DATA LITERACY

October 15, 2021, 2:00 – 3:30 PM ET
DISCLAIMER

Any content or opinions 

expressed in this webinar are not 

that of the U.S. Department of 

Education nor an endorsement of 

any persons, products, programs, 

or policies mentioned herein.
U.S. 

DEPARTMENT 

OF 

EDUCATION 

STEM 

RESOURCES

ED.GOV/STEM
CONTEXT: 

DATA ON DATA SCIENCE

• Top Jobs Demanded in 2020 Globally 

(World Economic Forum)

• Top 10 Jobs in 2017, 2018, and 2019 

(LinkedIn)

• Top 5 Skillsets Demanded since 2014 

(LinkedIn)

We know these skillsets are important for 

personal finance, health, and civic 

engagement.
NATIONAL 

SPREADSHEET

DAY

OCT 17
DEPUTY SECRETARY

CINDY MARTEN

U.S. Department of Education
STEVEN LEVITT

Freakonomics Co-Author

William B. Ogden 

Distinguished Service 

Professor of Economics, 

University of Chicago

Freakonomics Podcast #391
TRENA WILKERSON

President, National Council of Teachers for 

Mathematics
Data Literacy

Data Science Mathematics, 

Statistics, and the Disciplines

Trena L. Wilkerson, NCTM President

Professor of Mathematics Education

Baylor University

www.nctm.org

twilkerson@nctm.org
Key Considerations

• Understand and critique our world

• Expand opportunities

• Critical thinking skills are essential

• Vital for both STEM and non-STEM fields

• Imperative for all students

• Make connections across and within disciplines/fieldsUnderstanding Context
Fostering data literacy includes

fostering mathematics, statistics, 

quantitative, media, and discipline specific 

literacy to equip students with the 

knowledge and resources needed to make 

sound, informed decisions and to solve 

problems arising in their personal and 

professional lives as members of society. 

NCTM’s Catalyzing Change Series for Early 

Childhood and Elementary Mathematics (2020), 

Middle School Mathematics (2020) and High 

School Mathematics (2018)
Partners & Stakeholders 

• Families/Caregivers

• Communities

• Teacher Educators

• Businesses

• Schools-teachers, administrators, school 

boards, Pk-12+

• Post-Secondary Education

• Curriculum & Assessment Writers/Developers
Data Literacy

• Equity Stance/Lens

• Build/foster/support positive identity

• Inclusive across Pk-12

– Meaningful, thoughtful approach

• Rethink/Reimagine statistics education

• Interdisciplinary approach

• How to support ALL teachers in this work
An imperative!

The development of statistical thinking 

is an imperative today. Every individual 

must be able to synthesize data to 

support decision making, make sense 

of our world, and prepare for the future. 

November 2020 President’s Message (Wilkerson)
An Imperative for Our Future

“Making a difference will require 

collaboration, communication, and work 

across diverse groups and communities, 

but engaging in these conversations is 

our collective responsibility to the 

students of today and tomorrow, as well 

as to our democratic society at large” 

(Catalyzing Change in High School Mathematics: 

Initiating Critical Conversations, NCTM 2018, p. 93) 
JO BOALER

Nomellini & Olivier 

Professor of Education, 

Stanford University

Director, youcubed
Introduction –

Youcubed Data 

Science

Jo Boaler, 

The Nominelli-Olivier 

Professor of Mathematics 

Education 

@joboaler
• Our goal is to share free resources for teachers & 

students showing the beauty and creativity in open 

mathematics & data science

• Over 53 million visits to website in last 5 years

• Hundreds of free tasks & lessons K-16

• Used in 2/3 of US Schools

• 145,000 newsletter subscribers

• TEDx talks – over 1.5 million views

• Newsletters opened in over 160 countries
California 

Mathematics 

Framework

Data Science K-12 

is highlighted

Data Science can be an 

equitable pathway 
High School Pathways – in California and beyond

California Mathematics Framework, 2022
Big Ideas for Data Science, K-10

Co-author: 

Rob Gould

Integrating 

data science 

into the 

content 

being taught
Example of 

“Big Ideas” for 

Grades 1-2

Over 10,000 

visits since 

Monday!!
Online Class 

to excite 

teachers 

about data 

science

Taken by over 20,000 

teachers

What is Data Science 

and why it is important?

