• Sign on to wireless
– Use the ‘Northwestern – Guest’ wireless
• Follow instructions in browser for login.
• Input OSEP as ‘Sponsor’
• If the ‘Northwestern Guest’ wireless doesn’t work, ask
for Login/Password for ‘Northwestern’ wireless.
• Go to
http://gk12northwestern.wikispaces.com/201
4+Summer+Workshop
Northwestern University
Computational Thinking in STEM
Building interest and proficiency in computational thinking in STEM
http://ct-stem.northwestern.edu
Meet the Team
Principal Investigators
Kemi Jona Michael Horn Vicky Kalogera Laura Trouille
Graduate Students
David Weintrop
Elham Behesti
Uri Wilensky
Kai Orton
High School Lead Teachers & PD Providers:
Pilot Teachers:
11 in 2012-2013
16 in 2013-2014
XX in 2014-2015
Mark Vondracek Ami Lefevre Meagan Morscher
This work is supported in part by the National Science Foundation under NSF grants CNS-1138461 and is covered by IRB study
STU00058570. However, any opinions, findings, conclusions, and/or recommendations are those of the investigators and do not necessarily
reflect the views of the Foundation.
CT-STEM: Goals
Goals:
• Build teacher knowledge, interest, and confidence:
• developing your students’ CT-STEM skills
• using CT tools to improve your students’ learning of STEM concepts
• Connect CT-STEM to what you already do & to Illinois standards
• Train in discipline-specific CT-STEM lesson plans
CT-STEM: Train the Trainer Model
Lead Teachers (that’s you!):
• 1 in each discipline at each school
• Attend Summer Workshop
• Lead 3 Academic Year Workshops for teachers at your school
• Teach and assess 4 CT-STEM lessons in your classroom
CT-STEM Teachers:
• ~4 in each discipline at each school
• Attend 3 Academic Year Workshops
• Teach and assess 4 CT-STEM lessons in your classroom
Northwestern University Computational Thinking in STEM
High-School 2 - 4 Hour Lesson Plans
CT-STEM Lesson Plans
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“Big Data” is Everywhere
~40 109 Web pages at ~300 kilobytes each = 10 Petabytes
Youtube 48 hours video uploaded per minute;
in 2 months in 2010, uploaded more than total NBC ABC CBS
~2.5 petabytes per year uploaded?
LSST 30 TB/night
LHC 15 petabytes per year
Radiology 69 petabytes per year
Square Kilometer Array Telescope will be 100
terabits/second
Earth Observation becoming ~4 petabytes per year
Earthquake Science – few terabytes total today
PolarGrid – 100’s terabytes/year
Exascale simulation data dumps – terabytes/second
8
McKinsey Institute on Big Data Jobs
• There will be a shortage of talent necessary for organizations to take
advantage of big data. By 2018, the United States alone could face a
shortage of 140,000 to 190,000 people with deep analytical skills as well as
1.5 million managers and analysts with the know-how to use the analysis of
big data to make effective decisions.
9
CT-STEM: Key Concepts
• Algorithmic Thinking:
– create a series of ordered steps to solve a problem
– allows for automation of a procedure
• Examples:
• Efficiency at a buffet table
• Long Division
• Experimental Procedure
CT-STEM: Key Concepts
• Abstraction:
– Pulling out the important details
– Identifying principles that apply to other situations
• Examples:
• Holiday dinners
• Construct a model of an atom
• Use the term ‘titration’ in an experimental design
CT-STEM: Key Concepts
• Computational Modeling:
– Use a computational tool to develop a representation
of a system (i.e., visualize an abstraction of a system)
– Use a computational tool to analyze, visualize, and
gain understanding of a STEM concept
• Examples:
• CAD (in engineering)
• Netlogo and other computational environments
CT-STEM: Key Concepts
• Decomposition:
– Reformulating a seemingly difficult problem into
one we know how to solve
• Examples:
• Road networks in a major city -> Muddy City
CT-STEM: Key Concepts
• Generalization:
– How is this problem is similar to others?
– Can we transfer the problem solving process from
a solved problem to this new one?
• Examples:
• Can I apply the same strategies that I learned
playing soccer to playing basketball?
• Gravity and flux
CT-STEM: Key Concepts
• Big Data:
– Big Data refers to a collection of data sets so large and
complex, it’s impossible to process them with the usual
databases and tools.
– Because of its size and associated numbers, Big Data is
hard to capture, store, search, share, analyze and visualize.
