analytics

THO NGUYEN, Senior Program Officer, Computer Science and Telecommunications Board

JON EISENBERG, Board Director, Computer Science and Telecommunications Board

Reviewers

This Consensus Study Report 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 report as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process. We thank the following individuals for their review of this report:

Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the conclusions or recommendations of this report nor did they see the final draft before its release. The review of this report was overseen by MARCIA C. LINN, University of California, Berkeley, and FRED B. SCHNEIDER, Cornell University. They were responsible for making certain that an independent examination of this report was carried out in accordance with the standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the authoring committee and the National Academies.

Acknowledgments

This report would not have been possible without the individuals who provided their expertise, including those who served on the committee and those who participated in discussions with the committee. We recognize their invaluable contributions to our work. The first thanks are to the committee members for their passion, deep knowledge, and contributions to the study. Members of the committee benefited from discussions with and presentations by many individuals who participated in our information-gathering meetings.

• At the first meeting, we had the opportunity to talk with our contacts at the National Science Foundation, Allyson Kennedy who was joined by Fengfeng Ke, to get further clarity on the statement of task.
• At the second meeting, the following topics were explored:
  • The Landscape of K–12 Data and Computing Education: State of the States. Presenters included Joshua Childs (University of Texas at Austin and ECEP Alliance) and Zarek Drozda along with Samantha Leav (Data Science 4 Everyone).
  • Implementation of Data and Computing in K–12 Data Classrooms. Presenters included Jenifer Hooten (Academy for Technology and the Classics, Santa Fe, New Mexico), Chinma Uche (Academy of Aerospace and Engineering, Windsor, Connecticut), Rachel “Ray” Levy (North Carolina State University,

• • Data Science and AI Academy), and Padmanabhan (Padhu) Seshaiyer (George Mason University).
  • Increasing Calls for Data and Computing: The Role of Professional Organizations. Presenters included Kevin Dykema (National Council of Teachers of Mathematics), Jacqueline El-Sayed (American Society for Engineering Education), Erika Shugart (National Science Teaching Association), and Bryan “BT” Twarek (Computer Science Teachers Association).
• The third meeting included two panels:
  • Panel 1 discussed Assessment. Panelists included Mark Wilson and Karen Draney (University of California, Berkeley), Roxana Hadad (Processing Foundation), Maya Israel (University of Florida), and David Weintrop (University of Maryland).
  • Panel 2 examined Connections to Career and Technical Education at different levels within the education systems. Panelists included Catherine Imperatore (Association for Career and Technical Education), Amery Martinez (Denver Public Schools), and Karl Schubert (University of Arkansas).

This report was made possible with support from the National Science Foundation. We particularly thank Jeff Forbes (program director, Education and Workforce) and the Directorate for Computer and Information Science and Engineering for their sponsorship of this work.

This study was a product of the combined work of three boards within the National Academies of Sciences, Engineering, and Medicine. The project was led by the Board on Science Education in collaboration with the Board on Mathematical Sciences and Analytics and the Computer Science and Telecommunications Board. We are grateful to the members of all these boards for their contributions to the framing of studies and fostering connections to the relevant communities.

Thanks are also due to the project staff: study directors Dr. Amy Stephens and Dr. Kerry Brenner, and Dr. Heidi Schweingruber, director of the Board on Science Education. Leticia Garcilazo Green, LaChelle Thompson, and Sam Koretsky also provided invaluable assistance. Laura Yoder substantially improved the readability of the report, and Heather Kreidler provided crucial fact-checking support. Kirsten Sampson Snyder expertly guided the report thought the report review process, and Bea Porter masterfully guided the report through production.

