What This Bill Does
This bill directs the National Science Foundation to fund research and development projects that teach students mathematical modeling and statistical modeling in kindergarten through 12th grade. The bill aims to modernize mathematics education by including data science, computational thinking, and real-world problem solving.
##
Who It Affects
* Kindergarten through 12th grade students and teachers
* Public schools, private schools, faith-based schools, and homeschools
* Colleges and universities
* Nonprofit organizations
* Federal laboratories
* School districts and tribal educational agencies
* Employers in STEM fields
##
Key Provisions
* The Director of the National Science Foundation must award funding on a competitive basis to colleges, nonprofits, and school partnerships to research and develop mathematical and statistical modeling education methods (Sec. 2(c))
* Organizations receiving awards must include evaluation plans with outcome-focused measures to assess the impact of the projects and report results annually (Sec. 2(g))
* The Director must evaluate all funded projects using common benchmarks and tools, then submit a report to Congress within 180 days after the evaluation concludes with recommendations for improving the program (Sec. 2(h))
* The Director must enter into an agreement with the National Academies of Sciences, Engineering and Medicine to conduct a study on barriers and successful practices in teaching mathematical and statistical modeling, with a report due within 24 months (Sec. 3(a) and 3(c))
* Up to $10,000,000 per year is authorized for fiscal years 2025 through 2029 for the National Science Foundation's STEM Education Directorate to carry out the bill (Sec. 2(i))
##
What Changes
Schools and educational organizations can now compete for federal funding to develop new approaches to teaching mathematical and statistical modeling. The National Science Foundation will conduct research on which teaching methods work best. Within 24 months, a major study will be completed examining barriers to implementing these subjects and best practices for teacher training. The federal government will make specific data available to inform decisions about mathematics education modernization.
##
Important Definitions
* **STEM** - Science, technology, engineering, and mathematics, including computer science (Sec. 2(b)(8))
* **Mathematical Modeling** - Uses the definition from the 2019 Guidelines to Assessment and Instruction in Mathematical Modeling Education (GAIMME) report, 2nd edition (Sec. 2(b)(5))
* **Statistical Modeling** - Uses the definition from the 2021 Guidelines to Assessment and Instruction in Statistical Education (GAISE II) report (Sec. 2(b)(7))
* **Operations Research** - The application of scientific methods to manage military, governmental, commercial, and industrial processes to maximize efficiency (Sec. 2(b)(6))
* **Federal Laboratory** - Has the meaning given in section 4 of the Stevenson-Wydler Technology Innovation Act of 1980 (Sec. 2(b)(2))
##
Effective Date
The authority to provide awards expires on September 30, 2028 (Sec. 4(b)). Not specified in bill text for when the law itself takes effect.
IIB
118TH CONGRESS
2D SESSION
H. R. 1735
IN THE SENATE OF THE UNITED STATES
SEPTEMBER 24, 2024
Received; read twice and referred to the Committee on Health, Education,
Labor, and Pensions
AN ACT
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,
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SECTION 1. SHORT TITLE.
1
This Act may be cited as the ‘‘Mathematical and Sta-
2
tistical Modeling Education Act’’.
3
SEC. 2. MATHEMATICAL AND STATISTICAL MODELING EDU-
4
CATION.
5
(a) FINDINGS.—Congress finds the following:
6
(1) The mathematics taught in schools, includ-
7
ing statistical problem solving and data science, is
8
not keeping pace with the rapidly evolving needs of
9
the public and private sector, resulting in a STEM
10
skills shortage and employers needing to expend re-
11
sources to train and upskill employees.
12
(2) According to the Bureau of Labor Statis-
13
tics, the United States will need 1,000,000 addi-
14
tional STEM professionals than it is on track to
15
produce in the coming decade.
16
(3) The field of data science, which is relevant
17
in almost every workplace, relies on the ability to
18
work in teams and use computational tools to do
19
mathematical and statistical problem solving.
20
(4) Many STEM occupations offer higher
21
wages, more opportunities for advancement, and a
22
higher degree of job security than non-STEM jobs.
23
(5) The STEM workforce relies on computa-
24
tional and data-driven discovery, decision making,
25
and predictions, from models that often must quan-
26
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tify uncertainty, as in weather predictions, spread of
1
disease, or financial forecasting.
2
(6) Most fields, including analytics, science, eco-
3
nomics, publishing, marketing, actuarial science, op-
4
erations research, engineering, and medicine, require
5
data savvy, including the ability to select reliable
6
sources of data, identify and remove errors in data,
7
recognize and quantify uncertainty in data, visualize
8
and analyze data, and use data to develop under-
9
standing or make predictions.
