I
119THCONGRESS
1
STSESSION H. R. 730
To coordinate Federal research and development efforts focused on modern-
izing mathematics in STEM education through mathematical and statis-
tical modeling, including data-driven and computational thinking, prob-
lem, project, and performance-based learning and assessment, inter-
disciplinary exploration, and career connections, and for other purposes.
IN THE HOUSE OF REPRESENTATIVES
JANUARY24, 2025
Ms. H
OULAHAN(for herself and Mr. BAIRD) introduced the following bill;
which was referred to the Committee on Science, Space, and Technology
A BILL
To coordinate Federal research and development efforts fo-
cused 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.
Be it enacted by the Senate and House of Representa-1
tives of the United States of America in Congress assembled, 2
SECTION 1. SHORT TITLE. 3
This Act may be cited as the ‘‘Mathematical and Sta-4
tistical Modeling Education Act’’. 5
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SEC. 2. MATHEMATICAL AND STATISTICAL MODELING EDU-1
CATION. 2
(a) F
INDINGS.—Congress finds the following: 3
(1) The mathematics taught in schools, includ-4
ing statistical problem solving and data science, is 5
not keeping pace with the rapidly evolving needs of 6
the public and private sector, resulting in a STEM 7
skills shortage and employers needing to expend re-8
sources to train and upskill employees. 9
(2) According to the Bureau of Labor Statis-10
tics, the United States will need 1,000,000 addi-11
tional STEM professionals than it is on track to 12
produce in the coming decade. 13
(3) The field of data science, which is relevant 14
in almost every workplace, relies on the ability to 15
work in teams and use computational tools to do 16
mathematical and statistical problem solving. 17
(4) Many STEM occupations offer higher 18
wages, more opportunities for advancement, and a 19
higher degree of job security than non-STEM jobs. 20
(5) The STEM workforce relies on computa-21
tional and data-driven discovery, decision making, 22
and predictions, from models that often must quan-23
tify uncertainty, as in weather predictions, spread of 24
disease, or financial forecasting. 25
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(6) Most fields, including analytics, science, eco-1
nomics, publishing, marketing, actuarial science, op-2
erations research, engineering, and medicine, require 3
data savvy, including the ability to select reliable 4
sources of data, identify and remove errors in data, 5
recognize and quantify uncertainty in data, visualize 6
and analyze data, and use data to develop under-7
standing or make predictions. 8
(7) Rapidly emerging fields, such as artificial 9
intelligence, machine learning, quantum computing 10
and quantum information, all rely on mathematical 11
and statistical concepts, which are critical to prove 12
under what circumstances an algorithm or experi-13
ment will work and when it will fail. 14
(8) Military academies have a long tradition in 15
teaching mathematical modeling and would benefit 16
from the ability to recruit students with this exper-17
tise from their other school experiences. 18
(9) Mathematical modeling has been a strong 19
educational priority globally, especially in China, 20
where participation in United States mathematical 21
modeling challenges in high school and higher edu-22
cation is orders of magnitude higher than in the 23
United States, and Chinese teams are taking a ma-24
jority of the prizes. 25
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(10) Girls participate in mathematical modeling 1
challenges at all levels at similar levels as boys, while 2
in traditional mathematical competitions girls par-3
ticipate less and drop out at every stage. Students 4
cite opportunity for teamwork, using mathematics 5
and statistics in meaningful contexts, ability to use 6
computation, and emphasis on communication as 7
reasons for continued participation in modeling chal-8
lenges. 9
(b) D
EFINITIONS.—In this section: 10
(1) D
IRECTOR.—The term ‘‘Director’’ means 11
the Director of the National Science Foundation. 12
(2) F
EDERAL LABORATORY .—The term ‘‘Fed-13
eral laboratory’’ has the meaning given such term in 14
section 4 of the Stevenson-Wydler Technology Inno-15
vation Act of 1980 (15 U.S.C. 3703). 16
(3) F
OUNDATION.—The term ‘‘Foundation’’ 17
means the National Science Foundation. 18
(4) I
NSTITUTION OF HIGHER EDUCATION .—The 19
term ‘‘institution of higher education’’ has the 20
meaning given such term in section 101(a) of the 21
Higher Education Act of 1965 (20 U.S.C. 1001(a)). 22
