[Congressional Record Volume 170, Number 148 (Monday, September 23, 2024)]
[House]
[Pages H5638-H5641]
From the Congressional Record Online through the Government Publishing Office [www.gpo.gov]




          MATHEMATICAL AND STATISTICAL MODELING EDUCATION ACT

  Mr. LUCAS. Mr. Speaker, I move to suspend the rules and pass the bill 
(H.R. 1735) 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. 1735

       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 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

[[Page H5639]]

     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 the 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 the 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 private, faith-based, 
     or homeschools, or 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 1 or more local educational agencies and 
     1 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--
       (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; and
       (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, private schools (including faith-based 
     schools), or homeschools, including statistical modeling, 
     data science, operations research, and computational 
     thinking, which may include--
       (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 that 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--
       (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; and
       (D) data of various types including quantities, words, and 
     images;
       (4) taking a school or district-wide approach to 
     professional development in mathematical modeling and 
     statistical modeling;
       (5) engaging rural local agencies;
       (6) supporting research on effective mathematical modeling 
     and statistical modeling teaching practices, including 
     problem- and project-based learning, universal design for 
     accessibility, and rubrics and mastery-based grading 
     practices to assess student performance;
       (7) designing and developing pre-service and in-service 
     training resources to assist educators in adopting 
     transdisciplinary teaching practices within mathematics and 
     statistics courses;
       (8) coordinating with local partners to adapt mathematics 
     and statistics teaching practices to leverage local natural, 
     business, industry, and community assets in order to support 
     community-based learning;
       (9) providing hands-on training and research opportunities 
     for mathematics and statistics educators at Federal 
     laboratories, institutions of higher education, or in 
     industry;
       (10) developing mechanisms for partnerships between 
     educators and employers to help educators and students make 
     connections between their mathematics and statistics projects 
     and topics of relevance in today's world;
       (11) designing and implementing professional development 
     courses and experiences, including mentoring for educators, 
     that combine face-to-face and online experiences;
       (12) reduce gaps in access to learning opportunities for 
     students from groups historically underrepresented in STEM;
       (13) provide support and resources for students from groups 
     historically underrepresented in STEM;
       (14) addressing critical transitions, such as middle school 
     to high school, high school to college, and school to 
     internships and jobs;
       (15) researching effective approaches for engaging students 
     from groups historically underrepresented in STEM; and
       (16) any other activity the Director determines will 
     accomplish the goals of this section.
       (g) Evaluations.--All proposals for awards under this 
     section shall include an evaluation plan that includes the 
     use of outcome oriented measures to assess the impact and 
     efficacy of the award. Each recipient of an award under this 
     section shall include results from these evaluative 
     activities in annual and final project reports.
       (h) Accountability and Dissemination.--
       (1) Evaluation required.--The Director shall evaluate the 
     portfolio of awards made under this section. Such evaluation 
     shall--
       (A) use a common set of benchmarks and tools to assess the 
     results of research conducted under such awards and identify 
     best practices; and
       (B) to the extent practicable, integrate the findings of 
     research resulting from the activities funded through such 
     awards with the findings of other research on student's 
     pursuit of degrees or careers in STEM.
       (2) Report on evaluations.--Not later than 180 days after 
     the completion of the evaluation under paragraph (1), the 
     Director shall submit to Congress and make widely available 
     to the public a report that includes--
       (A) the results of the evaluation; and
       (B) any recommendations for administrative and legislative 
     action that could optimize the effectiveness of the awards 
     made under this section.
       (i) Funding.--$10,000,000 for each of the fiscal years 2025 
     through 2029 is authorized to be used by the Directorate for 
     STEM Education of the National Science Foundation to carry 
     out this section.

     SEC. 3. NASEM REPORT ON MATHEMATICAL AND STATISTICAL MODELING 
                   EDUCATION IN PREKINDERGARTEN THROUGH 12TH 
                   GRADE.