Teaching Examples –

Classroom Video

CODAP Explorations

Teaching Content –

Probability & Statistics

Student Videos for Use 

in Classrooms

Part of the State of Utah Data Science Credential

Facebook group: 2.7K members
K-12 Data Talks

•What do you notice? • What do you 

wonder? • What is going on in this data 

visualization?
Stephen Curry and Basketball Data Example

• What do you 

notice? 

• What do you 

wonder? 

• What is going 

on in this data 

visualization?
Arctic Sea Ice Data Example

Pola Bear declines
Rent vs. Wages by State Data Example
Youcubed Youcubed Data Talks – data science – Mini Lessons for K – data talks -5

Over 100,000 Downloads
Youcubed Data Science Resources: Grade 6-10 Lessons

Over 30,000 downloads
High School Course: 

Explorations in Data Science: 

Youcubed Adaptable Curriculum

Partnered with 

Google &
Youcubed Adaptable Curriculum– in partnership with Google

Free with low Barrier for Entry

Widely Accessible Tools

Low-Floor, High Ceiling – Project based

Live and Current Datasets

Use of Local & Customizable Data

Broad Flexibility for Individual Settings
Academic Advisors and Industry Advisors

Talithia Williams

Dept of Mathematics

Harvey Mudd

Rachel Levy

Mathematician

Elena Grewal

Previously Head of 

Data Science 

Airbnb

Evan Shieh

Data Scientist

Amazon

Rob Gould

Dept of Statistics

UCLA

Steve Levitt

Economist

University of Chicago

Jeff Feng, Airbnb

Head of Machine 

Learning

Ellen Huynh, Airbnb

Senior Program 

Manager
Project Based

Youcubed: Explorations in Data Science: 8 Units

Data 

tells a

story

ACS Data 

Distributions

Bivariate 

Data

Modeling 

with Data

Data 

Predictions

Being a 

Data 

Scientist

A Project Based One Year Course

Data Categorical

tells a Data 

story

PrDobaatabilistic 

Mtodele

lsli ang

story
Encouraging Student 

Agency

Students ask their own 

questions & investigate data 

sets from their own 

communities
What will students learn?
The 

mathematics 

of high school 

data science

Statistics Probability

Matrices Linear Algebra

Computational 

Thinking Programming
Who is 

taking our 

data science 

course?

• 1500 accounts made, 100,000 page views

• This year - 575 teachers in 500 different 

schools/districts 

(220 have been to our PD)

• Over 30,000 students

• 46% girls/non-binary

• 40% students of color

• 58% not mathematically accelerated

High School Professional Development Workshops @youcubed. 

In person and online
Enjoyment 

& 

Engagement

•

Data Science

This is the most interesting 

course I have taken. An 

profound analyzation of 

data feels like deciphering a 

crime scene for clues. .

•It's fun. It’s like you're a 

research expert. 

•Everyone is involved in 

group projects no matter 

what, which is what makes 

it a great course. 

In comparison to 

other classes

•It’s a well thought class that 

does not overload you with work 

and tests.

•Even the grading scale is fair & I 

won’t worry if I need to fix an 

assignment 

•This course definitely surprised 

me after being enrolled to it for 

a month now. I enjoy it a lot 

more than I thought. From how 

the grading works, to the 

curriculum, or the 

communication this class has. 

It’s such a great way of learning 

along with it also being a very 

enjoyable class. 
Challenge •

Data Science

This data science course is 

challenging but in a good way.

• I think it can be very 

challenging but working 

groups and the help of our 

teacher, it’s more 

understandable

• I would describe it as 

challenging, fun, and creative 

because some tasks are 

challenging and yet you get to 

be creative. 

Other Maths

Classes

•

•

In normal integrate/precal classes I’m always lost 

and behind. 