(5) M
ATHEMATICAL MODELING .—The term 23
‘‘mathematical modeling’’ has the meaning given 24
such term in the 2019 Guidelines to Assessment and 25
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Instruction in Mathematical Modeling Education 1
(GAIMME) report, 2nd edition. 2
(6) O
PERATIONS RESEARCH .—The term ‘‘oper-3
ations research’’ means the application of scientific 4
methods to the management and administration of 5
organized military, governmental, commercial, and 6
industrial processes to maximize operational effi-7
ciency. 8
(7) S
TATISTICAL MODELING .—The term ‘‘sta-9
tistical modeling’’ has the meaning given such term 10
in the 2021 Guidelines to Assessment and Instruc-11
tion in Statistical Education (GAISE II) report. 12
(8) STEM.—The term ‘‘STEM’’ means the 13
academic and professional disciplines of science, 14
technology, engineering, and mathematics, including 15
computer science. 16
(c) P
REPARINGEDUCATORSTOENGAGESTUDENTS 17
INMATHEMATICAL AND STATISTICALMODELING.—The 18
Director shall make awards on a merit-reviewed, competi-19
tive basis to institutions of higher education and nonprofit 20
organizations (or a consortium thereof) for research and 21
development to advance innovative approaches to support 22
and sustain high-quality mathematical modeling education 23
in schools that are operated by local educational agencies, 24
including statistical modeling, data science, operations re-25
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search, and computational thinking. The Director shall en-1
courage applicants to form partnerships to address critical 2
transitions, such as middle school to high school, high 3
school to college, and school to internships and jobs. 4
(d) A
PPLICATION.—An entity seeking an award 5
under subsection (c) shall submit an application at such 6
time, in such manner, and containing such information as 7
the Director may require. The application shall include the 8
following: 9
(1) A description of the target population to be 10
served by the research activity for which such an 11
award is sought, including student subgroups de-12
scribed in section 1111(b)(2)(B)(xi) of the Elemen-13
tary and Secondary Education Act of 1965 (20 14
U.S.C. 6311(b)(2)(B)(xi)), and students experi-15
encing homelessness and children and youth in fos-16
ter care. 17
(2) A description of the process for recruitment 18
and selection of students, educators, or local edu-19
cational agencies to participate in such research ac-20
tivity. 21
(3) A description of how such research activity 22
may inform efforts to promote the engagement and 23
achievement of students, including students from 24
groups historically underrepresented in STEM, in 25
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prekindergarten through grade 12 in mathematical 1
modeling and statistical modeling using problem- 2
based learning with contextualized data and com-3
putational tools. 4
(4) In the case of a proposal consisting of a 5
partnership or partnerships with one or more local 6
educational agencies and one or more researchers, a 7
plan for establishing a sustained partnership that is 8
jointly developed and managed, draws from the ca-9
pacities of each partner, and is mutually beneficial. 10
(e) P
ARTNERSHIPS.—In making awards under sub-11
section (c), the Director shall encourage applications that 12
include the following: 13
(1) Partnership with a nonprofit organization 14
or an institution of higher education that has exten-15
sive experience and expertise in increasing the par-16
ticipation of students in prekindergarten through 17
grade 12 in mathematical modeling and statistical 18
modeling. 19
(2) Partnership with a local educational agency, 20
a consortium of local educational agencies, or Tribal 21
educational agencies. 22
(3) An assurance from school leaders to making 23
reforms and activities proposed by the applicant a 24
priority. 25
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(4) Ways to address critical transitions, such as 1
middle school to high school, high school to college, 2
and school to internships and jobs. 3
(5) Input from education researchers and cog-4
nitive scientists, as well as practitioners in research 5
and industry, so that what is being taught is up-to- 6
date in terms of content and pedagogy. 7
(6) A communications strategy for early con-8
versations with parents, school leaders, school 9
boards, community members, employers, and other 10
stakeholders. 11
(7) Resources for parents, school leaders, school 12