Contents

Boxes, Figures, and Tables

Acronyms and Abbreviations

Preface

Summary

1 Introduction

DATA AND COMPUTING IN SOCIETY

DATA AND COMPUTING IN THE WORKFORCE

DATA AND COMPUTING IN K–12 EDUCATION

Need for Learning Opportunities

Access to Resources

Opportunities for Participation

CHARGE TO THE COMMITTEE

Study Process

Defining Key Terms

REPORT ORGANIZATION

REFERENCES

2 Current Context

EFFORTS TO INCORPORATE COMPUTING INTO K–12 EDUCATION

Computer Science Courses

Current Status of Computer Science Education in K–12

Artificial Intelligence

Quantum Information Science

Summary

EFFORTS TO INCORPORATE DATA AND DATA SCIENCE INTO K–12 EDUCATION

Summary

STEM SUBJECTS AS A CONTEXT FOR DATA AND COMPUTING EDUCATION

Mathematics

Statistics

Science

Summary

CHALLENGES TO IMPLEMENTATION

Summary

SUMMARY AND CONCLUSIONS

REFERENCES

3 Foundational Competencies

COMPETENCY 1: PROBLEM POSING AND PROBLEM-SOLVING PROCESSES

Examples of Problem Cycles from Data Science and Computing

Components of the Competency

COMPETENCY 2: PRODUCING AND WORKING WITH DATA

Components of the Competency

COMPETENCY 3: ABSTRACTION, ALGORITHMIC THINKING, AND AUTOMATION

Components of the Competency

COMPETENCY 4: PROBABILISTIC AND INFERENTIAL REASONING

Components of the Competency

COMPETENCY 5: MODELS AND REPRESENTATIONS

Components of the Competency

COMPETENCY 6: TECHNOLOGY AND SOCIETY

Components of the Competency

COMPETENCY 7: DATA AND COMPUTING SYSTEMS

Components of the Competency

CONNECTIONS AMONG COMPETENCIES

SUMMARY AND CONCLUSIONS

REFERENCES

4 Elevating the Foundational Competencies Within STEM-Related Subjects

FOUNDATIONAL COMPETENCIES WITHIN MATHEMATICS AND STATISTICS

COMPETENCY 1: PROBLEM POSING AND PROBLEM-SOLVING PROCESSES

COMPETENCY 2: PRODUCING AND WORKING WITH DATA

COMPETENCY 3: ABSTRACTION, ALGORITHMIC THINKING, AND AUTOMATION

COMPETENCY 4: PROBABILISTIC AND INFERENTIAL REASONING

COMPETENCY 5: MODELS AND REPRESENTATIONS

COMPETENCY 6: TECHNOLOGY AND SOCIETY

COMPETENCY 7: DATA AND COMPUTING SYSTEMS

EXAMPLES OF THE FOUNDATIONAL COMPETENCIES IN MATHEMATICS AND STATISTICS

FOUNDATIONAL COMPETENCIES WITHIN SCIENCE AND ENGINEERING

Competency 1: Problem Posing and Problem-Solving Processes

Competency 2: Producing and Working with Data

Competency 3: Abstraction, Algorithmic Thinking, and Automation

Competency 4: Probabilistic and Inferential Reasoning

Competency 5: Models and Representations

Competency 6: Technology and Society

Competency 7: Data and Computing Systems

Examples of the Foundational Competencies in Science and Engineering

MAKING INTEGRATION EXPLICIT FOR LEARNERS

SUMMARY AND CONCLUSIONS

REFERENCES

5 Effective Learning Experiences

SCHOLARSHIP ON HOW PEOPLE LEARN

Learning Is Active, Social, Constructive, and Lifelong

Prior Knowledge and Experiences Shape Learning

Motivation and Identity Are Part of the Learning Process

Learning About a Discipline Requires Extended Opportunities Over Time

Ways of Thinking About Society Can Inform the Design of Learning Experiences

DESIGN CONSIDERATIONS TO SUPPORT LEARNING

DESIGNING FOR TECHNOLOGY AND TOOL INTEGRATION

PROMISING EXAMPLES OF LEARNING ACROSS GRADE BANDS

Elementary School

Middle School

High School

SUMMARY AND CONCLUSIONS

REFERENCES

6 Design of Curriculum

APPROACHES TO INTEGRATION

Summary

INTEGRATING DATA AND COMPUTING INTO EXISTING SCIENCE AND MATH CURRICULUM

Mathematics

Science

Examples of Curriculum Integration into Science and Math by Grade Band

Summary

SUMMARY AND CONCLUSION

REFERENCES

7 Preparing and Supporting Teachers of Data and Computing

WHAT KNOWLEDGE IS NEEDED TO TEACH DATA AND COMPUTING?

