outline
GAISE preK-12 II, The Introduction
to Data Science Course, and DataScience Education
Rob GouldUCLA• Overview of GAISE II• Overview of the Introduction to Data Science (IDS) highschool course• Examples of how IDS meets the GAISE II• (If time remains, a demonstration of the participatory
sensing dashboard.)

ORIGINAL VISION• “Every high-school graduate should be able to use soundstatistical reasoning to intelligently cope with therequirements of citizenship, employment, and family and tobe prepared for a healthy, happy, and productive life.” –Pre-k-12 GAISE, p.1

DATA HAVE CHANGED3GAISE IIPRE-K-12

WE MUST HELP STUDENTS (STARTING AT YOUNGAGE) MAKE SENSE OF DATA THAT SURROUNDTHEM

MAJOR ENHANCEMENTS BEING
ADDRESSED:

FormulateStatistical
Investigative
Question•Incorporating new Stats ed research•Different data and variable types (images, words, etc.-

Collect/Consider
the Data•Distinguishing between primary and secondary data
•Questioning throughout the statistical problem-solving process – question
posing and question asking

Analyze
the Data•Multivariate thinking•Science based examples

Interpret
the Results•Integrating technology and computational thinking

FRAMEWORK

Level
Level C
Level BA

Vision• All students, regardless of success in mathematics and regardless ofwhether they plan to attend college, should develop "data acumen" to beengaged citizens.
• All students can learn to analyze data to answer questions that interest
them and to address problems that they feel are important.

fi• All students should understand how data analysis includes ethicalresponsibilities concerning privacy, con dentiality, and equity.

LAUSD Demographics• Developed with funding from the National Science
Foundation in partnership with• Los Angeles Uni ed School District (LAUSD),• UCLA Department of Statistics,• UCLA Department of Computer Science, and• UCLA Graduate School of Education and Information
Science.

• LAUSD is second-largest
school district in U.S.,
with about 750,000
students;• 80% of students are

below the poverty level• IDS was designed for *all*students, regardless ofcareer goals or pastacademic achievement

https://commons.wikimedia.org/wiki/File:Hamilton_High_School_LAUSD_Entrance.jpg

fi{{Information |Description= Hamilton High School Entrance in Los Angeles, CA |Source= Own Work |Date= 26 March 2006 |Author= User:Jorobeq }}Structure• A year-long course for high-school students (ages 14-18)• Requirements: algebra• Currently taught in 65 high schools in 4 states and hastaught 12,500 students to date. (Currently, 5000 students
are taking the course.)• Open-source and freely available under Creative
Commons license: http://introdatascience.org• 4 units of daily lessons.
• Each unit ends in a "practicum": a long project that ties together theprevious weeks and• each unit includes a participatory sensing data-gathering campaign.
• Lessons consist of• student-centered classroom activities to develop conceptual
understanding and• data analysis labs using R (Rstudio) to apply concepts and learn
basic coding.

4 Lessons from IDS

Unit 1, Lesson 2: Stick Figures"Collect and record as muchinformation as you can about thesepeople""Organize this information on aposter any way that you feel ishelpful"

Posters are displayed, and students discuss:• what are similarities and di erences in theUnit 2

ways the data were organized• what information ('variables') is available?
• which organizations made it easiest to see

ff

the variables?

Konold, Finzer, Kreetong (2016)
Understand that data are information• "spreadsheet" format is notnatural for many students (andtheir teachers)• students need to develop theconception of "case"• students have basically soundand solid notions of data
• students are comfortable andmay even prefer hierarchicalrepresentations overspreadsheet representations

Understand how to collect and record..

Understand that a variable measures thesame characteristic on several
individuals

Understand that ...data can beorganized and stored in avariety of structures

The Statistical Investigation Cycle is at the Foundation of IDS

Students begin the class learn to pose"statistical investigative questions"participatory sensing•A data-collection paradigm developed by Deborah Estrin's lab at UCLA(Center for Embedded Network Sensing)•Students engage in participatory sensing campaigns.
•Mobile devices used to collect data to address various issues: Nutrition,recycling, stress, water conservation•Students collect numbers, images, words, locations, times, dates.
•They are "human sensors", collecting a stream of data based ontriggers, and not random samples.

based on the GAISE Statistical
Investigation Process and thePPDAC (Wild & Pfannkuch 1999)
J. Burke, D. Estin, M. Hansen, A. Parker, N.
Ramanathan, M. Reddy, M.B. Srivastava, Participatory
Sensing. Center for Embedded Network Sensing.
(2006).

snack campaign• Motivating Questions:• What is my snacking pattern?
• How good am I at rating the healthiness of my snack?
• Do I tend to eat healthy? How does this compare to the rest ofmy class?

