MATHEMATICAL AND STATISTICAL MODELING EDUCATION ACT

On 2025-03-24, Representative Chrissy Houlahan (D-PA-6) delivered a floor speech titled "MATHEMATICAL AND STATISTICAL MODELING EDUCATION ACT" in the House. The speech addressed the economy and also covered the environment, foreign policy. It referenced legislation: HR730.

MATHEMATICAL AND STATISTICAL MODELING EDUCATION ACT

Congressional Record, Volume 171 Issue 53 (Monday, March 24, 2025) [Congressional Record Volume 171, Number 53 (Monday, March 24, 2025)] [House] [Pages H1197-H1201] From the Congressional Record Online through the Government Publishing Office [ www.gpo.gov ] MATHEMATICAL AND STATISTICAL MODELING EDUCATION ACT Mr. BABIN. Mr. Speaker, I move to suspend the rules and pass the bill (H.R. 730) to coordinate Federal research and development efforts focused on modernizing mathematics in STEM education through mathematical and statistical modeling, including data-driven and computational thinking, problem, project, and performance-based learning and assessment, interdisciplinary exploration, and career connections, and for other purposes, as amended. The Clerk read the title of the bill. The text of the bill is as follows: H.R. 730 Be it enacted by the Senate and House of Representatives of the United States of America in Congress assembled, SECTION 1. SHORT TITLE. This Act may be cited as the ``Mathematical and Statistical Modeling Education Act''. SEC. 2. MATHEMATICAL AND STATISTICAL MODELING EDUCATION. (a) Findings.--Congress finds the following: (1) The mathematics taught in schools, including statistical problem solving and data science, is not keeping pace with the rapidly evolving needs of the public and private sector, resulting in a STEM skills shortage and employers needing to expend resources to train and upskill employees. (2) According to the Bureau of Labor Statistics, the United States will need 1,000,000 [[Page H1198]] additional STEM professionals than it is on track to produce in the coming decade. (3) The field of data science, which is relevant in almost every workplace, relies on the ability to work in teams and use computational tools to do mathematical and statistical problem solving. (4) Many STEM occupations offer higher wages, more opportunities for advancement, and a higher degree of job security than non-STEM jobs. (5) The STEM workforce relies on computational and data- driven discovery, decision making, and predictions, from models that often must quantify uncertainty, as in weather predictions, spread of disease, or financial forecasting. (6) Most fields, including analytics, science, economics, publishing, marketing, actuarial science, operations research, engineering, and medicine, require data savvy, including the ability to select reliable sources of data, identify and remove errors in data, recognize and quantify uncertainty in data, visualize and analyze data, and use data to develop understanding or make predictions. (7) Rapidly emerging fields, such as artificial intelligence, machine learning, quantum computing and quantum information, all rely on mathematical and statistical concepts, which are critical to prove under what circumstances an algorithm or experiment will work and when it will fail. (8) Military academies have a long tradition in teaching mathematical modeling and would benefit from the ability to recruit students with this expertise from their other school experiences. (9) Mathematical modeling has been a strong educational priority globally, especially in China, where participation in United States mathematical modeling challenges in high school and higher education is orders of magnitude higher than in the United States, and Chinese teams are taking a majority of the prizes. (10) Girls participate in mathematical modeling challenges at all levels at similar levels as boys, while in traditional mathematical competitions girls participate less and drop out at every stage. Students cite opportunity for teamwork, using mathematics and statistics in meaningful contexts, ability to use computation, and emphasis on communication as reasons for continued participation in modeling challenges. (b) Definitions.--In this section: (1) Director.--The term ``Director'' means the Director of the National Science Foundation. (2) Federal laboratory.--The term ``Federal laboratory'' has the meaning given such term in section 4 of the Stevenson-Wydler Technology Innovation Act of 1980 (15 U.S.C. 3703). (3) Foundation.--The term ``Foundation'' means the National Science Foundation. (4) Institution of higher education.--The term ``institution of higher education'' has the meaning given such term in section 101(a) of the Higher Education Act of 1965 (20 U.S.C. 1001(a)). (5) Mathematical modeling.--The term ``mathematical modeling'' has the meaning given such term in the 2019 Guidelines to Assessment and Instruction in Mathematical Modeling Education (GAIMME) report, 2nd edition. (6) Operations research.--The term ``operations research'' means the application of scientific methods to the management and administration of organized military, governmental, commercial, and industrial processes to maximize operational efficiency. (7) Statistical modeling.