II 

118TH CONGRESS 

1ST SESSION S. 2739 

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. 

IN THE SENATE OF THE UNITED STATES 

SEPTEMBER 7, 2023 

Ms. HASSAN (for herself and Mrs. BLACKBURN) introduced the following bill; 

which was read twice and referred to the Committee on Health, Education, Labor, and Pensions 

A BILL 

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. 

1 Be it enacted by the Senate and House of Representa2 tives of the United States of America in Congress assembled, 


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1 SECTION 1. SHORT TITLE. 

2 This Act may be cited as the ‘‘Mathematical and Sta3 tistical Modeling Education Act’’. 

4 SEC. 2. MATHEMATICAL AND STATISTICAL MODELING EDU5 CATION. 

6 (a) FINDINGS.—Congress finds the following: 

7 (1) The mathematics taught in schools, includ8 ing statistical problem solving and data science, is 

9 not keeping pace with the rapidly evolving needs of 

10 the public and private sector, resulting in a STEM 

11 skills shortage and employers needing to expend re12 sources to train and upskill employees. 

13 (2) According to the Bureau of Labor Statis14 tics, the United States will need 1,000,000 addi15 tional STEM professionals than it is on track to 

16 produce in the coming decade. 

17 (3) The field of data science, which is relevant 

18 in almost every workplace, relies on the ability to 

19 work in teams and use computational tools to do 

20 mathematical and statistical problem solving. 

21 (4) Many STEM occupations offer higher 

22 wages, more opportunities for advancement, and a 

23 higher degree of job security than non-STEM jobs. 

24 (5) The STEM workforce relies on computa25 tional and data-driven discovery, decision-making, 

26 and predictions, from models that often must quanVerDate Sep 11 2014 22:19 Sep 07, 2023 Jkt 039200 PO 00000 Frm 00002 Fmt 6652 Sfmt 6201 E:\BILLS\S2739.IS S2739

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1 tify uncertainty, as in weather predictions, spread of 

2 disease, or financial forecasting. 

3 (6) Most fields, including analytics, science, eco4 nomics, publishing, marketing, actuarial science, op5 erations research, engineering, and medicine, require 

6 data savvy, including the ability to select reliable 

7 sources of data, identify and remove errors in data, 

8 recognize and quantify uncertainty in data, visualize 

9 and analyze data, and use data to develop under10 standing or make predictions. 

11 (7) Rapidly emerging fields, such as artificial 

12 intelligence, machine learning, quantum computing 

13 and quantum information, all rely on mathematical 

14 and statistical concepts, which are critical to prove 

15 under what circumstances an algorithm or experi16 ment will work and when it will fail. 

17 (8) Military academies have a long tradition in 

18 teaching mathematical modeling and would benefit 

19 from the ability to recruit students with this exper20 tise from their other school experiences. 

21 (9) Mathematical modeling has been a strong 

22 educational priority globally, especially in China, 

23 where participation in United States mathematical 

24 modeling challenges in high school and higher edu25 cation is orders of magnitude higher than in the 


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1 United States, and Chinese teams are taking a ma2 jority of the prizes. 

3 (10) Girls participate in mathematical modeling 

4 challenges at all levels at similar levels as boys, while 

5 in traditional mathematical competitions girls par6 ticipate less and drop out at every stage. Students 

7 cite opportunity for teamwork, using mathematics 

8 and statistics in meaningful contexts, ability to use 

9 computation, and emphasis on communication as 

10 reasons for continued participation in modeling chal11 lenges. 

12 (b) DEFINITIONS.—In this section: 

13 (1) DIRECTOR.—The term ‘‘Director’’ means 

14 the Director of the National Science Foundation. 

15 (2) FEDERAL LABORATORY.—The term ‘‘Fed16 eral laboratory’’ has the meaning given such term in 

17 section 4 of the Stevenson-Wydler Technology Inno18 vation Act of 1980 (15 U.S.C. 3703). 

19 (3) FOUNDATION.—The term ‘‘Foundation’’ 

20 means the National Science Foundation. 

