Title: Mathematical and Statistical Modeling Education Act
Policy area: Education

Summary:
Mathematical and Statistical Modeling Education Act
This bill requires the National Science Foundation (NSF) to take certain actions to increase mathematical and statistical modeling education in public elementary and secondary schools.
First, the NSF must provide competitive grants to institutions of higher education and nonprofit organizations for research and development to support high-quality mathematical modeling education (e.g., data science and computational thinking) in schools.
In addition, the NSF must seek to enter into an agreement with the National Academies of Sciences, Engineering, and Medicine to study and report on mathematical and statistical modeling education in schools.

[Congressional Bills 117th Congress]
[From the U.S. Government Publishing Office]
[S. 1839 Introduced in Senate (IS)]

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117th CONGRESS
  1st Session
                                S. 1839

   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

                              May 26, 2021

 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.

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

SECTION 1. SHORT TITLE.

    This Act may be cited as the ``Mathematical and Statistical 
Modeling Education Act''.

SEC. 2. MATHEMATICAL AND STATISTICAL MODELING EDUCATION.

    (a) Findings.--Congress finds the following:
            (1) The mathematics taught in schools, including 
        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 resources to train and upskill employees.
            (2) According to the Bureau of Labor Statistics, the United 
        States will need 1,000,000 additional 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.
            (5) The STEM workforce relies on computational and data-
        driven discovery, decision-making, and predictions, from models 
        that often must quantify uncertainty, as in weather 
        predictions, spread of disease, or financial forecasting.
            (6) Most fields, including analytics, science, economics, 
        publishing, marketing, actuarial science, operations 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 
        understanding 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 experiment 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 expertise from their other school 
        experiences.
            (9) Mathematical modeling has been a strong educational 
        priority globally, especially in China, where participation in 
        United States mathematical modeling challenges in high school 
        and higher education is orders of magnitude higher than in the 
        United States, and Chinese teams are taking a majority of the 
        prizes.
            (10) Girls participate in mathematical modeling challenges 
        at all levels at similar levels as boys, while in traditional 
        mathematical competitions girls participate 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 challenges.
    (b) Definitions.--In this section:
            (1) Director.--The term ``Director'' means the Director of 
        the National Science Foundation.
            (2) Federal laboratory.--The term ``Federal laboratory'' 
        has the meaning given such term in section 4 of the Stevenson-
        Wydler Technology Innovation 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 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 the term in the 2019 
        Guidelines to Assessment and Instruction in Mathematical 
        Modeling Education (GAIMME) report, 2nd edition.
            (6) Operations research.--The term ``operations research'' 
        means the application of scientific methods to the management 
        and administration of organized military, governmental, 
        commercial, and industrial processes to maximize operational 
        efficiency.
            (7) Statistical modeling.--The term ``statistical 
        modeling'' has the meaning given the term in the 2021 
        Guidelines to Assessment and Instruction in Statistical 
        Education (GAISE II) report.
            (8) Stem.--The term ``STEM'' means the academic and 
        professional disciplines of science, technology, engineering, 
        and mathematics.
    (c) Preparing Educators To Engage Students in Mathematical and 
Statistical Modeling.--The Director shall provide grants on a merit-
reviewed, competitive basis to institutions of higher education and 
nonprofit organizations (or a consortium thereof) for research and 
development to advance innovative approaches to support and sustain 
high-quality mathematical modeling education in schools operated by 
local education agencies, including statistical problem solving, data 
science, operations research, and computational thinking. The Director 
shall encourage 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 a grant 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 grant is sought, including 
        student subgroups described in section 1111(b)(2)(B)(xi) of the 
        Elementary and Secondary Education Act of 1965 (20 U.S.C. 
        6311(b)(2)(B)(xi)), and students experiencing homelessness and 
        children and youth in foster care.
            (2) A description of the process for recruitment and 
        selection of students, educators, or local educational agencies 
        to participate in such research activity.
            (3) A description of how such research activity may inform 
        efforts to promote the engagement and achievement of students 
        in prekindergarten through grade 12 in mathematical modeling 
        and statistical modeling using problem-based learning with 
        contextualized data and computational tools.
            (4) In the case of a proposal consisting of a partnership 
        or partnerships with 1 or more local educational agencies and 1 
        or more researchers, a plan for establishing a sustained 
        partnership that is jointly developed and managed, draws from 
        the capacities of each partner, and is mutually beneficial.
    (e) Partnerships.--In awarding grants under subsection (c), the 
Director shall encourage applications that include--
            (1) partnership with a nonprofit organization or an 
        institution of higher education that has extensive experience 
        and expertise in increasing the participation of students in 
        prekindergarten through grade 12 in mathematical modeling and 
        statistical modeling;
            (2) partnership with a local educational agency, 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 cognitive 
        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 conversations with 
        parents, school leaders, school boards, community members, 
        employers, and other stakeholders; and
            (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 a grant under this 
section shall use the grant funds for research and development 
activities to advance innovative approaches to support and sustain 
high-quality mathematical modeling education in public schools, 
including statistical modeling, data science, operations research, and 
computational thinking, which may include--
            (1) engaging prekindergarten through grade 12 educators in 
        professional learning opportunities to enhance mathematical 
        modeling and statistical problem solving knowledge, and 
        developing training and best practices to provide more 
        interdisciplinary learning opportunities;
            (2) conducting research on curricula and teaching practices 
        that empower students to choose the mathematical, statistical, 
        computational, and technological tools that they will apply to 
        a problem, as is required in life and the workplace, rather 
        than prescribing a particular approach or method;
            (3) providing students with opportunities to explore and 
        analyze real data sets from contexts that are meaningful to the 
        students, which may include--
                    (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; and
                    (D) data of various types including quantities, 
                words, and images;
            (4) taking a school or district-wide approach to 
        professional development in mathematical modeling and 
        statistical modeling;
            (5) engaging rural local educational agencies;
            (6) supporting research on effective mathematical modeling 
        and statistical modeling teaching practices, including problem- 
        and project-based learning, universal design for accessibility, 
        and rubrics 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 leverage local natural, 
        business, industry, and community assets in order to support 
        community-based learning;
            (9) providing hands-on training and research opportunities 
        for mathematics and statistics educators at Federal 
        laboratories, institutions of higher education, or in industry;
            (10) developing mechanisms for partnerships between 
        educators and employers to help educators and students make 
        connections between their mathematics and statistics projects 
        and topics of relevance in today's world;
            (11) designing and implementing professional development 
        courses and experiences, including mentoring for educators, 
        that combine face-to-face and online experiences;
            (12) addressing critical transitions, such as middle school 
        to high school, high school to college, and school to 
        internships and jobs; and
            (13) any other activity the Director determines will 
        accomplish the goals of this section.
    (g) Evaluations.--All proposals for grants under this section shall 
include an evaluation plan that includes the use of outcome oriented 
measures to assess the impact and efficacy of the grant. Each recipient 
of a grant under this section shall include results from these 
evaluative activities in annual and final projects.
    (h) Accountability and Dissemination.--
            (1) Evaluation required.--The Director shall evaluate the 
        portfolio of grants awarded under this section. Such evaluation 
        shall--
                    (A) use a common set of benchmarks and tools to 
                assess the results of research conducted under such 
                grants and identify best practices; and
                    (B) to the extent practicable, integrate the 
                findings of research resulting from the activities 
                funded through such grants with the findings of other 
                research on student's pursuit of degrees or careers in 
                STEM.
            (2) Report on evaluations.--Not later than 180 days after 
        the completion of the evaluation under paragraph (1), the 
        Director shall submit to Congress and make widely available to 
        the public a report that includes--
                    (A) the results of the evaluation; and
                    (B) any recommendations for administrative and 
                legislative action that could optimize the 
                effectiveness of the grants awarded under this section.
    (i) Authorization of Appropriations.--For each of fiscal years 2022 
through 2026, there are authorized out of funds appropriated to the 
National Science Foundation, $10,000,000 to carry out the activities 
under this section.

