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S 1602119th CongressIn Committee

Mathematical and Statistical Modeling Education Act

Introduced: May 5, 2025
Standard Summary
Comprehensive overview in 1-2 paragraphs

The Mathematical and Statistical Modeling Education Act would create a coordinated federal effort, led by the National Science Foundation (NSF), to modernize mathematics in K-12 STEM education through mathematical and statistical modeling, data-driven work, and computational thinking. It would fund research and development to support problem-, project-, and performance-based learning, interdisciplinary exploration, and career connections. A key feature is awarding grants to institutions of higher education and nonprofit organizations (in partnership with local education agencies) to develop innovative modeling-focused curricula and teacher professional development, with a strong emphasis on reaching students historically underrepresented in STEM and on smoother transitions between school levels and into internships or jobs. The bill also directs a study by the National Academies of Sciences, Engineering, and Medicine (NASEM) on the implementation and effectiveness of mathematical and statistical modeling in K-12 education, with public stakeholder input and a final report. Funding is authorized for both efforts through 2030, with a sunset on the awards authority in 2029. Overall, the act aims to expand data literacy, computational thinking, and modeling skills among students while strengthening teacher preparation and school-community partnerships to align education with workforce needs.

Key Points

  • 1Establishes a federal initiative, led by NSF, to modernize K-12 math education via mathematical and statistical modeling, data science, operations research, and computational thinking, including data-driven problem solving and interdisciplinary learning.
  • 2Authorizes $10,000,000 annually (fiscal years 2026-2030) for NSF’s Directorate for STEM Education to fund research and development projects in high-quality mathematical modeling education in public schools, with emphasis on partnerships, transitions, and engagement of underrepresented groups.
  • 3Requires merit-reviewed awards to institutions of higher education and nonprofit organizations (or consortia) and encourages partnerships with local educational agencies, tribal education entities, and industry/community stakeholders; mandates comprehensive project plans and sustained collaborations.
  • 4Specifies use-of-funds activities, including educator professional development, research on curricula and teaching practices, use of real data sets, district-wide implementation, support for rural agencies, accessibility and mastery-based assessments, pre-service/in-service training resources, and mechanisms to connect educators with employers.
  • 5Creates a National Academies study on factors affecting implementation, teacher preparation, and stakeholder communication for mathematical/statistical modeling in K-12, with public meetings and a final report due within 24 months of an agreement; funds the study at $1,000,000 annually (2026-2030).
  • 6Establishes evaluation, accountability, and dissemination requirements for grant programs (common benchmarks, annual/final reporting, and a public report to Congress) and provides for reporting of findings and best practices.
  • 7Includes sunset for the awards authority on September 30, 2029; funding is derived from NSF appropriations; defines key terms (Director, Foundation, GAIMME, GAISE II, etc.) to guide implementation.

Impact Areas

Primary group/area affected: K-12 students (with special focus on groups historically underrepresented in STEM), and K-12 educators in public schools; districts and local educational agencies, including rural and tribal education entities.Secondary group/area affected: Institutions of higher education, nonprofit organizations, federal laboratories (through professional development and collaboration), and industry partners involved in education and workforce pipelines.Additional impacts: Potential improvement in data literacy and computational thinking across the curriculum; enhanced teacher preparation and ongoing professional development; stronger connections between schooling, internships/jobs, and local workforce needs; increased capacity for evaluating and disseminating effective modeling-based teaching practices; movement toward problem-, project-, and performance-based learning with emphasis on real data, equity, and accessibility.
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