21 (4) INSTITUTION OF HIGHER EDUCATION.—The 

22 term ‘‘institution of higher education’’ has the 

23 meaning given such term in section 101(a) of the 

24 Higher Education Act of 1965 (20 U.S.C. 1001(a)). 


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1 (5) MATHEMATICAL MODELING.—The term 

2 ‘‘mathematical modeling’’ has the meaning given the 

3 term in the 2019 Guidelines for Assessment and In4 struction in Mathematical Modeling Education 

5 (GAIMME) report, 2nd edition. 

6 (6) OPERATIONS RESEARCH.—The term ‘‘oper7 ations research’’ means the application of scientific 

8 methods to the management and administration of 

9 organized military, governmental, commercial, and 

10 industrial processes to maximize operational effi11 ciency. 

12 (7) STATISTICAL MODELING.—The term ‘‘sta13 tistical modeling’’ has the meaning given the term in 

14 the 2021 Guidelines for Assessment and Instruction 

15 in Statistical Education II (GAISE II) report. 

16 (8) STEM.—The term ‘‘STEM’’ means the aca17 demic and professional disciplines of science, tech18 nology, engineering, and mathematics. 

19 (c) PREPARING EDUCATORS TO ENGAGE STUDENTS

20 IN MATHEMATICAL AND STATISTICAL MODELING.—The 

21 Director shall provide grants on a merit-reviewed, com22 petitive basis to institutions of higher education and non23 profit organizations (or a consortium thereof) for research 

24 and development to advance innovative approaches to sup25 port and sustain high-quality mathematical modeling eduVerDate Sep 11 2014 01:35 Sep 08, 2023 Jkt 039200 PO 00000 Frm 00005 Fmt 6652 Sfmt 6201 E:\BILLS\S2739.IS S2739

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1 cation in schools operated by local educational agencies, 

2 including statistical problem solving, data science, oper3 ations research, and computational thinking. The Director 

4 shall encourage applicants to form partnerships to address 

5 critical transitions, such as middle school to high school, 

6 high school to postsecondary education, and school to in7 ternships and jobs. 

8 (d) APPLICATION.—An entity seeking a grant under 

9 subsection (c) shall submit an application at such time, 

10 in such manner, and containing such information as the 

11 Director may require. The application shall include the fol12 lowing: 

13 (1) A description of the target population to be 

14 served by the research activity for which such grant 

15 is sought, including student subgroups described in 

16 section 1111(b)(2)(B)(xi) of the Elementary and 

17 Secondary Education Act of 1965 (20 U.S.C. 

18 6311(b)(2)(B)(xi)), and students experiencing home19 lessness and children and youth in foster care. 

20 (2) A description of the process for recruitment 

21 and selection of students, educators, or local edu22 cational agencies to participate in such research ac23 tivity. 

24 (3) A description of how such research activity 

25 may inform efforts to promote the engagement and 


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1 achievement of students in prekindergarten through 

2 grade 12 in mathematical modeling and statistical 

3 modeling using problem-based learning with 

4 contextualized data and computational tools. 

5 (4) In the case of a proposal consisting of a 

6 partnership or partnerships with 1 or more local 

7 educational agencies and 1 or more researchers, a 

8 plan for establishing a sustained partnership that is 

9 jointly developed and managed, draws from the ca10 pacities of each partner, and is mutually beneficial. 

11 (e) PARTNERSHIPS.—In awarding grants under sub12 section (c), the Director shall encourage applications that 

13 include— 

14 (1) partnership with a nonprofit organization or 

15 an institution of higher education that has extensive 

16 experience and expertise in increasing the participa17 tion of students in prekindergarten through grade 

18 12 in mathematical modeling and statistical mod19 eling; 

20 (2) partnership with a local educational agency, 

21 consortium of local educational agencies, or Tribal 

22 educational agencies; 

23 (3) an assurance from school leaders to making 

24 reforms and activities proposed by the applicant a 

25 priority; 


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1 (4) ways to address critical transitions, such as 

2 middle school to high school, high school to postsec3 ondary education, and school to internships and 

4 jobs; 

5 (5) input from education researchers and cog6 nitive scientists, as well as practitioners in research 

7 and industry, so that what is being taught is up-to8 date in terms of content and pedagogy; 

9 (6) a communications strategy for early con10 versations with parents, school leaders, school 

11 boards, community members, employers, and other 

12 stakeholders; and 

13 (7) resources for parents, school leaders, school 

14 boards, community members, and other stakeholders 

15 to build skills in modeling and analytics. 