10
(7) Rapidly emerging fields, such as artificial
11
intelligence, machine learning, quantum computing
12
and quantum information, all rely on mathematical
13
and statistical concepts, which are critical to prove
14
under what circumstances an algorithm or experi-
15
ment will work and when it will fail.
16
(8) Military academies have a long tradition in
17
teaching mathematical modeling and would benefit
18
from the ability to recruit students with this exper-
19
tise from their other school experiences.
20
(9) Mathematical modeling has been a strong
21
educational priority globally, especially in China,
22
where participation in United States mathematical
23
modeling challenges in high school and higher edu-
24
cation is orders of magnitude higher than in the
25
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United States, and Chinese teams are taking a ma-
1
jority of the prizes.
2
(10) Girls participate in mathematical modeling
3
challenges at all levels at similar levels as boys, while
4
in traditional mathematical competitions girls par-
5
ticipate less and drop out at every stage. Students
6
cite opportunity for teamwork, using mathematics
7
and statistics in meaningful contexts, ability to use
8
computation, and emphasis on communication as
9
reasons for continued participation in modeling chal-
10
lenges.
11
(b) DEFINITIONS.—In this section:
12
(1) DIRECTOR.—The term ‘‘Director’’ means
13
the Director of the National Science Foundation.
14
(2) FEDERAL LABORATORY.—The term ‘‘Fed-
15
eral laboratory’’ has the meaning given such term in
16
section 4 of the Stevenson-Wydler Technology Inno-
17
vation Act of 1980 (15 U.S.C. 3703).
18
(3) FOUNDATION.—The term ‘‘Foundation’’
19
means the National Science Foundation.
20
(4) INSTITUTION OF HIGHER EDUCATION.—The
21
term ‘‘institution of higher education’’ has the
22
meaning given such term in section 101(a) of the
23
Higher Education Act of 1965 (20 U.S.C. 1001(a)).
24
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(5) MATHEMATICAL
MODELING.—The term
1
‘‘mathematical modeling’’ has the meaning given the
2
term in the 2019 Guidelines to Assessment and In-
3
struction in Mathematical Modeling Education
4
(GAIMME) report, 2nd edition.
5
(6) OPERATIONS RESEARCH.—The term ‘‘oper-
6
ations research’’ means the application of scientific
7
methods to the management and administration of
8
organized military, governmental, commercial, and
9
industrial processes to maximize operational effi-
10
ciency.
11
(7) STATISTICAL MODELING.—The term ‘‘sta-
12
tistical modeling’’ has the meaning given the term in
13
the 2021 Guidelines to Assessment and Instruction
14
in Statistical Education (GAISE II) report.
15
(8) STEM.—The term ‘‘STEM’’ means the aca-
16
demic and professional disciplines of science, tech-
17
nology, engineering, and mathematics, including
18
computer science.
19
(c) PREPARING EDUCATORS TO ENGAGE STUDENTS
20
IN MATHEMATICAL AND STATISTICAL MODELING.—The
21
Director shall make awards on a merit-reviewed, competi-
22
tive basis to institutions of higher education, and nonprofit
23
organizations (or a consortium thereof) for research and
24
development to advance innovative approaches to support
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and sustain high-quality mathematical modeling education
1
in schools that are private, faith-based, or homeschools,
2
or operated by local educational agencies, including statis-
3
tical modeling, data science, operations research, and com-
4
putational thinking. The Director shall encourage appli-
5
cants to form partnerships to address critical transitions,
6
such as middle school to high school, high school to col-
7
lege, and school to internships and jobs.
8
(d) APPLICATION.—An entity seeking an award
9
under subsection (c) shall submit an application at such
10
time, in such manner, and containing such information as
11
the Director may require. The application shall include the
12
following:
13
(1) A description of the target population to be
14
served by the research activity for which such an
15
award is sought, including student subgroups de-
16
scribed in section 1111(b)(2)(B)(xi) of the Elemen-
17
tary and Secondary Education Act of 1965 (20
18
U.S.C. 6311(b)(2)(B)(xi)), and students experi-
19
encing homelessness and children and youth in fos-
20
ter care.
21
(2) A description of the process for recruitment
22
and selection of students, educators, or local edu-
23
cational agencies to participate in such research ac-
24
tivity.
25
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(3) A description of how such research activity
1
may inform efforts to promote the engagement and
2
achievement of students, including students from
3
groups historically underrepresented in STEM, in
4
prekindergarten through grade 12 in mathematical
5
modeling and statistical modeling using problem-
6
based learning with contextualized data and com-
7
putational tools.
8
(4) In the case of a proposal consisting of a
9
partnership or partnerships with 1 or more local
10
educational agencies and 1 or more researchers, a
11
plan for establishing a sustained partnership that is
12
jointly developed and managed, draws from the ca-
13
pacities of each partner, and is mutually beneficial.