       (a) Study.--Not later than 180 days after the date of the 
     enactment of this Act, the Director shall seek to enter into 
     an agreement with the National Academies of Sciences, 
     Engineering and Medicine (in this section referred to as 
     ``NASEM'') (or if NASEM declines to enter into such an 
     agreement, another appropriate entity) under

[[Page H5640]]

     which NASEM, or such other appropriate entity, agrees to 
     conduct a study on the following:
       (1) Factors that enhance or barriers to the implementation 
     of mathematical modeling and statistical modeling in 
     elementary and secondary education, including opportunities 
     for and barriers to use modeling to integrate mathematical 
     and statistical ideas across the curriculum, including the 
     following:
       (A) Pathways in mathematical modeling and statistical 
     problem solving from kindergarten to the workplace so that 
     students are able to identify opportunities to use their 
     school mathematics and statistics in a variety of jobs and 
     life situations and so that employers can benefit from 
     students' school learning of data science, computational 
     thinking, mathematics, statistics, and related subjects.
       (B) The role of community-based problems, service-based 
     learning. and internships for connecting students with career 
     preparatory experiences.
       (C) Best practices in problem-, project-, performance-based 
     learning and assessment.
       (2) Characteristics of teacher education programs that 
     successfully prepare teachers to engage students in 
     mathematical modeling and statistical modeling, as well as 
     gaps and suggestions for building capacity in the pre-service 
     and in-service teacher workforce.
       (3) Mechanisms for communication with stakeholders, 
     including parents, administrators, and the public, to promote 
     understanding and knowledge of the value of mathematical 
     modeling and statistical modeling in education.
       (b) Public Stakeholder Meeting.--In the course of 
     completing the study described in subsection (a), NASEM or 
     such other appropriate entity shall hold not less than one 
     public meeting to obtain stakeholder input on the topics of 
     such study.
       (c) Report.--The agreement under subsection (a) shall 
     require NASEM, or such other appropriate entity, not later 
     than 24 months after the effective date of such agreement, to 
     submit to the Secretary of Education and the appropriate 
     committees of jurisdiction of Congress a report containing--
       (1) the results of the study conducted under subsection 
     (a);
       (2) recommendations to modernize the processes described in 
     subsection (a)(1); and
       (3) recommendations for such legislative and administrative 
     action as NASEM, or such other appropriate entity, determines 
     appropriate.
       (d) Funding.--From amounts appropriated or otherwise made 
     available for the Directorate for STEM Education of the 
     National Science Foundation, the Director shall allocate up 
     to $1,000,000 for fiscal year 2024 to carry out this section.

     SEC. 4. LIMITATIONS.

       (a) Limitation on Funding.--Amounts made available to carry 
     out sections 2 and 3 shall be derived from amounts 
     appropriated or otherwise made available to the National 
     Science Foundation.
       (b) Sunset.--The authority to provide awards under this Act 
     shall expire on September 30, 2028.

  The SPEAKER pro tempore. Pursuant to the rule, the gentleman from 
Oklahoma (Mr. Lucas) and the gentlewoman from California (Ms. Lofgren) 
each will control 20 minutes.
  The Chair recognizes the gentleman from Oklahoma.