16 (f) USE OF FUNDS.—An entity that receives a grant 

17 under this section shall use the grant funds for research 

18 and development activities to advance innovative ap19 proaches to support and sustain high-quality mathe20 matical modeling education in public schools, including 

21 statistical modeling, data science, operations research, and 

22 computational thinking, which may include— 

23 (1) engaging prekindergarten through grade 12 

24 educators in professional learning opportunities to 

25 enhance mathematical modeling and statistical probVerDate Sep 11 2014 22:19 Sep 07, 2023 Jkt 039200 PO 00000 Frm 00008 Fmt 6652 Sfmt 6201 E:\BILLS\S2739.IS S2739

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1 lem solving knowledge, and developing training and 

2 best practices to provide more interdisciplinary 

3 learning opportunities; 

4 (2) conducting research on curricula and teach5 ing practices that empower students to choose the 

6 mathematical, statistical, computational, and techno7 logical tools that they will apply to a problem, as is 

8 required in life and the workplace, rather than pre9 scribing a particular approach or method; 

10 (3) providing students with opportunities to ex11 plore and analyze real data sets from contexts that 

12 are meaningful to the students, which may include— 

13 (A) missing or incorrect values; 

14 (B) quantities of data that require choice 

15 and use of appropriate technology; 

16 (C) multiple data sets that require choices 

17 about which data are relevant to the current 

18 problem; and 

19 (D) data of various types including quan20 tities, words, and images; 

21 (4) taking a school or district-wide approach to 

22 professional development in mathematical modeling 

23 and statistical modeling; 

24 (5) engaging rural local educational agencies; 


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1 (6) supporting research on effective mathe2 matical modeling and statistical modeling teaching 

3 practices, including problem- and project-based 

4 learning, universal design for accessibility, and ru5 brics and mastery-based grading practices to assess 

6 student performance; 

7 (7) designing and developing pre-service and in8 service training resources to assist educators in 

9 adopting transdisciplinary teaching practices within 

10 mathematics and statistics courses; 

11 (8) coordinating with local partners to adapt 

12 mathematics and statistics teaching practices to le13 verage local natural, business, industry, and commu14 nity assets in order to support community-based 

15 learning; 

16 (9) providing hands-on training and research 

17 opportunities for mathematics and statistics edu18 cators at Federal laboratories or institutions of high19 er education, or in industry; 

20 (10) developing mechanisms for partnerships 

21 between educators and employers to help educators 

22 and students make connections between their mathe23 matics and statistics projects and topics of relevance 

24 in today’s world; 


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1 (11) designing and implementing professional 

2 development courses and experiences, including men3 toring for educators, that combine face-to-face and 

4 online experiences; 

5 (12) addressing critical transitions, such as 

6 middle school to high school, high school to postsec7 ondary education, and school to internships and 

8 jobs; and 

9 (13) any other activity the Director determines 

10 will accomplish the goals of this section. 

11 (g) EVALUATIONS.—All proposals for grants under 

12 this section shall include an evaluation plan that includes 

13 the use of outcome-oriented measures to assess the impact 

14 and efficacy of the grant. Each recipient of a grant under 

15 this section shall include results from these evaluative ac16 tivities in annual and final projects. 