14
(e) PARTNERSHIPS.—In making awards under sub-
15
section (c), the Director shall encourage applications that
16
include—
17
(1) partnership with a nonprofit organization or
18
an institution of higher education that has extensive
19
experience and expertise in increasing the participa-
20
tion of students in prekindergarten through grade
21
12 in mathematical modeling and statistical mod-
22
eling;
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(2) partnership with a local educational agency,
1
a consortium of local educational agencies, or Tribal
2
educational agencies;
3
(3) an assurance from school leaders to making
4
reforms and activities proposed by the applicant a
5
priority;
6
(4) ways to address critical transitions, such as
7
middle school to high school, high school to college,
8
and school to internships and jobs;
9
(5) input from education researchers and cog-
10
nitive scientists, as well as practitioners in research
11
and industry, so that what is being taught is up-to-
12
date in terms of content and pedagogy;
13
(6) a communications strategy for early con-
14
versations with parents, school leaders, school
15
boards, community members, employers, and other
16
stakeholders; and
17
(7) resources for parents, school leaders, school
18
boards, community members, and other stakeholders
19
to build skills in modeling and analytics.
20
(f) USE
OF FUNDS.—An entity that receives an
21
award under this section shall use the award for research
22
and development activities to advance innovative ap-
23
proaches to support and sustain high-quality mathe-
24
matical modeling education in public schools, private
25
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schools (including faith-based schools), or homeschools, in-
1
cluding statistical modeling, data science, operations re-
2
search, and computational thinking, which may include—
3
(1) engaging prekindergarten through grade 12
4
educators in professional learning opportunities to
5
enhance mathematical modeling and statistical prob-
6
lem solving knowledge, and developing training and
7
best practices to provide more interdisciplinary
8
learning opportunities;
9
(2) conducting research on curricula and teach-
10
ing practices that empower students to choose the
11
mathematical, statistical, computational, and techno-
12
logical tools that they will apply to a problem, as is
13
required in life and the workplace, rather than pre-
14
scribing a particular approach or method;
15
(3) providing students with opportunities to ex-
16
plore and analyze real data sets from contexts that
17
are meaningful to the students, which may include—
18
(A) missing or incorrect values;
19
(B) quantities of data that require choice
20
and use of appropriate technology;
21
(C) multiple data sets that require choices
22
about which data are relevant to the current
23
problem; and
24
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(D) data of various types including quan-
1
tities, words, and images;
2
(4) taking a school or district-wide approach to
3
professional development in mathematical modeling
4
and statistical modeling;
5
(5) engaging rural local agencies;
6
(6) supporting research on effective mathe-
7
matical modeling and statistical modeling teaching
8
practices, including problem- and project-based
9
learning, universal design for accessibility, and ru-
10
brics and mastery-based grading practices to assess
11
student performance;
12
(7) designing and developing pre-service and in-
13
service training resources to assist educators in
14
adopting transdisciplinary teaching practices within
15
mathematics and statistics courses;
16
(8) coordinating with local partners to adapt
17
mathematics and statistics teaching practices to le-
18
verage local natural, business, industry, and commu-
19
nity assets in order to support community-based
20
learning;
21
(9) providing hands-on training and research
22
opportunities for mathematics and statistics edu-
23
cators at Federal laboratories, institutions of higher
24
education, or in industry;
25
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(10) developing mechanisms for partnerships
1
between educators and employers to help educators
2
and students make connections between their mathe-
3
matics and statistics projects and topics of relevance
4
in today’s world;
5
(11) designing and implementing professional
6
development courses and experiences, including men-
7
toring for educators, that combine face-to-face and
8
online experiences;
9
(12) reduce gaps in access to learning opportu-
10
nities for students from groups historically under-
11
represented in STEM;
12
(13) provide support and resources for students
13
from groups historically underrepresented in STEM;
14
(14) addressing critical transitions, such as
15
middle school to high school, high school to college,
16
and school to internships and jobs;
17
(15) researching effective approaches for engag-
18
ing students from groups historically underrep-
19
resented in STEM; and
20
(16) any other activity the Director determines
21
will accomplish the goals of this section.
22
(g) EVALUATIONS.—All proposals for awards under
23
this section shall include an evaluation plan that includes
24
the use of outcome oriented measures to assess the impact
25
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and efficacy of the award. Each recipient of an award
1
under this section shall include results from these evalua-
2
tive activities in annual and final project reports.
3
(h) ACCOUNTABILITY AND DISSEMINATION.—
4
(1) EVALUATION
REQUIRED.—The Director
5
shall evaluate the portfolio of awards made under
6
this section. Such evaluation shall—
7
(A) use a common set of benchmarks and
[Text truncated for display. Full text available on Congress.gov.]