Transcript Computational Thinking for Everyone

Computational Thinking for
Everyone
Jeannette M. Wing
President’s Professor of Computer Science
Carnegie Mellon University
and
Assistant Director
Computer and Information Science and Engineering Directorate
National Science Foundation
 2008 Jeannette M. Wing
Outline
• Computational Thinking
• A Vision for our Field
• The Two A’s to CT
• Research and Education Implications
Two and a Half Years Later…
• External Response and Impact
• Reality Check
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My Grand Vision for the Field
• Computational thinking will be a fundamental skill
used by everyone in the world by the middle of the
21st Century.
– Just like reading, writing, and arithmetic.
– Imagine every child knowing how to think like a computer
scientist!
– Incestuous: Computing and computers will enable the spread
of computational thinking.
– In research: scientists, engineers, …, historians, artists
– In education: K-12 students and teachers, undergrads, …
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J.M. Wing, “Computational Thinking,” CACM Viewpoint, March 2006, pp. 33-35.
http://www.cs.cmu.edu/~wing/
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The First A to Computational Thinking
• Abstractions are our “mental” tools
• The abstraction process includes
– Choosing the right abstractions
– Operating simultaneously at multiple layers of
abstraction
– Defining the relationships the between layers
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The Second A to Computational Thinking
• The power of our “mental” tools is amplified by
our “metal” tools.
• Automation is mechanizing our abstractions,
abstraction layers, and their relationships
– Mechanization is possible due to precise and exacting
notations and models
– There is some “computer” below (human or machine,
virtual or physical)
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Two A’s to C.T. Combined
• Computing is the automation of our abstractions
– They give us the audacity and ability to scale.
• Computational thinking
– choosing the right abstractions, etc.
– choosing the right “computer” for the task
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Research Implications
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CT in Other Sciences, Math, and Engineering
Biology
- Shotgun algorithm expedites sequencing
of human genome
- DNA sequences are strings in a language
- Protein structures can be modeled as knots
Credit: Wikipedia
- Protein kinetics can be modeled as computational processes
- Cells as a self-regulatory system are like electronic circuits
Brain Science
- Modeling the brain as a computer
- Vision as a feedback loop
- Analyzing fMRI data with machine learning
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Credit: LiveScience
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CT in Other Sciences, Math, and Engineering
Chemistry [Madden, Fellow of Royal Society of Edinburgh]
- Atomistic calculations are used to explore
chemical phenomena
- Optimization and searching algorithms identify
best chemicals for improving reaction
conditions to improve yields
Credit: University of Minnesota
Credit: NASA
Geology
- Modeling the earth’s surface to the sun,
from the inner core to the surface
- Abstraction boundaries and hierarchies of
complexity model the earth and our atmosphere
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CT in Other Sciences, Math, and Engineering
Astronomy
- Sloan Digital Sky Server brings a telescope
to every child
- KD-trees help astronomers analyze very large
multi-dimensional datasets
Credit: SDSS
Mathematics
Credit: Wikipedia
- Discovering E8 Lie Group:
18 mathematicians, 4 years and 77 hours of
supercomputer time (200 billion numbers).
Profound implications for physics (string
theory)
- Four-color theorem proof
Engineering (electrical, civil, mechanical, aero & astro,…)
Credit: Wikipedia
- Calculating higher order terms implies
more precision, which implies reducing
weight, waste, costs in fabrication
- Boeing 777 tested via computer simulation alone,
not in a wind tunnel
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Credit: Boeing
CT for Society
Economics
- Automated mechanism design underlies
electronic commerce, e.g., ad placement,
on-line auctions, kidney exchange
- Internet marketplace requires revisiting
Nash equilibria model
Social Sciences
- Social networks explain phenomena such
as MySpace, YouTube
- Statistical machine learning is used for
recommendation and reputation services,
e.g., Netflix, affinity card
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CT for Society
Medicine
- Robotic surgery
- Electronic health records require privacy technologies
- Scientific visualization enables virtual colonoscopy
Humanities
Credit: University of Utah
- What do you do with a million books?
