IIB 

117TH CONGRESS 

2D SESSION H. R. 3588 

IN THE SENATE OF THE UNITED STATES 

JULY 27, 2022 

Received; read twice and referred to the Committee on Health, Education, 

Labor, and Pensions 

AN ACT 

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. 


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

3 SECTION 1. SHORT TITLE. 

4 This Act may be cited as the ‘‘Mathematical and Sta5 tistical Modeling Education Act’’. 

6 SEC. 2. MATHEMATICAL AND STATISTICAL MODELING EDU7 CATION. 

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

9 (1) The mathematics taught in schools, includ10 ing statistical problem solving and data science, is 

11 not keeping pace with the rapidly evolving needs of 

12 the public and private sector, resulting in a STEM 

13 skills shortage and employers needing to expend re14 sources to train and upskill employees. 

15 (2) According to the Bureau of Labor Statis16 tics, the United States will need 1,000,000 addi17 tional STEM professionals than it is on track to 

18 produce in the coming decade. 

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

20 in almost every workplace, relies on the ability to 

21 work in teams and use computational tools to do 

22 mathematical and statistical problem solving. 

23 (4) Many STEM occupations offer higher 

24 wages, more opportunities for advancement, and a 

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


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1 (5) The STEM workforce relies on computa2 tional and data-driven discovery, decision making, 

3 and predictions, from models that often must quan4 tify uncertainty, as in weather predictions, spread of 

5 disease, or financial forecasting. 

6 (6) Most fields, including analytics, science, eco7 nomics, publishing, marketing, actuarial science, op8 erations research, engineering, and medicine, require 

9 data savvy, including the ability to select reliable 

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

11 recognize and quantify uncertainty in data, visualize 

12 and analyze data, and use data to develop under13 standing or make predictions. 

14 (7) Rapidly emerging fields, such as artificial 

15 intelligence, machine learning, quantum computing 

16 and quantum information, all rely on mathematical 

17 and statistical concepts, which are critical to prove 

18 under what circumstances an algorithm or experi19 ment will work and when it will fail. 

20 (8) Military academies have a long tradition in 

21 teaching mathematical modeling and would benefit 

22 from the ability to recruit students with this exper23 tise from their other school experiences. 

24 (9) Mathematical modeling has been a strong 

25 educational priority globally, especially in China, 


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1 where participation in United States mathematical 

2 modeling challenges in high school and higher edu3 cation is orders of magnitude higher than in the 

4 United States, and Chinese teams are taking a ma5 jority of the prizes. 

6 (10) Girls participate in mathematical modeling 

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

8 in traditional mathematical competitions girls par9 ticipate less and drop out at every stage. Students 

10 cite opportunity for teamwork, using mathematics 

11 and statistics in meaningful contexts, ability to use 

12 computation, and emphasis on communication as 

13 reasons for continued participation in modeling chal14 lenges. 

15 (b) DEFINITIONS.—In this section: 

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

17 the Director of the National Science Foundation. 

18 (2) FEDERAL LABORATORY.—The term ‘‘Fed19 eral laboratory’’ has the meaning given such term in 

20 section 4 of the Stevenson-Wydler Technology Inno21 vation Act of 1980 (15 U.S.C. 3703). 

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

23 means the National Science Foundation. 

24 (4) INSTITUTION OF HIGHER EDUCATION.—The 

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


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

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

3 (5) MATHEMATICAL MODELING.—The term 

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

5 term in the 2019 Guidelines to Assessment and In6 struction in Mathematical Modeling Education 

7 (GAIMME) report, 2nd edition. 

8 (6) OPERATIONS RESEARCH.—The term ‘‘oper9 ations research’’ means the application of scientific 

10 methods to the management and administration of 

11 organized military, governmental, commercial, and 

12 industrial processes to maximize operational effi13 ciency. 

14 (7) STATISTICAL MODELING.—The term ‘‘sta15 tistical modeling’’ has the meaning given the term in 

16 the 2021 Guidelines to Assessment and Instruction 

17 in Statistical Education (GAISE II) report. 

18 (8) STEM.—The term ‘‘STEM’’ means the aca19 demic and professional disciplines of science, tech20 nology, engineering, and mathematics. 