I think this course is 

interesting and funner to 

learn. I am enjoying it 

because there is more 

group discussion and 

more projects so you 

aren’t just sitting in class 

bored and confused
•

•

•

•

Learning from Data Science

Data science is something you will eventually need. I personally 

recommend it to anyone who’s thinking of it. It's the class of the future. 

I really like this class and I hope to see it being added to more high 

schools

It's a class that helps with deep thinking on data that helps to 

understand how data is collected and useful. It brings the side of critical 

math thinking that I didn't know I had

I realized many things about myself and other things I like ever since 

I’ve been in this class.
Initiative to Change University Admissions 

Stanford change...

Mathematics: four years of any rigorous mathematics 

incorporating a solid grounding in fundamental skills 

(algebra, geometry, trigonometry). We also welcome 

preparation in skills related to statistics, data science 

and calculus.
Data Science throughout K-12

•

•

•

New Initiative from the 

State of California

Big Ideas instead of 

standards

Mathematics writing team: 

Jo Boaler & Cathy Williams

Youcubed – California Maths
SUYEN MACHADO

Co-Author, Introduction to Data 

Science

UCLA, Center X
Introduction to 

Data Science (IDS) 

for High School 

Students

Suyen Machado

Co-author & Director, Data Science Project

University of California, Los Angeles


Retrieved from: https://nasaeclips.arc.nasa.gov/teachertoolbox/the5e
The Data Science Test

https://bestcase.wordpress.com



LESSONS LABS PROJECTS


Professional 

Development
IDS Professional 

Development

Year 1 9 days IDS 

Implementation

Year 2 4 days

Deeper dive into:

R Skills, Concepts, 

Pedagogy


EMMANUEL SCHANZER

Bootstrap Data Science, 

Brown University 
Emmanuel Schanzer, Brown University BootstrapWorld.org :: Equity, Scale, Rigor / BootstrapWorld
BootstrapWorld.org :: Equity, Scale, Rigor

We made a controversial bet...

● Siloed classes aren’t the only way to do this

● Integrate computing & math authentically

● There’s a way to do it equitably, for all students

61
BootstrapWorld.org :: Equity, Scale, Rigor

15 years later....

● One of the largest providers of in-school CS nationwide

● One of three curricula cited by the NSF

● ~43% girls and young women, ~46% black and latinx students

● Taught almost entirely by non-CS teachers with no computing 

background, in non-CS classes!

62
BootstrapWorld.org :: Equity, Scale, Rigor

Ingredients for K-12 Data Science

63

Statistics Computing
BootstrapWorld.org :: Equity, Scale, Rigor

Ingredients for K-12 Data Science

64
BootstrapWorld.org :: Equity, Scale, Rigor

Responsible K-12 Data Science

● Computational Concepts

○ Problem decomposition

○ Reliability

○ Reproducibility

● Bootstrap been focused on these concepts since 

the 1990s

● “Coding” doesn’t touch ANY of them!

65
BootstrapWorld.org :: Equity, Scale, Rigor

Responsible K-12 Data Science

Statistics

Civic 

Responsibility

66

Computing

Domain 

Investment
BootstrapWorld.org :: Equity, Scale, Rigor

“New Math” 2.0

● We want to unleash the power of data, across the curriculum. 

● We use free tools - a disciplined approach

○ Elementary school: spreadsheets

○ Middle school: CODAP (which we have extended!)

○ High school: Pyret

● We build free curricular materials for grades 5-12

● An interdisciplinary challenge requires an interdisciplinary team

● We design for integration, creating on-ramps for teachers in every 

subject, at every level - at scale.

67
BootstrapWorld.org :: Equity, Scale, Rigor

Find the path that’s right for your

school or district

Access totally-free curriculum and 

tools

Curriculum, Training and Pedagogy 

designed for integration

www.BootstrapWorld.org/materials/data-science/

schanzer@BootstrapWorld.org 68

Key Take-Aways for Bootstrap
PANEL
RESOURCES

• Education Materials

• Forthcoming Guidance 

Resources

• Federal Funding Opportunities
Education Materials & Resources

71

• NASA / Microsoft “Day of Data” – modules for using spreadsheets to explore 

space data in classroom settings.

