HR 730 RFS
Be it enacted by the Senate and House of Representa-
tives of the United States of America in Congress assembled, 
SECTION 1. SHORT TITLE. 
This Act may be cited as the ‘‘Mathematical and Sta-
tistical Modeling Education Act’’. 
SEC. 2. MATHEMATICAL AND STATISTICAL MODELING EDU-
CATION. 
(a) FINDINGS.—Congress finds the following: 
(1) The mathematics taught in schools, includ-
ing 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 re-
sources to train and upskill employees. 
(2) According to the Bureau of Labor Statis-
tics, the United States will need 1,000,000 addi-
tional 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. 
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(5) The STEM workforce relies on computa-
tional and data-driven discovery, decision making, 
and predictions, from models that often must quan-
tify uncertainty, as in weather predictions, spread of 
disease, or financial forecasting. 
(6) Most fields, including analytics, science, eco-
nomics, publishing, marketing, actuarial science, op-
erations 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 under-
standing 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 experi-
ment 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 exper-
tise from their other school experiences. 
(9) Mathematical modeling has been a strong 
educational priority globally, especially in China, 
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where participation in United States mathematical 
modeling challenges in high school and higher edu-
cation is orders of magnitude higher than in the 
United States, and Chinese teams are taking a ma-
jority of the prizes. 
(10) Girls participate in mathematical modeling 
challenges at all levels at similar levels as boys, while 
in traditional mathematical competitions girls par-
ticipate 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 chal-
lenges. 
(b) DEFINITIONS.—In this section: 
(1) DIRECTOR.—The term ‘‘Director’’ means 
the Director of the National Science Foundation. 
(2) FEDERAL LABORATORY.—The term ‘‘Fed-
eral laboratory’’ has the meaning given such term in 
section 4 of the Stevenson-Wydler Technology Inno-
vation 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 
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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 ‘‘oper-
ations research’’ means the application of scientific 
methods to the management and administration of 
organized military, governmental, commercial, and 
industrial processes to maximize operational effi-
ciency. 
(7) STATISTICAL MODELING.—The term ‘‘sta-
tistical modeling’’ has the meaning given such term 
in the 2021 Guidelines to Assessment and Instruc-
tion 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, competi-
tive basis to institutions of higher education and nonprofit 
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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 re-
search, and computational thinking. The Director shall en-
courage 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 de-
scribed in section 1111(b)(2)(B)(xi) of the Elemen-
tary and Secondary Education Act of 1965 (20 
U.S.C. 6311(b)(2)(B)(xi)), and students experi-
encing homelessness and children and youth in fos-
ter care. 
(2) A description of the process for recruitment 
and selection of students, educators, or local edu-
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cational agencies to participate in such research ac-
tivity. 
(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 com-
putational 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 ca-
pacities of each partner, and is mutually beneficial. 
(e) PARTNERSHIPS.—In making awards under sub-
section (c), the Director shall encourage applications that 
include the following: 
(1) Partnership with a nonprofit organization 
or an institution of higher education that has exten-
sive experience and expertise in increasing the par-
ticipation of students in prekindergarten through 
grade 12 in mathematical modeling and statistical 
modeling. 
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(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 cog-
nitive 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 con-
versations 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 ap-
proaches to support and sustain high-quality mathe-
matical modeling education in public schools, including 
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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 prob-
lem solving knowledge, and developing training and 
best practices to provide more interdisciplinary 
learning opportunities. 
(2) Conducting research on curricula and teach-
ing practices that empower students to choose the 
mathematical, statistical, computational, and techno-
logical tools they will apply to a problem, as is re-
quired in life and the workplace, rather than pre-
scribing a particular approach or method. 
(3) Providing students with opportunities to ex-
plore 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. 
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(D) Data of various types including quan-
tities, 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 mathe-
matical modeling and statistical modeling teaching 
practices, including problem- and project-based 
learning, universal design for accessibility, and ru-
brics 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 le-
verage local natural, business, industry, and commu-
nity assets in order to support community-based 
learning. 
(9) Providing hands-on training and research 
opportunities for mathematics and statistics edu-
cators at Federal laboratories, institutions of higher 
education, or in industry. 
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(10) Developing mechanisms for partnerships 
between educators and employers to help educators 
and students make connections between their mathe-
matics and statistics projects and topics of relevance 
in today’s world. 
(11) Designing and implementing professional 
development courses and experiences, including men-
toring for educators, that combine face-to-face and 
online experiences. 
(12) Reducing gaps in access to learning oppor-
tunities for students from groups historically under-
represented in STEM. 
(13) Providing support and resources for stu-
dents 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 en-
gaging students from groups historically underrep-
resented in STEM. 
(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 
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