Nat’l Endowment for the Humanities
Inst of Museum and Library Services
Law
- Stanford CL approaches include AI, temporal logic,
state machines, process algebras, petri nets
- POIROT Project on fraud investigation is creating a detailed
ontology of European law
CT for Everyone- Sherlock Project on crime
13 scene investigation
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CT for Society
Entertainment
- Games
- Movies
Credit: Dreamworks SKG
- Dreamworks uses HP data center to
renderShrek and Madagascar
- Lucas Films uses 2000-node data center to
produce Pirates of the Caribbean.
Credit: Carnegie Mellon University
Arts
- Art (e.g., Robotticelli)
- Drama
- Music
- Photography
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Credit: Christian Moeller
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Sports
Credit: Wikipedia
- Lance Armstrong’s cycling
computer tracks man and
machine statistics
- Synergy Sports analyzes
digital videos NBA games
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Educational Implications
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Pre-K to Grey
• K-6, 7-9, 10-12
• Undergraduate courses
– Freshmen year
• “Ways to Think Like a Computer Scientist” aka Principles of
Computing
– Upper-level courses
• Graduate-level courses
– Computational arts and sciences
• E.g., entertainment technology, computational linguistics, …,
computational finance, …, computational biology,
computational astrophysics
• Post-graduate
– Executive and continuing education, senior citizens
– Teachers, not just students
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Question and Challenge to Community
What are effective ways of learning (teaching)
computational thinking by (to) children?
- What concepts can students best learn when? What should we teach when?
What is our analogy to numbers in K, algebra in 7, and calculus in 12?
- We uniquely also should ask how best to integrate The Computer
with learning and teaching the concepts.
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Simple Daily Examples
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Looking up a name in an alphabetically sorted list
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Standing in line at a bank, supermarket, customs & immigration
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Putting things in your child’s knapsack for the day
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Taking your kids to soccer, gymnastics, and swim practice
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Cooking a gourmet meal
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Cleaning out your garage
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Storing away your child’s Lego pieces scattered on the LR floor
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Doing laundry, getting food at a buffet
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Even in grade school, we learn algorithms (long division, factoring, GCD, …)
and abstract data types (sets, tables, …).
– Linear: start at the top
– Binary search: start in the middle
– Performance analysis of task scheduling
– Pre-fetching and caching
– Traveling salesman (with more constraints)
– Parallel processing: You don’t want the meat to get cold while you’re cooking the
vegetables.
– Keeping only what you need vs. throwing out stuff when you run out of space.
– Using hashing (e.g., by shape, by color)
– Pipelining the wash, dry, and iron stages; plates, salad, entrée, dessert stations
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External Community Response
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External Community …
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Outside of CMU
Outside of Computer Science
Outside of Science and Engineering
Outside of US
• Impact on research and education through NSF
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Research Impact
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“Computational Thinking,” Andrew Hebert
(Director, MSR/Cambridge), p. 20, 2006.
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Volume 440
Number 7083 pp
383-580,
March 23, 2006
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Spearheaded by Alan Bundy
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Also, report by Conrad Taylor on my talk at
Grand Challenges in Computing Conference,
British Computer Society, London, March 2008
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Reach Through NSF
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CDI: Cyber-Enabled Discovery and Innovation
Computational Thinking for Science and Engineering
• Paradigm shift
– Not just our metal tools (transistors and wires) but also our
mental tools (abstractions and methods)
• It’s about partnerships and transformative research.
– To innovate in/innovatively use computational thinking; and
– To advance more than one science/engineering discipline.