21 (c) PREPARING EDUCATORS TO ENGAGE STUDENTS

22 IN MATHEMATICAL AND STATISTICAL MODELING.—The 

23 Director shall provide grants on a merit-reviewed, com24 petitive basis to institutions of higher education, and non25 profit organizations (or a consortium thereof) for research 


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1 and development to advance innovative approaches to sup2 port and sustain high-quality mathematical modeling edu3 cation in schools operated by local education agencies, in4 cluding statistical modeling, data science, operations re5 search, and computational thinking. The Director shall en6 courage applicants to form partnerships to address critical 

7 transitions, such as middle school to high school, high 

8 school to college, and school to internships and jobs. 

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

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

11 in such manner, and containing such information as the 

12 Director may require. The application shall include the fol13 lowing: 

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

15 served by the research activity for which such grant 

16 is sought, including student subgroups described in 

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

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

19 6311(b)(2)(B)(xi)), and students experiencing home20 lessness and children and youth in foster care. 

21 (2) A description of the process for recruitment 

22 and selection of students, educators, or local edu23 cational agencies to participate in such research ac24 tivity. 


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1 (3) A description of how such research activity 

2 may inform efforts to promote the engagement and 

3 achievement of students in prekindergarten through 

4 grade 12 in mathematical modeling and statistical 

5 modeling using problem-based learning with contex6 tualized data and computational tools. 

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

8 partnership or partnerships with 1 or more local 

9 educational agencies and 1 or more researchers, a 

10 plan for establishing a sustained partnership that is 

11 jointly developed and managed, draws from the ca12 pacities of each partner, and is mutually beneficial. 

13 (e) PARTNERSHIPS.—In awarding grants under sub14 section (c), the Director shall encourage applications that 

15 include— 

16 (1) partnership with a nonprofit organization or 

17 an institution of higher education that has extensive 

18 experience and expertise in increasing the participa19 tion of students in prekindergarten through grade 

20 12 in mathematical modeling and statistical mod21 eling; 

22 (2) partnership with a local educational agency, 

23 a consortium of local educational agencies, or Tribal 

24 educational agencies; 


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1 (3) an assurance from school leaders to making 

2 reforms and activities proposed by the applicant a 

3 priority; 

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

5 middle school to high school, high school to college, 

6 and school to internships and jobs; 

7 (5) input from education researchers and cog8 nitive scientists, as well as practitioners in research 

9 and industry, so that what is being taught is up-to10 date in terms of content and pedagogy; 

11 (6) a communications strategy for early con12 versations with parents, school leaders, school 

13 boards, community members, employers, and other 

14 stakeholders; and 

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

16 boards, community members, and other stakeholders 

17 to build skills in modeling and analytics. 

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

19 under this section shall use the grant funds for research 

20 and development activities to advance innovative ap21 proaches to support and sustain high-quality mathe22 matical modeling education in public schools, including 

23 statistical modeling, data science, operations research, and 

24 computational thinking, which may include— 


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1 (1) engaging prekindergarten through grade 12 

2 educators in professional learning opportunities to 

3 enhance mathematical modeling and statistical prob4 lem solving knowledge, and developing training and 

5 best practices to provide more interdisciplinary 

6 learning opportunities; 

7 (2) conducting research on curricula and teach8 ing practices that empower students to choose the 

9 mathematical, statistical, computational, and techno10 logical tools that they will apply to a problem, as is 

11 required in life and the workplace, rather than pre12 scribing a particular approach or method; 

13 (3) providing students with opportunities to ex14 plore and analyze real data sets from contexts that 

15 are meaningful to the students, which may include— 

16 (A) missing or incorrect values; 

17 (B) quantities of data that require choice 

18 and use of appropriate technology; 

19 (C) multiple data sets that require choices 

20 about which data are relevant to the current 

21 problem; and 

22 (D) data of various types including quan23 tities, words, and images; 


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

2 professional development in mathematical modeling 

3 and statistical modeling; 

4 (5) engaging rural local agencies; 

5 (6) supporting research on effective mathe6 matical modeling and statistical modeling teaching 

7 practices, including problem- and project-based 

8 learning, universal design for accessibility, and ru9 brics and mastery-based grading practices to assess 

10 student performance; 

11 (7) designing and developing pre-service and in12 service training resources to assist educators in 

13 adopting transdisciplinary teaching practices within 

14 mathematics and statistics courses; 

15 (8) coordinating with local partners to adapt 

16 mathematics and statistics teaching practices to le17 verage local natural, business, industry, and commu18 nity assets in order to support community-based 

19 learning; 

20 (9) providing hands-on training and research 

21 opportunities for mathematics and statistics edu22 cators at Federal laboratories, institutions of higher 

23 education, or in industry; 

24 (10) developing mechanisms for partnerships 

25 between educators and employers to help educators 


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1 and students make connections between their mathe2 matics and statistics projects and topics of relevance 

3 in today’s world; 

4 (11) designing and implementing professional 

5 development courses and experiences, including men6 toring for educators, that combine face-to-face and 

7 online experiences; 

8 (12) addressing critical transitions, such as 

9 middle school to high school, high school to college, 

10 and school to internships and jobs; and 

11 (13) any other activity the Director determines 

12 will accomplish the goals of this section. 

