Sponsors (3)
The bill modestly invests federal funds to build evidence-based K–12 modeling, statistics, and data-science curricula and teacher capacity—improving readiness for data careers—while creating modest taxpayer cost and risks of uneven local implementation and funding uncertainty due to appropriation limits and a sunset date.
K–12 students (nationwide) would get clearer, research-backed curricula and pathways in mathematical modeling, statistics, and data science, improving college readiness and preparation for data-driven jobs.
Teachers and educator-preparation programs would receive funding, professional learning, and federal recommendations to strengthen pre-service and in-service instruction in modeling and statistics, improving teacher capacity to teach interdisciplinary, data-focused content.
Federal investment (authorized funding: $10M/year for R&D plus $1M/year for the national study) and a multi-year authorization through Sept 30, 2029 provide sustained, evidence-building support and short-term funding certainty for research, dissemination of best practices, and national guidance.
Taxpayers would underwrite ongoing costs (roughly $10M/year for R&D plus $1M/year for the study), which could displace other priorities in constrained budgets.
School districts—especially rural, small, or under-resourced ones—may struggle to participate or implement new curricula due to administrative burdens, capacity limits, broadband and partnership gaps, and the fact that awards are limited to institutions of higher education and nonprofits rather than districts.
Emphasis on studies, pilots, and a commissioned national study could delay scalable classroom changes; moreover, the study's recommendations are non-binding, so states and districts—particularly under-resourced ones—may not adopt them, limiting on-the-ground impact for students.
Based on analysis of 4 sections of legislative text.
Directs NSF to fund competitive R&D awards to improve mathematical and statistical modeling education and commissions a study on implementing modeling in preK–12.
Directs the National Science Foundation (NSF) to fund competitive, merit-reviewed research and development awards to colleges, universities, and nonprofit organizations to improve mathematical and statistical modeling education (including data science, operations research, and computational thinking) that can be used in K–12 school settings and to support transitions into higher education and the workforce. It also requires NSF to contract with the National Academies (or another entity) to study how to implement mathematical and statistical modeling across preK–12 education, hold public stakeholder input, and deliver a report with recommendations within 24 months. Authorizes $1 million per year for FY2026–2030 for the NSF Directorate for STEM Education to support the required study; directs that award funds come from NSF appropriations; and ends the authority to make the competitive awards on September 30, 2029. The law focuses on research, pilot programs, teacher preparation, and stakeholder engagement rather than imposing mandates on states or districts.
Introduced January 24, 2025 by Christina Houlahan · Last progress March 25, 2025