• U.S. Census Statistics in Schools Program – lesson plans and other 

resources across K-12 subjects, including math, history/social studies, geography, 

and English

• U.S. Census Academy – one- or two-hour courses on using Excel, R, and 

Census Data. Most relevant for adult learners and teachers.

• Data.Gov – a centralized housing for open Federal datasets.
72

Forthcoming Guidance
73

Soon Available on What Works Clearinghouse
74

•

Funding Resources for Supporting Data Science in K-12

American Recovery Plan ESSER Funds (includes any ESEA activity)

Elementary and Secondary Education Act (ESEA) Title IV Part A Funds

Teacher Quality Partnerships (TQP)

Supporting Effective Educator Development (SEED)

Education Innovation Research (EIR)

Education Research Grants (ERG)

Federal Communications Commission E-Rate Program

NSF Harnessing the Data Revolution (HDR) Program

NSF Research on Learning in Formal and Informal Settings (DRL)

•

•

•

•

•

•

•

•
75

ARP ESSER Funds

• Any activity authorized under ESEA is eligible.

• Additional eligible categories include:

• PD: partnerships with PD programs, hiring & retainment, technology training & literacy, 

and financial support, including student loans for educators (sec. D-1)

• Hardware: laptops, tablets, tech accessories, and assistive technology (sec. C-19)

• Software: software/online/virtual programs and online/virtual/cultural 

curriculum/programs (sec. C-19)

• Internet: improvements in technology infrastructure, operations, and use (sec. C-19)

• Out of School Time: online program, course development, hardware, or other materials 

(sec. C-2, C-13)

• Must spend by 2023 – invest in long-term contracts where possible.
76

•

Regular ED Grant Programs

Teacher Quality Partnerships (TQP) – funds collaborations between eligible 

K-12 districts and Institutes of Higher Education for teacher training

• Supporting Effective Educator Development (SEED) – develop, expand, 

and evaluate evidence-based practices for teacher training

• Education Innovation Research (EIR) Early Phase Grants – create, 

develop, implement, replicate, or take to scale field-initiated innovations

• Education Research Grants (ERG) STEM Topic – research on the 

improvement of students' STEM knowledge and skills
77

•

Other Federal Grant Programs

ED Office of Education Technology Grants Hub – centralized resource of all 

technology-related grants and programs. 

• FCC E-Rate – discounts for telecommunications, Internet access, and equipment 

to eligible schools and libraries. **See also COVID Emergency Connectivity Fund.

• NSF Harnessing the Data Revolution – an umbrella program covering three 

grant areas for graduate scholarships, data science field research, and data science 

institutes.

• NSF Division of Research and Learning – relevant programs for data science 

education include DRK-12, ITEST, and AISL solicitations.
SPEAKER

RESOURCES
REFERENCES & RESOURCES

National Council of Teachers of Mathematics (Presenter: Trena Wilkerson| Contact: nctm@nctm.org)

Books

• Catalyzing Change Series: https://www.nctm.org/change/

• K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II)-Book or Free Download: 

• Pre- High School Mathematics Lessons to Explore, Understand, and Respond to Social Injustice: 

https://www.nctm.org/Store/Products/High-School-Mathematics-Lessons-to-Explore,-Understand,-andRespond-to-Social-Injustice/

Webinars & Web Resources

• Teacher Tip Tuesday: Avoiding Data and Science Misinformation in Today's Messy Media Landscape (Open to 

All): https://www.nctm.org/online-learning/Webinars/Details/527

• Recommendations for Statistics in the Secondary Curriculum: Implications for Teachers: 

https://www.nctm.org/online-learning/Webinars/Details/357

• The Practice of Statistics: YES, We Can Do It at School!: https://www.nctm.org/onlinelearning/Webinars/Details/371

• Using Math to Make Sense of Our World: Pandemics, Viruses, and Our Actions: https://www.nctm.org/onlinelearning/Webinars/Details/349