• Does knowing nutritional value change my habits?
• Data collection: Collect data every time you eat a snack for the next
four days.

https://sandbox.mobilizingcs.org/#dashboard/#urn:public:nutrition

Framework(Participatory Sensing)• Ask Questions: Level C•Formulate multivariable statistical investigative questions and determine how data can be collected
and analyzed to provide an answe

ns.

sssar

B

nfi

C

B

C

•Collect and Consider Data: Level A•Understand that within a dataset that can be different types of variable•Interrogate the data to understand the context of the variable•Collect and Consider Data: Level•Interrogate to determine how data were collected, what types of variables, etc•Understand that data can be collected or existing data obtained from other source•Collect/Consider Data: Level C•Understand how concerns about privacy and human subjects affect distribution and collection of dat•Understand that in some situations the data may not generalize to the desired populatio•Analyze: Level•Use technology to lter and subset dat•Summarize and describe relationships between multiple variable•Interpret: Level•State the limits of generalizatio•Interpret: Level•Use multivariate thinking to explain how variables impact one anotherHorror Movie shuf e"What's the di erence in proportion of survival
rates for females and males?"

Are women in slasher films more likely
to survive until the end of the film thanmen?

flff

IDS uses Rstudio Cloud but relies on the package "mobilizR"
which is based on the "mosaic" package (Pruim, Horton, Kaplan)

> set.seed(1> shuffles <-do(500)*tally(shuffle(survival)~gender, data=slasher, format="percent", margins=TRUE> shuffles <- mutate(shuffles, diff = Survives.Female - Survives.Male> histogram(~diff, data=shuffles)

Could this difference be due to chance?
Or is it too large?

))

outcome in data

informal inference

Analyze Data Framework:

Level A: Observe whether there appears to be a di erence in two groups
Level B: Explore patterns of association between two categorical variables
Level C: Describe associations between two categorical variables
Level C: use simulations to investigate associations between two categorical variables• Makar & Rubin (2009): generalizing beyond the data at
hand and expressing uncertainty.

• IDS emphasizes the "informal" aspects. Does not teachp-values or con dence intervals or formal hypothesis
tests.

Interpret Results Framework:
Level B: Use statistical evidence from analyses to answer ...questions throughstructured answers with some teacher guidance.
Level B: Generalize beyond the sample...including a statement of uncertainty
Level C: Understand what it means for an outcome...to be plausible or not compared tochance variation• Instead, develops understanding and intuition to assist

when students take Statistics (which does cover 'formal'inference')fffi• Informal inference is mostly levels A and B in GAISE II

Why teach coding?

Summary• Learning to "code" using R has many advantages:• The GAISE II revision provides learning outcomes that can• Students use code to communicate models and ideas

guide the development of a data science education
curriculum.

• Students more easily understand code than mathematical notation• Teaches reproducible research habits and communication• The GAISE II assumes a " avor" of data science that has
the primary focus on teaching students to develop "dataacumen"--learning to reason with data.

• Some coding is needed for students to access data.
• Heinzman (2020):• Many di erent groups are creating data science• Students nd that coding is "helpful" and "productive" for solving

curriculum. It is extremely important that statisticians
play a primary role in shaping these curricula

problems. (Heinzman, 2020)

flffi

fffifi• Students nd it "e cient" and "empowering" (Heinzman, 2020)

Thank you!
rgould@stat.ucla.edu
amstat.org/education/gaise
introdatascience.org

GAISE II teamAnna Bargagliotti
Christine Franklin
Pip Arnold
Rob Gould
Sheri Johnson
Leticia Perez
Denise A. Spangler

IDS TeamRob Gould
Suyen Machado
Monica Casilla
Brendan Chang
Leeanne Trusela
Shubahm Kayastha
James Molyneux
Amelia McNamara
Terri Johnson
Hongsuda Tangmungarunkit
Steve Nolanreferences
A. Bargagliotti, C. Franklin, P. Arnold, R. Gould, S. Johnson, L. Perez and D. Spangler, Pre-K-12 Guidelines forassessment and instruction in statistics education (GAISE) report II. Alexandria, VA: American Statistical
Association and Reston, VA: National Council of Teachers of Mathematics. (2020)
J. Burke, D. Estin, M. Hansen, A. Parker, N. Ramanathan, M. Reddy, M.B. Srivastava, Participatory Sensing.
Center for Embedded Network Sensing. (2006).
R. Gould, A. Bargagliotti, T. Johnson, An Analysis of Secondary Techers' Reasoning with Participatory Sensing
Data, Statistics Education Research Journal, 16(2) November 2017.
E. Heinzman, Math is No Longer a Four-Letter Word: A Mixed Methods Study of Two Non-Traditional FourthYear Mathematics Classes. PhD dissertation, University of California, San Diego, 2020. https://escholarship.org/uc/item/25d4s7vq
C. Konold, W. Finzer, K. Kreeton, Modeling as a Core Component of Structuring Data, Proceedings InternationalConference on Statistical Reasoning, Teaching and Learning, 2016
K. Makar and A. Rubin, A Framework For Thinking About Informal Statistical Inference, Statistics Education
Research Journal 8(1)(2009): 82-105.
C. Wild and M. Pfannkuch, Statistical Thinking in Empirical Enquiry, International Statistical Review, 67(1999),223-265.

Traditional Mathematics Pathway to University
Algebra 1

Geometry

Algebra II

Pre-Calculus

Advanced Mathematics Pathway

Geometry

Algebra II

Pre-Calculus

Calculus

Proposed Data Science Pathway
AlgebraComputerScience

Into DataScience

Statistics

Independent study found that IDS increases "college readiness"

https://sandbox.mobilizingcs.org/#dashboard/#urn:public:snack