SEC. 3. NASEM REPORT ON MATHEMATICAL AND STATISTICAL MODELING EDUCATION 
              IN PREKINDERGARTEN THROUGH 12TH GRADE.

    (a) Study.--Not later than 60 days after the date of enactment of 
this Act, the Director shall seek to enter into an agreement with the 
National Academies of Sciences, Engineering and Medicine (in this 
section referred to as ``NASEM'') (or if NASEM declines to enter into 
such an agreement, another appropriate entity) under which NASEM, or 
such other appropriate entity, agrees to conduct a study on the 
following:
            (1) Factors that enhance or barriers to the implementation 
        of mathematical modeling and statistical modeling in elementary 
        and secondary education, including opportunities for and 
        barriers to use modeling to integrate mathematical and 
        statistical ideas across the curriculum, including the 
        following:
                    (A) Pathways in mathematical modeling and 
                statistical problem solving from kindergarten to the 
                workplace so that students are able to identify 
                opportunities to use their school mathematics and 
                statistics in a variety of jobs and life situations and 
                so that employers can benefit from students' school 
                learning of data science, computational thinking, 
                mathematics, statistics, and related subjects.
                    (B) The role of community-based problems, service-
                based learning, and internships for connecting students 
                with career preparatory experiences.
                    (C) Best practices in problem-, project-, and 
                performance-based learning and assessment.
            (2) Characteristics of teacher education programs that 
        successfully prepare teachers to engage students in 
        mathematical modeling and statistical modeling, as well as gaps 
        and suggestions for building capacity in the pre-service and 
        in-service teacher workforce.
            (3) Mechanisms for communication with stakeholders, 
        including parents, administrators, and the public, to promote 
        understanding and knowledge of the value of mathematical 
        modeling and statistical modeling in education.
    (b) Public Stakeholder Meeting.--In the course of completing the 
study described in subsection (a), NASEM or such other appropriate 
entity shall hold not less than one public meeting to obtain 
stakeholder input on the topics of such study.
    (c) Report.--The agreement under subsection (a) shall require 
NASEM, or such other appropriate entity, not later than 24 months after 
the effective date of such agreement, to submit to the Secretary of 
Education and the appropriate committees of jurisdiction of Congress a 
report containing--
            (1) the results of the study conducted under subsection 
        (a);
            (2) recommendations to modernize the processes described in 
        subsection (a)(1); and
            (3) recommendations for such legislative and administrative 
        action as NASEM, or such other appropriate entity, determines 
        appropriate.
    (d) Authorization of Appropriations.--For the fiscal year 2022, 
there are authorized out of funds appropriated to the National Science 
Foundation, $1,000,000 to carry out the activities under this section.
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