I 
117TH CONGRESS 
1ST SESSION H. R. 3588 
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 
MAY 28, 2021 
Ms. HOULAHAN (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-
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’’. 
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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. 
(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. 
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(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, 
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. 
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