Knowledge of Data and Computing

Pedagogical Content Knowledge and Related Concepts

Professional Learning Communities

Special Considerations for Elementary Teachers

Summary

PREPARATION OF DATA AND COMPUTING TEACHERS

Teacher Preparation Programs

In-Service Teacher Professional Learning

Subject Matter Certification of Data and Computing Teachers

Alternative Certification Pathways for Computer Science Teachers

Summary

ADDITIONAL CONSIDERATIONS FOR PREPARING TEACHERS OF DATA AND COMPUTING

SUMMARY AND CONCLUSIONS

REFERENCES

8 Systemic Change to Support Data and Computing Learning

SYSTEM CHANGE AT THE NATIONAL AND STATE LEVELS

Summary

SYSTEM CHANGE AT THE LOCAL LEVEL

School Leaders as Champions

Systems for Instructional Support

Curricular Materials

Engaging Families and Communities

The Importance of OST Spaces

Summary

IMPLICATIONS FOR POLICY AND PRACTICE

Summary

SUMMARY AND CONCLUSION

REFERENCES

9 Recommendations and Research Agenda

FOUNDATIONAL COMPETENCIES

RECOMMENDATIONS

Adding Data and Computing to K–12 Education

Supporting Teachers of Data and Computing

Transforming the System

RESEARCH AGENDA

Design of Courses and Curriculum

Implementation of Courses and Curriculum

Professional Learning

System Supports

Measuring Impact

A FINAL NOTE

Appendix: Biosketches

Boxes, Figures, and Tables

BOXES

1-1 Statement of Task

1-2 A Note on Statistics

2-1 Defining and Researching Computational Thinking

2-2 High School Mathematics Requirements in Utah

2-3 New High School Mathematics Pathways in Maryland

3-1 Competency 1: Problem Posing and Problem-Solving Processes

3-2 Competency 2: Producing and Working with Data

3-3 Competency 3: Abstraction, Algorithmic Thinking, and Automation

3-4 Competency 4: Probabilistic and Inferential Reasoning

3-5 Using Probability for Weather and GPS

3-6 Competency 5: Models and Representations

3-7 Representing Geospatial Data

3-8 Competency 6: Technology and Society

3-9 Competency 7: Data and Computing Systems

5-1 Shrinking Lands Project

6-1 Statistics Integration into Algebra II

6-2 Computer Science Integration into Science Class

7-1 Questions for Leaders of Teacher Professional Learning

8-1 Mathematics Redesign in Indiana

8-2 Digital Youth Network in Evanston, IL

FIGURES

1-1 Conceptual diagram depicting relationships among computing, computer science, and computational thinking

1-2 Data science and data thinking

2-1 Number of states with computer science standards

2-2 Number of states allocating funding to computer science education

2-3 Five big ideas from the AI4K12 framework

2-4 Data science state implementation tiers

3-1 Data investigation process cycle

3-2 Various types of graphs

3-3 Mosaic plot showing Titanic survival data

3-7-1 A map illustrating urban growth from 1985 to 2011, with a fire hazard map superimposed

4-1 Process cycle from Common Core

4-2 Process cycle from GAIMME

4-3 Process cycle from GAISE II

4-4 Invented student display of plant size measurements collected by students

4-5 Invented displays of arm span by students

4-6 Visualization created by a student using TinkerPlots to show deviation from mean values for all measurements

4-7 Spinners as tools for modeling the sources of error in measurement

4-8 Student-discovered strategies for calculating the area of a triangle in Lattice Land

4-9 Representing pixel brightness graphically

4-10 Engineering design process represented cyclically

4-11 Engineering design process as series of steps

4-12 Important characteristics of the engineering design process for elementary school (left) and middle school (right)

5-1 Student model development in fifth grade unit on nurse logs

5-2 Modeling dynamic ecosystems

5-3 Using CODAP for data analysis

5-4 Examples of student-produced infographics before (left) and after (right) editorial feedback to more clearly communicate with data