17 (h) ACCOUNTABILITY AND DISSEMINATION.— 

18 (1) EVALUATION REQUIRED.—The Director 

19 shall evaluate the portfolio of grants awarded under 

20 this section. Such evaluation shall— 

21 (A) use a common set of benchmarks and 

22 tools to assess the results of research conducted 

23 under such grants and identify best practices; 

24 and 


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1 (B) to the extent practicable, integrate the 

2 findings of research resulting from the activities 

3 funded through such grants with the findings of 

4 other research on student’s pursuit of degrees 

5 or careers in STEM. 

6 (2) REPORT ON EVALUATIONS.—Not later than 

7 180 days after the completion of the evaluation 

8 under paragraph (1), the Director shall submit to 

9 Congress and make widely available to the public a 

10 report that includes— 

11 (A) the results of the evaluation; and 

12 (B) any recommendations for administra13 tive and legislative action that could optimize 

14 the effectiveness of the grants awarded under 

15 this section. 

16 (i) AUTHORIZATION OF APPROPRIATIONS.—For each 

17 of fiscal years 2024 through 2028, there are authorized 

18 out of funds appropriated to the National Science Founda19 tion, $10,000,000 to carry out the activities under this 

20 section. 

21 SEC. 3. NASEM REPORT ON MATHEMATICAL AND STATIS22 TICAL MODELING EDUCATION IN PRE23 KINDERGARTEN THROUGH GRADE 12. 

24 (a) STUDY.—Not later than 60 days after the date 

25 of enactment of this Act, the Director shall seek to enter 


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1 into an agreement with the National Academies of 

2 Sciences, Engineering and Medicine (in this section re3 ferred to as ‘‘NASEM’’) (or if NASEM declines to enter 

4 into such an agreement, another appropriate entity) under 

5 which NASEM, or such other appropriate entity, agrees 

6 to conduct a study on the following: 

7 (1) Factors that enhance, or barriers to, the 

8 implementation of mathematical modeling and sta9 tistical modeling in elementary and secondary edu10 cation, including opportunities for, and barriers to, 

11 use modeling to integrate mathematical and statis12 tical ideas across the curriculum, including the fol13 lowing: 

14 (A) Pathways in mathematical modeling 

15 and statistical problem solving from kinder16 garten to the workplace so that students are 

17 able to identify opportunities to use their school 

18 mathematics and statistics in a variety of jobs 

19 and life situations and so that employers can 

20 benefit from students’ school learning of data 

21 science, computational thinking, mathematics, 

22 statistics, and related subjects. 

23 (B) The role of community-based prob24 lems, service-based learning, and internships for 


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1 connecting students with career preparatory ex2 periences. 

3 (C) Best practices in problem-, project-, 

4 and performance-based learning and assess5 ment. 

6 (2) Characteristics of teacher education pro7 grams that successfully prepare teachers to engage 

8 students in mathematical modeling and statistical 

9 modeling, as well as gaps and suggestions for build10 ing capacity in the pre-service and in-service teacher 

11 workforce. 

12 (3) Mechanisms for communication with stake13 holders, including parents, administrators, and the 

14 public, to promote understanding and knowledge of 

15 the value of mathematical modeling and statistical 

16 modeling in education. 

17 (b) PUBLIC STAKEHOLDER MEETING.—In the course 

18 of completing the study described in subsection (a), 

19 NASEM or such other appropriate entity shall hold not 

20 less than one public meeting to obtain stakeholder input 

21 on the topics of such study. 

22 (c) REPORT.—The agreement under subsection (a) 

23 shall require NASEM, or such other appropriate entity, 

24 not later than 24 months after the effective date of such 

25 agreement, to submit to the Secretary of Education and 


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1 the appropriate committees of jurisdiction of Congress a 

2 report containing— 

3 (1) the results of the study conducted under 

4 subsection (a); 

5 (2) recommendations to modernize the proc6 esses described in subsection (a)(1); and 

7 (3) recommendations for such legislative and 

8 administrative action as NASEM, or such other ap9 propriate entity, determines appropriate. 

10 (d) AUTHORIZATION OF APPROPRIATIONS.—For fis11 cal year 2024, there are authorized out of funds appro12 priated to the National Science Foundation, $1,000,000 

13 to carry out the activities under this section. 

Æ 


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