                             General Leave

  Mr. LUCAS. Mr. Speaker, I ask unanimous consent that all Members may 
have 5 legislative days in which to revise and extend their remarks and 
include extraneous material on H.R. 1735, the bill now under 
consideration.
  The SPEAKER pro tempore. Is there objection to the request of the 
gentleman from Oklahoma?
  There was no objection.
  Mr. LUCAS. Mr. Speaker, I yield myself such time as I may consume.
  Mr. Speaker, I am proud to support H.R. 1735, the Mathematical and 
Statistical Modeling Education Act, sponsored by my colleagues 
Representatives Houlahan and Baird.
  Anyone who pays attention to the economy knows the importance of STEM 
education. The Bureau of Labor Statistics predicts that our need for 
STEM jobs will increase by nearly 11 percent by 2032.
  To fill those jobs and ensure that we have a highly productive 
workforce, we need to focus on STEM education in our schools. We need 
to be sure that we are providing a useful STEM education.
  We know that many STEM jobs require data-driven decisionmaking, but 
we are not providing a grounding in that skill in our schools. To this 
end, H.R. 1735 modernizes our mathematics curriculum by providing 
competitive grants to support education in mathematical and statistical 
modeling.
  As someone trained in agricultural economics, I understand the value 
of conducting mathematical and statistical analysis. A solid grounding 
in these fields helps us predict crop yields, identify the effects of 
temperatures and water levels, and model commodity markets. Those are 
just examples from a single industry.
  The skills are crucial to a wide variety of professions and help 
inform computational and data-driven thinking. Statistical analysis 
underpins everything, from developing artificial intelligence to 
improving advanced manufacturing.
  This bill would allow us to better teach these critical skills 
through R&D into new curricula and teaching methods. It would also 
direct the National Academies to conduct a study that will identify 
best practices for mathematical and statistical modeling education.
  I thank Representatives Houlahan and Baird for their work on this 
issue over the past several years. This is smart legislation that 
supports our students and our economy.
  Mr. Speaker, I urge my colleagues to pass it today, and I reserve the 
balance of my time.
  Ms. LOFGREN. Mr. Speaker, I yield myself such time as I may consume.
  Mr. Speaker, this bill is an important one, the Mathematical and 
Statistical Modeling Education Act, represented by two really 
impressive Members of Congress, Ms. Houlahan and Dr. Baird.
  This bill would direct the National Science Foundation to invest in 
K-12 education research and development, with a focus on mathematical 
modeling.
  This bill would help modernize STEM education by supporting the 
creation of advanced curricula that cover mathematical and statistical 
modeling, including computational and data-driven thinking.
  This is the kind of education our students deserve and what they need 
for today's society and its technological and data-driven demands.
  Members and staff have worked really hard to accommodate many desired 
changes to this bill, and I believe it is in a state now that should 
satisfy just about everyone. It has support from the premier 
educational, statistical, and mathematical organizations in the United 
States. The Senate companion bill is in a similar good place. It is 
time for this bill to become law.
  This is an important bill that will better prepare our students for 
careers in STEM, and I hope all of us will join together and support 
this bill.
  Mr. Speaker, I reserve the balance of my time.
  Mr. LUCAS. Mr. Speaker, I yield such time as he may consume to the 
gentleman from Indiana (Mr. Baird) to speak on his bill.
  Mr. BAIRD. Mr. Speaker, I thank Congresswoman Houlahan for her 
dedication to improving STEM education and for being a great partner to 
work with on advancing this bill.
  Mr. Speaker, the Mathematical and Statistical Modeling Education Act 
provides a much-needed solution to improving the quality of STEM 
education in America. This bill would advance mathematical instruction 
in our classrooms by incorporating modern tools and context, with data 
and computational studies.
  Mathematical modeling is taught today, but on a limited basis. Even 
so, mathematical modeling is the foundation for the important work of 
our Nation when it comes to research, development, and, ultimately, 
technological innovation.
  While the bill directs the National Science Foundation to grant 
awards to our institutions of higher education, the bill does not award 
any new funding. We must work with the resources we already have.
  As an animal scientist, I understand the life-changing effects proper 
mathematical modeling can have on our livestock, our food, and, 
ultimately, our Nation's well-being.
  That is why I am proud to co-lead this bipartisan legislation to 
ensure that the United States continues to dominate in STEM education, 
and I encourage all of my colleagues to vote ``yes'' for this bill.
  Ms. LOFGREN. Mr. Speaker, I yield myself such time as I may consume.
  Mr. Speaker, I am so pleased to serve in the Congress with 
Congresswoman Houlahan, who has such a spectacular background in 
science. She is an engineer, former business executive, former teacher, 
former nonprofit executive, and an Air Force vet. She has it all. 
Working with Dr. Baird, she put together this bill that is so 
important.

[[Page H5641]]

  Mr. Speaker, I yield such time as she may consume to the gentlewoman 
from Pennsylvania (Ms. Houlahan).