boards, community members, and other stakeholders 13
to build skills in modeling and analytics. 14
(f) U
SE OFFUNDS.—An entity that receives an 15
award under this section shall use the award for research 16
and development activities to advance innovative ap-17
proaches to support and sustain high-quality mathe-18
matical modeling education in public schools, including 19
statistical modeling, data science, operations research, and 20
computational thinking, which may include the following: 21
(1) Engaging prekindergarten through grade 12 22
educators in professional learning opportunities to 23
enhance mathematical modeling and statistical prob-24
lem solving knowledge, and developing training and 25
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best practices to provide more interdisciplinary 1
learning opportunities. 2
(2) Conducting research on curricula and teach-3
ing practices that empower students to choose the 4
mathematical, statistical, computational, and techno-5
logical tools they will apply to a problem, as is re-6
quired in life and the workplace, rather than pre-7
scribing a particular approach or method. 8
(3) Providing students with opportunities to ex-9
plore and analyze real data sets from contexts that 10
are meaningful to the students, which may include 11
the following: 12
(A) Missing or incorrect values. 13
(B) Quantities of data that require choice 14
and use of appropriate technology. 15
(C) Multiple data sets that require choices 16
about which data are relevant to the current 17
problem. 18
(D) Data of various types including quan-19
tities, words, and images. 20
(4) Taking a school or district-wide approach to 21
professional development in mathematical modeling 22
and statistical modeling. 23
(5) Engaging rural local agencies. 24
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(6) Supporting research on effective mathe-1
matical modeling and statistical modeling teaching 2
practices, including problem- and project-based 3
learning, universal design for accessibility, and ru-4
brics and mastery-based grading practices to assess 5
student performance. 6
(7) Designing and developing pre-service and 7
in-service training resources to assist educators in 8
adopting transdisciplinary teaching practices within 9
mathematics and statistics courses. 10
(8) Coordinating with local partners to adapt 11
mathematics and statistics teaching practices to le-12
verage local natural, business, industry, and commu-13
nity assets in order to support community-based 14
learning. 15
(9) Providing hands-on training and research 16
opportunities for mathematics and statistics edu-17
cators at Federal laboratories, institutions of higher 18
education, or in industry. 19
(10) Developing mechanisms for partnerships 20
between educators and employers to help educators 21
and students make connections between their mathe-22
matics and statistics projects and topics of relevance 23
in today’s world. 24
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(11) Designing and implementing professional 1
development courses and experiences, including men-2
toring for educators, that combine face-to-face and 3
online experiences. 4
(12) Reducing gaps in access to learning oppor-5
tunities for students from groups historically under-6
represented in STEM. 7
(13) Providing support and resources for stu-8
dents from groups historically underrepresented in 9
STEM. 10
(14) Addressing critical transitions, such as 11
middle school to high school, high school to college, 12
and school to internships and jobs. 13
(15) Researching effective approaches for en-14
gaging students from groups historically underrep-15
resented in STEM. 16
(16) Any other activity the Director determines 17
will accomplish the goals of this section. 18
(g) E
VALUATIONS.—All proposals for awards under 19
this section shall include an evaluation plan that includes 20
the use of outcome oriented measures to assess the impact 21
and efficacy of the award. Each recipient of an award 22
under this section shall include results from such evalua-23
tive activities in annual and final project reports. 24
(h) A
CCOUNTABILITY AND DISSEMINATION.— 25
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(1) EVALUATION REQUIRED .—The Director 1
shall evaluate the portfolio of awards made under 2
this section. Such evaluation shall— 3
(A) use a common set of benchmarks and 4
tools to assess the results of research conducted 5
under such awards and identify best practices; 6
and 7
(B) to the extent practicable, integrate the 8
findings of research resulting from the activities 9
