K-12 Data and Computing Competencies: Implications for Statistics Educators

Abstract

While computing and data shape nearly every aspect of modern life, efforts to expand data and computing education in K-12 settings have grown rapidly but unevenly. The 2026 National Academies “Developing Competencies for the Future of Data and Computing: The Role of K-12” consensus report (LINK: https://www.nationalacademies.org/projects/DBASSE-BOSE-23-04/) identified and described seven competencies that are critical for students to thrive in a data-driven computational world: 1) problem posing and problem-solving processes; 2) producing and working with data; 3) abstraction, algorithmic thinking, and automation; 4) probabilistic and inferential reasoning; 5) models and representations; 6) technology and society; and 7) data and computing systems.
The report suggests a road map to use these competencies to improve the integration of data and computing into primary and secondary education.

What are the implications of the growth of K-12 computer science, data science, machine learning, and artificial intelligence and the seven competencies on undergraduate statistics education? How does the report connect to the GAISE (Guidelines for Assessment and Instruction in Statistics Education) K-12 and GAISE College reports? How can we build on the proposed integrated framework in productive ways? In this webinar, four members of the consensus report committee will consider these critical questions, provide an overview of the consensus report, and offer prognostications about the future of undergraduate statistics education.