• Digital Classroom Resources: Search for Statistics, Data, Data Science, Data Literacy: 

https://www.nctm.org/classroomresources/
REFERENCES & RESOURCES

Youcubed Data Science (Presenter: Jo Boaler | Contact: contact@bootstrapworld.org)

• Website: https://www.youcubed.org/

• Teacher Online Course: https://www.youcubed.org/21st-century-teaching-and-learning/

• High School Data Science Course: https://www.youcubed.org/resource/high-school-data-science-course/ 

• Data Science K-12 Lesson Plans: https://www.youcubed.org/data-science-lessons/

• Data Talks: https://www.youcubed.org/data-science-lessons/

• Data Big Ideas (K-10): https://www.youcubed.org/data-big-ideas/

Introduction to Data Science (Presenter: Suyen Machado | Contact: info@idsucla.org)

• Website: https://www.introdatascience.org

• Program Overview: https://www.introdatascience.org/introduction-to-data-science-curriculum

• Partnering with IDS: https://www.introdatascience.org/introduction-to-data-science-partnership

• Research & Publications: https://www.introdatascience.org/news-publications-and-presentations
REFERENCES & RESOURCES

Bootstrap Data Science (Presenter: Emmanuel Schanzer | Contact: contact@bootstrapworld.org)

• Website: https://www.BootstrapWorld.org

• DS materials: https://www.bootstrapworld.org/materials/data-science/

(Elementary grade materials are available upon request, but not yet linked publicly)

• Published research: www.BootstrapWorld.org/impact

• Work on accessibility:

• * https://cs.brown.edu/~sk/Publications/Papers/Published/sbk-adap-stud-ide-blind-prog/

• * https://cs.brown.edu/~sk/Publications/Papers/Published/sbk-accessible-ast-blocks/

• “4 ingredients of K12 Data Science” position paper:

https://bootstrapworld.org/blog/curriculum/Four-Ingredients-of-Data-Science-Education.shtml

• A sneak peak at our upcoming paper on the role of Domain Investment in K12 Data Science

https://bootstrapworld.org/blog/curriculum/Data-Science-Student-Experience.shtml
REFERENCES & RESOURCES

Federal Funding Resources (Presenter: Zarek Drozda | Contact: zarek.drozda@ed.gov)

• ARP ESSER Funds FAQ Guidance (May 2021): 

https://oese.ed.gov/files/2021/05/ESSER.GEER_.FAQs_5.26.21_745AM_FINALb0cd6833f6f46e03ba2d97d30af

f953260028045f9ef3b18ea602db4b32b1d99.pdf

• Teacher Quality Partnerships (TQP): https://oese.ed.gov/offices/office-of-discretionary-grants-supportservices/effective-educator-development-programs/teacher-quality-partnership/about-us/

• Supporting Effective Educator Development (SEED): https://oese.ed.gov/offices/office-of-discretionary-grantssupport-services/effective-educator-development-programs/supporting-effective-educator-developmentgrant-program/us-supporting-effective-educator-development/

• Education Innovation Research (EIR): https://oese.ed.gov/offices/office-of-discretionary-grants-supportservices/innovation-early-learning/education-innovation-and-research-eir/

• Education Research Grant Program (ERG): https://ies.ed.gov/funding/ncer_progs.asp

• OET Education Technology Grants Hub: https://tech.ed.gov/funding/

• FCC E-Rate: https://www.usac.org/e-rate/

• FCC Emergency Connectivity Fund: https://www.fcc.gov/emergency-connectivity-fund-faqs

• NSF Harnessing the Data Revolution: https://www.nsf.gov/cise/harnessingdata/

• NSF Division of Research and Learning: https://www.nsf.gov/div/index.jsp?div=DRL
QUESTIONS FOR ED?

Zarek Drozda | Data Science Education Fellow

Zarek.Drozda@ed.gov

Patti Curtis | Robert Noyce/Ellen Lettvin STEM Education Fellow

Patti.Curtis@ed.gov

Melissa Moritz | Afterschool and Summer Learning Fellow

Melissa.W.Moritz@ed.gov