                              {time}  2015

  Ms. HOULAHAN. Mr. Speaker, I thank Ranking Member Lofgren for 
yielding me the time to speak on behalf of my bill, H.R. 1735, and I 
thank so much my colleague, Representative Baird, for his tireless 
efforts to help us get this across the finish line.
  It is past time that America's K-12 students enter the 21st century, 
and this Mathematical and Statistical Modeling Education Act is a 
bipartisan bill that will help with that and will direct $10 million in 
funding, importantly, funding that is already appropriated to the 
National Science Foundation, toward grant programs, and it will support 
the modernization of mathematical and statistical modeling education 
across this fine Nation.
  As someone who is educated as a systems engineer, with both 
undergraduate and graduate degrees, which focused on things like 
operations research and linear programming and statistical modeling, I 
have had the privilege of using all of these kinds of maths all during 
my career and life.
  Also, as a former high school chemistry teacher, I saw firsthand that 
many of my students struggled because they lacked the basic foundations 
in math that are necessary to succeed in the sciences, and frankly, to 
succeed in our economy.
  These math skills form the basis of all the STEM disciplines, and 
importantly, they also form the basis of critical thinking and problem-
solving as well. Without them, students struggle to keep up, let alone 
to get ahead not just in math and science but ultimately in our 
competitive workplace and world.
  The National Assessment of Educational Progress, which is 
colloquially known as the Nation's report card, has shown that this is 
a national challenge. The most recent assessment registered the very 
largest declines in math scores since we first started assessing them 
in 1990. The scores of our average fourth graders were down five 
points. Worse, the average score of our eighth graders recorded an 
eight-point lower assessment than the last time.
  As our students are continuing to recover from the pandemic, now 
particularly is a very good time to focus on a renewed and modified 
investment in math and STEM education and skills.
  If our students can't get ahead in school, how will they get ahead in 
the STEM workforce as engineers, as chemists, as nurses, as doctors, 
and so much more?
  As importantly, how will those who do not pursue STEM fields succeed 
where these same kinds of skills are very, very useful in things such 
as the trades and manufacturing or any other job in industry?
  Thankfully, schools across the country are already developing new 
tools and curricula to better connect students and help them learn 
these challenging topics.
  It is crucial though that the Federal Government deliver its 
financial support to schools that are already leading this effort and 
that want to in the future.
  My colleagues also supported an amendment to the bill ensuring that 
this funding is broadly available, so students can benefit from it no 
matter what kind of school they go to. Their adjustment made sure that 
schools which are private, faith-based, or homeschools also have access 
to this funding.
  I also want to highlight that this funding is all drawn from funds 
that are already appropriated to the National Science Foundation. This 
legislation does not represent any new funding authorizations or 
authorities. It is simply ensuring that the NSF is able to spend the 
money it already receives to bolster innovation in this very important 
area.
  This bill will go a long way towards providing our very youngest 
people with the skills that they need to succeed in today's 
marketplace. It passed with a strong bipartisan majority in the 117th 
Congress and was passed unanimously out of the Committee on Science, 
Space, and Technology last year in this Congress as well.
  I once again thank my Republican colleague, Mr. Baird, for his work 
on this legislation. I also extend my deep appreciation to the SST 
staff and committee who have helped to shepherd this legislation 
through today.
  Mr. Speaker, I urge all of my colleagues on both sides of the aisle 
to support this very commonsense, bipartisan measure.
  Ms. LOFGREN. Mr. Speaker, I have no additional requests for time. I 
would just like to once again thank Congresswoman Houlahan, Dr. Baird, 
and I also thank the chairman. We have had a great run here in the 
Science Committee this evening with these terrific bills, which I urge 
all Members to support.
  Mr. Speaker, I yield back the balance of my time.
  Mr. LUCAS. Mr. Speaker, I have no additional speakers, and I yield 
myself the balance of my time.
  I encourage my colleagues to vote for this awesome bill worked on in 
a very productive fashion by our colleagues.
  I would note, Mr. Speaker, we have had a very productive session in 
the 118th Congress. I thought under the previous committee leadership 
the two previous sessions it would be hard to beat, but we have 
accomplished a lot, and we have laid the groundwork for whatever 
remains of the 118th to tie up a whole bunch of loose ends. That is a 
testament to the gentlewoman and her staff, and I think to my staff and 
my colleagues on my side of the room.
  The issues we work on, as the gentlewoman and I have discussed many 
times, are not just today and tomorrow. It is 50 years, it is 150 
years, it is 500 years from now, the net effect.
  Mr. Speaker, I yield back the balance of my time.
  The SPEAKER pro tempore. The question is on the motion offered by the 
gentleman from Oklahoma (Mr. Lucas) that the House suspend the rules 
and pass the bill, H.R. 1735, as amended.
  The question was taken; and (two-thirds being in the affirmative) the 
rules were suspended and the bill, as amended, was passed.
  A motion to reconsider was laid on the table.

                          ____________________