funded through such awards with the findings 10
of other research on student’s pursuit of de-11
grees or careers in STEM. 12
(2) R
EPORT ON EVALUATIONS .—Not later than 13
180 days after the completion of the evaluation 14
under paragraph (1), the Director shall submit to 15
Congress and make widely available to the public a 16
report that includes the following: 17
(A) The results of the evaluation. 18
(B) Any recommendations for administra-19
tive and legislative action that could optimize 20
the effectiveness of the awards made under this 21
section. 22
(i) F
UNDING.—From amounts appropriated or other-23
wise made available for the Directorate for STEM Edu-24
cation of the National Science Foundation, the Director 25
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shall allocate $10,000,000 for each of the fiscal years 1
2026 through 2030 to carry out this section. 2
SEC. 3. NASEM REPORT ON MATHEMATICAL AND STATIS-3
TICAL MODELING EDUCATION IN PRE-4
KINDERGARTEN THROUGH 12TH GRADE. 5
(a) S
TUDY.—Not later than 180 days after the date 6
of the enactment of this Act, the Director shall seek to 7
enter into an agreement with the National Academies of 8
Sciences, Engineering and Medicine (in this section re-9
ferred to as ‘‘NASEM’’) (or if NASEM declines to enter 10
into such an agreement, another appropriate entity) under 11
which NASEM, or such other appropriate entity, agrees 12
to conduct a study on the following: 13
(1) Factors that enhance or barriers to the im-14
plementation of mathematical modeling and statis-15
tical modeling in elementary and secondary edu-16
cation, including opportunities for and barriers to 17
use modeling to integrate mathematical and statis-18
tical ideas across the curriculum, including the fol-19
lowing: 20
(A) Pathways in mathematical modeling 21
and statistical problem solving from kinder-22
garten to the workplace so students are able to 23
identify opportunities to use their school mathe-24
matics and statistics in a variety of jobs and 25
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life situations and so employers can benefit 1
from students’ school learning of data science, 2
computational thinking, mathematics, statistics, 3
and related subjects. 4
(B) The role of community-based prob-5
lems, service-based learning. and internships for 6
connecting students with career preparatory ex-7
periences. 8
(C) Best practices in problem-, project-, 9
performance-based learning and assessment. 10
(2) Characteristics of teacher education pro-11
grams that successfully prepare teachers to engage 12
students in mathematical modeling and statistical 13
modeling, as well as gaps and suggestions for build-14
ing capacity in the pre-service and in-service teacher 15
workforce. 16
(3) Mechanisms for communication with stake-17
holders, including parents, administrators, and the 18
public, to promote understanding and knowledge of 19
the value of mathematical modeling and statistical 20
modeling in education. 21
(b) P
UBLICSTAKEHOLDERMEETING.—In the course 22
of completing the study described in subsection (a), 23
NASEM or such other appropriate entity shall hold not 24
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fewer than one public meeting to obtain stakeholder input 1
on the topics of such study. 2
(c) R
EPORT.—The agreement under subsection (a) 3
shall require NASEM, or such other appropriate entity, 4
not later than 24 months after the effective date of such 5
agreement, to submit to the Director, the Secretary of 6
Education, and the Congress a report containing the fol-7
lowing: 8
(1) The results of the study conducted under 9
subsection (a). 10
(2) Recommendations to modernize the proc-11
esses described in subsection (a)(1). 12
(3) Recommendations for such legislative and 13
administrative action as NASEM, or such other ap-14
propriate entity, determines appropriate. 15
(d) F
UNDING.—From amounts appropriated or oth-16
erwise made available for the Directorate for STEM Edu-17
cation of the National Science Foundation, the Director 18
shall allocate up to $1,000,000 for fiscal year 2026 to 19
carry out this section. 20
SEC. 4. LIMITATIONS. 21
(a) L
IMITATION ONFUNDING.—Amounts made avail-22
able to carry out sections 2 and 3 shall be derived from 23
amounts appropriated or otherwise made available to the 24
National Science Foundation. 25
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(b) SUNSET.—The authority to provide awards under 1
this Act shall expire on September 30, 2029. 2
Æ
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