• Examples:
• Sequencing the human genome
• The Galaxy Zoo Project of over 1 million galaxies
The Human Genome…By the Numbers
46…Chromosomes in each cell
~23,000…Genes in the human genome
2.4 million…Base pairs in the largest human gene
3.1 billion…Base pairs in each cell
75-100 trillion…Cells in the human body
CT in Biology
• Shotgun algorithm expedites sequencing of human genome
- DNA sequences are strings in a language
- Protein structures can be modeled as knots
- Protein kinetics can be modeled as computational processes
- Cells as a self-regulatory system are like electronic circuits
CT in Astronomy
• Mass Determination of our Milky Way’s Black Hole
– Comparing observed data to simulations
CT in Chemistry
• Atomistic calculations explore chemical phenomena
• Optimization and searching algorithms identify best chemicals for
improving reaction conditions to improve yields
CT in Engineering
• Boeing 777 never tested in a wind tunnel, only in computer
simulations
• Ability to calculate higher order terms implies more precision,
which implies reducing weight, waste, costs in fabrication, etc.
CT in Geology
• Modeling the earth inner layers, using seismic waves
• Modeling the earth and our atmosphere to track and
predict climate changes
CT in Math
• Discovering E8 Lie Group
– took 18 mathematicians, 4 years and 77 hours of
supercomputer time (200 billion numbers).
– Profound implications for physics (string theory)
CT in Medicine
• Robotic surgery
• Electronic health records require privacy technologies
• Scientific visualization enables virtual colonoscopy
CT in Social Sciences
• Social networks explain phenomena like MySpace, YouTube
• Statistical machine learning is used for recommendation and
reputation services, e.g., Netflix, affinity card
CT in the Humanities
• What do you do with a million books?
– Nat’l Endowment for the Humanities Institute of Museum
and Library Services
• Arts, drama, music, photography
Credit: Christian Mueller
CT in Entertainment
• Games
• Music MP3 sorting/searches
• Movies
- Dreamworks uses HP data center to renderShrek and Madagascar
- Lucas Films uses 2000-node data center to make Pirates of the Caribbean.
CT-STEM Skills Mapped to the Next Generation Science Standards
CT-STEM Skill
NGSS Standard Addressed
Data & Information
- Collecting / creating data
- Manipulating data
- Choosing data structures
- Analyzing & visualizing data
Analyzing and Interpreting Data:
- Use tools and models to generate, gather and
analyze data
Using Computational Thinking:
- Use CT tools for statistical analysis to analyze
/ model data
Computational Problem Solving
- Troubleshooting / debugging
- Applying recursion, iterative and conditional
logic
- Defining / interpreting instructions for a
computer
- Generating / applying algorithmic solutions
- Choosing effective computational tools
- Developing modular computational solutions
- Simplifying / reframing / generalizing problems
- Assessing approaches / solutions to a problem
- Creating abstractions
Planning Investigations:
- Evaluate data collection methods
(experimental design, simulations).
Constructing Solutions:
- Construct and revise explanations based on
evidence from a variety of sources (e.g.,
scientific principles, models, theories).
Computational Modeling
- Using models to understand a STEM concept
- Understanding how/why a model works
- Assessing models
- Using models to find / test solutions
- Building / extending models
Developing and Using Models:
- Use / construct models to predict / explain
relationships between systems and their
components
- Construct a model using an analogy,
example, or abstract representation to explain
- Developing modular computational solutions
- Simplifying / reframing / generalizing problems
- Assessing approaches / solutions to a problem
- Creating abstractions
CT-STEM Skill
NGSS Standard
Computational Modeling
- Using models to understand a STEM concept
- Understanding how/why a model works
- Assessing models
- Using models to find / test solutions
- Building / extending models
Developing and Using Models:
- Use / construct models to predict / explain
relationships between systems and their
components
- Construct a model using an analogy,
example, or abstract representation to explain
a scientific principle or solution
Using Computational Thinking:
- Use simple limit cases to test algorithms or
simulations to see if a model ‘makes sense’ by
comparing with the real world.
Using Mathematics and Computational
Thinking:
- Computational simulations are based on
mathematical models.
Systemic Thinking
- Investigating a system as a whole
- Understanding the relationships within a system
- Thinking in levels
- Visualizing a system
Systems and System Models:
- Models can be used to simulate systems including how energy, matter, and information
flow within systems at different scales.
Patterns:
- Different patterns observed at each scale of a
system can provide evidence for causality.
- Classifications used at one scale may need
revision when information from smaller or
larger scales is introduced.