6-1 A computational thinking framework for elementary school teachers

8-1 Dimensions of computer science (CS) education implementation with examples

TABLES

S-1 Overview of the Competencies for Data and Computing

2-1 K–12 Computer Science Framework’s Core Concept Areas

2-2 K–12 Computer Science Framework’s Core Practices

2-3 K–12 Computer Science Framework’s Crosscutting Concepts

2-4 Organizational Structure of the 2025 K–12 Data Science Learning Progressions

3-1 Overview of the Foundational Competencies for Data and Computing

3-2 Mapping the Foundational Competencies to Existing Frameworks for Computer Science and Data Science

4-1 Foundational Competencies for Data and Computing

4-2 Foundational Competencies Linked to Frameworks in Mathematics and Statistics

4-3 Foundational Competencies Linked to Framework in Science and Engineering

9-1 Descriptions of the Foundational Competencies

Acronyms and Abbreviations

Preface

Restructuring the K–12 curriculum to fully integrate data and computing is not just necessary, it is decades overdue. In his 1980 book Mindstorms: Children, Computers, and Powerful Ideas, Seymour Papert notes that “today’s culture is marked by a ubiquitous computer technology” (p. 181). He calls out “computational thinking” in an early form to be integrated into everyday life and suggested that we “recast powerful ideas in computational form, ideas that are as important to the poet as to the engineer” (p. 183).

Even earlier developments in statistics heralded the advent of exploratory data analysis (EDA), with eminent statistician John Tukey writing in 1962 that “I have come to feel that my central interest is in data analysis,” (p. 2) which included ways for planning the gathering of data, exploration of the data, and confirmatory procedures, with interpretation and domain knowledge important at each step. The concept of EDA took off as a precursor and complement to what had been confirmatory data analysis. Mosteller, et al.’s 1983 text Beginning Statistics with Data Analysis adopted a new approach that was organized into four units: (a) EDA, (b) methods for collecting data, (c) formal statistical inference, and (d) techniques for modeling.

I graduated from Albany (NY) High School in 1982. In my classes at that time, the role of data and computing was minimal, but the potential to infuse and integrate them into the curriculum existed. I had the benefit of exposure to computational and data revolutions while in college, and I was

captivated by the interplay between these worlds. It has led to my career as a biostatistician and data scientist.

Computation can facilitate making sense of the data that surround us—a capacity that can be used to leverage learning across the K–12 curriculum. It’s sometimes noted that our education system would not be the way it is if it had been developed when computers were everywhere. While there have been efforts to integrate computation into K–12 classrooms, transforming what and how we teach, all too often they’ve been piecemeal and fragmented, leading to a “messy garden” without a cohesive vision. At a time when computational tools and systems have become even more powerful, data are everywhere, and sophisticated artificial intelligence models are now omnipresent, we need a roadmap to integrate data and computing in an effective way.

For the past two years, I have had the privilege of chairing the Developing Competencies for the Future of Data and Computing: The Role of K–12 consensus study committee. Our charge was to advance national conversations about the role of data and computing in K–12 education. Building on prior research, existing standards, and other resources, we have outlined a set of students’ competencies in data and computing and suggested ways that learning opportunities and curricula could integrate them into future curricula.

I am appreciative of the wisdom, hard work, and time offered by each of the esteemed members of the committee: I have learned a tremendous amount, and the report would not have been possible without their efforts.

On behalf of the entire committee, I want to thank the remarkable National Academies of Sciences, Engineering, and Medicine staff members who made the report a reality. Study directors Dr. Amy Stephens and Dr. Kerry Brenner helped shepherd our efforts. Dr. Heidi Schweingruber, director of the Board on Science Education, provided exceptional leadership and guided our engagement with the broader STEM education world. Leticia Garcilazo Green, Sam Koretsky, LaChelle Thompson, and Tho Nguyen also provided invaluable assistance.

Kids are deeply curious about the world around them. Computation and data can build on this curiosity and allow future students to flourish. Long ago, Papert’s Mindstorms argued that “certain uses of very powerful computational technology and computational ideas can provide children with new possibilities for learning, thinking, and growing emotionally as well as cognitively” (pp. 17–18). So much more is known now than in 1980 about ways that students can learn about data and computing. This is the time for us to make major changes to the existing “messy garden”

so that current and future students can benefit from these not so new opportunities.

Nicholas J. Horton
Chair of the Committee on Developing Competencies for the Future of Data and Computing: The Role of K–12
January 2026

REFERENCES