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

14 this section shall include an evaluation plan that includes 

15 the use of outcome oriented measures to assess the impact 

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

17 this section shall include results from these evaluative ac18 tivities in annual and final projects. 

19 (h) ACCOUNTABILITY AND DISSEMINATION.— 

20 (1) EVALUATION REQUIRED.—The Director 

21 shall evaluate the portfolio of grants awarded under 

22 this section. Such evaluation shall— 

23 (A) use a common set of benchmarks and 

24 tools to assess the results of research conducted 


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1 under such grants and identify best practices; 

2 and 

3 (B) to the extent practicable, integrate the 

4 findings of research resulting from the activities 

5 funded through such grants with the findings of 

6 other research on student’s pursuit of degrees 

7 or careers in STEM. 

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

9 180 days after the completion of the evaluation 

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

11 Congress and make widely available to the public a 

12 report that includes— 

13 (A) the results of the evaluation; and 

14 (B) any recommendations for administra15 tive and legislative action that could optimize 

16 the effectiveness of the grants awarded under 

17 this section. 

18 (i) AUTHORIZATION OF APPROPRIATIONS.—For each 

19 of fiscal years 2023 through 2027, there are authorized 

20 to be appropriated to the National Science Foundation 

21 $10,000,000 to carry out the activities under this section. 


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1 SEC. 3. NASEM REPORT ON MATHEMATICAL AND STATIS2 TICAL MODELING EDUCATION IN PRE3 KINDERGARTEN THROUGH 12TH GRADE. 

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

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

6 into an agreement with the National Academies of 

7 Sciences, Engineering and Medicine (in this section re8 ferred to as ‘‘NASEM’’) (or if NASEM declines to enter 

9 into such an agreement, another appropriate entity) under 

10 which NASEM, or such other appropriate entity, agrees 

11 to conduct a study on the following: 

12 (1) Factors that enhance or barriers to the im13 plementation of mathematical modeling and statis14 tical modeling in elementary and secondary edu15 cation, including opportunities for and barriers to 

16 use modeling to integrate mathematical and statis17 tical ideas across the curriculum, including the fol18 lowing: 

19 (A) Pathways in mathematical modeling 

20 and statistical problem solving from kinder21 garten to the workplace so that students are 

22 able to identify opportunities to use their school 

23 mathematics and statistics in a variety of jobs 

24 and life situations and so that employers can 

25 benefit from students’ school learning of data 


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1 science, computational thinking, mathematics, 

2 statistics, and related subjects. 

3 (B) The role of community-based prob4 lems, service-based learning. and internships for 

5 connecting students with career preparatory ex6 periences. 

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

8 performance-based learning and assessment. 

9 (2) Characteristics of teacher education pro10 grams that successfully prepare teachers to engage 

11 students in mathematical modeling and statistical 

12 modeling, as well as gaps and suggestions for build13 ing capacity in the pre-service and in-service teacher 

14 workforce. 

15 (3) Mechanisms for communication with stake16 holders, including parents, administrators, and the 

17 public, to promote understanding and knowledge of 

18 the value of mathematical modeling and statistical 

19 modeling in education. 

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

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

22 NASEM or such other appropriate entity shall hold not 

23 less than one public meeting to obtain stakeholder input 

24 on the topics of such study. 


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

2 shall require NASEM, or such other appropriate entity, 

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

4 agreement, to submit to the Secretary of Education and 

5 the appropriate committees of jurisdiction of Congress a 

6 report containing— 

7 (1) the results of the study conducted under 

8 subsection (a); 

9 (2) recommendations to modernize the proc10 esses described in subsection (a)(1); and 

11 (3) recommendations for such legislative and 

12 administrative action as NASEM, or such other ap13 propriate entity, determines appropriate. 

14 (d) AUTHORIZATION OF APPROPRIATIONS.—For fis15 cal year 2023, there are authorized to be appropriated to 

16 the National Science Foundation $1,000,000 to carry out 

17 the activities under this section. 

Passed the House of Representatives July 26, 2022. 

Attest: CHERYL L. JOHNSON, 

Clerk. 


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