--The term ``statistical modeling'' has the meaning given such term in the 2021 Guidelines to Assessment and Instruction in Statistical Education (GAISE II) report. (8) STEM.--The term ``STEM'' means the academic and professional disciplines of science, technology, engineering, and mathematics, including computer science. (c) Preparing Educators To Engage Students in Mathematical and Statistical Modeling.--The Director shall make awards on a merit-reviewed, competitive basis to institutions of higher education and nonprofit organizations (or a consortium thereof) for research and development to advance innovative approaches to support and sustain high-quality mathematical modeling education in schools that are operated by local educational agencies, including statistical modeling, data science, operations research, and computational thinking. The Director shall encourage applicants to form partnerships to address critical transitions, such as middle school to high school, high school to college, and school to internships and jobs. (d) Application.--An entity seeking an award under subsection (c) shall submit an application at such time, in such manner, and containing such information as the Director may require. The application shall include the following: (1) A description of the target population to be served by the research activity for which such an award is sought, including student subgroups described in section 1111(b)(2)(B)(xi) of the Elementary and Secondary Education Act of 1965 (20 U.S.C. 6311(b)(2)(B)(xi)), and students experiencing homelessness and children and youth in foster care. (2) A description of the process for recruitment and selection of students, educators, or local educational agencies to participate in such research activity. (3) A description of how such research activity may inform efforts to promote the engagement and achievement of students, including students from groups historically underrepresented in STEM, in prekindergarten through grade 12 in mathematical modeling and statistical modeling using problem-based learning with contextualized data and computational tools. (4) In the case of a proposal consisting of a partnership or partnerships with one or more local educational agencies and one or more researchers, a plan for establishing a sustained partnership that is jointly developed and managed, draws from the capacities of each partner, and is mutually beneficial. (e) Partnerships.--In making awards under subsection (c), the Director shall encourage applications that include the following: (1) Partnership with a nonprofit organization or an institution of higher education that has extensive experience and expertise in increasing the participation of students in prekindergarten through grade 12 in mathematical modeling and statistical modeling. (2) Partnership with a local educational agency, a consortium of local educational agencies, or Tribal educational agencies. (3) An assurance from school leaders to making reforms and activities proposed by the applicant a priority. (4) Ways to address critical transitions, such as middle school to high school, high school to college, and school to internships and jobs. (5) Input from education researchers and cognitive scientists, as well as practitioners in research and industry, so that what is being taught is up-to-date in terms of content and pedagogy. (6) A communications strategy for early conversations with parents, school leaders, school boards, community members, employers, and other stakeholders. (7) Resources for parents, school leaders, school boards, community members, and other stakeholders to build skills in modeling and analytics. (f) Use of Funds.--An entity that receives an award under this section shall use the award for research and development activities to advance innovative approaches to support and sustain high-quality mathematical modeling education in public schools, including statistical modeling, data science, operations research, and computational thinking, which may include the following: (1) Engaging prekindergarten through grade 12 educators in professional learning opportunities to enhance mathematical modeling and statistical problem solving knowledge, and developing training and best practices to provide more interdisciplinary learning opportunities. (2) Conducting research on curricula and teaching practices that empower students to choose the mathematical, statistical, computational, and technological tools they will apply to a problem, as is required in life and the workplace, rather than prescribing a particular approach or method. (3) Providing students with opportunities to explore and analyze real data sets from contexts that are meaningful to the students, which may include the following: (A) Missing or incorrect values. (B) Quantities of data that require choice and use of appropriate technology. (C) Multiple data sets that require choices about which data are relevant to the current problem. (D) Data of various types including quantities, words, and images. (4) Taking a school or district-wide approach to professional 

Referenced legislation: HR730, HR730