• Fortuitous timing for me …
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CDI Response
• 1800 Letters of Intent, 1300 Preliminary
Proposals, 200 Final Proposals, 36 Awards
• FY08: ~$50M invested by all directorates and
offices
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Range of Disciplines in CDI Awards
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Aerospace engineering
Atmospheric sciences
Biochemistry
Biophysics
Chemical engineering
Communications science and engineering
Computer science
Geosciences
Linguistics
Materials engineering
Mathematics
Mechanical engineering
Molecular biology
Nanocomputing
Neuroscience
Robotics
Social sciences
Statistical physics
… advances via Computational Thinking
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Range of Societal Issues Addressed
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Cancer therapy
Climate change
Environment
Visually impaired
Water
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Educational Impact
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Colleges and Universities are Revisiting Curricula
• Carnegie Mellon: Tom Cortina’s 15-105
• MIT: John Guttag’s 6.00 (for freshmen)
• Georgia Tech:
– UG: “Threads”, Mark Guzdial, “Learning Computing with Robots,”
Tucker Balch and Deepak Kumar (Bryn Mawr)
– Grad: Alexander Gray and Nick Feamster
• Columbia: Al Aho
• Princeton: PICASso, for non-CS graduate students
• …
• Villanova, Haverford, Bryn Mawr, Georgetown, …
• U Wisconsin-La Crosse, …
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Microsoft Corporation
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Reach Through NSF
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CISE
• CPATH
– Revisiting undergrad curricula
– Enlarge scope to include outreach to K-12
• Broadening Participation in Computing
– Women, underrepresented minorities, people with
disabilities
– Alliances and demo projects
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CPATH Awards Specific to CT
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Brown
De Paul
Georgia State
North Carolina Agricultural and Technical State University
Middle Tennessee State University
Penn State University
Towson University
University of Illinois, Urbana-Champaign
University of Nebraska
University of Texas, El Paso
Utah State
Villanova
Virginia Tech
Washington State
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Broadening Participation
• AP Revision
– Academic Advisory group includes NSF, ACM, CSTA,
university reps (e.g., Cortina), and high school teachers
• New Image for Computing
– Working with Image of Computing and WGBH (Boston)
• Alliances
– e.g., ARTSI (HBCU/R1 Robotics, Touretzky, et al.)
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Beyond CISE
Challenge to Community: What is an effective way of
teaching (learning) computational thinking to (by)
K-12?
• Computational Thinking for Children
– National Academies Computer Science and
Telecommunications Board (CSTB): Workshops on CT for
Everyone. Collaborating with Board on Science Education.
• Cyber-enabled Learning
– Education and Human Resources (EHR) Directorate,
Office of Cyberinfrastructure (OCI), Social, Behavioral,
and Economic Sciences (SBE), and CISE
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Other Educational and Outreach Activities
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Peter Denning’s “Rebooting Computing Summit”, Jan 2009
Andy van Dam is CRA-E “Education Czar”
ACM Ed Council
CS4HS: Lenore Blum’s vision: “CS4HS in every state!”
Women@SCS Roadshow
Image of Computing Task Force: Jill Ross, Rick Rashid,
Jim Foley
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Two and a Half Years Later: Research
Computing
Community
NSF
CMU
NEH,
ILMS
Microsoft
Computational
Thinking
CDI
Center for CT
all sciences and engineering
computer science, arts, humanities, …
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Research Challenges and Opportunities
• CT for other sciences and engineering and beyond
– It’s inevitable
– They need us, they want us
• It’s about abstractions and symbolic “calculations”
not just number-crunching
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Two and a Half Years Later: Education
CRA-E
Computing
Community
ACM-Ed
CSTA
NSF
Rebooting
National Academies
Computational
Thinking
BPC
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CPATH
workshops
AP
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Educational Challenges and Opportunities
• Science, Technology, Engineering, and
Mathematics (STEM) Education continues to be a
huge challenge
• ~15,000 school districts in the US
• HS science and math teachers
• Public perception of STEM disciplines
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Bigger Picture: Societal and Political Issues
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Climate Change
Energy
Environment
Economics
Human Behavior
Sustainability
Healthcare
National security
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Competitiveness, Innovation, Leadership
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Broad Charge To You
• Help the community define a strategy for
determining what, if anything, of computational
thinking makes sense to teach at the K-12 level.
• where
– community = computer scientists, educators,
learning/cognitive scientists
– what, if anything, of computational thinking =
concepts/principles/skills underlying computing
– K-12 = especially early grades
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Questions
• What are the fundamental concepts of CT?
Elemental?
• What would be an effective ordering of these
concepts?
• How best should we integrate The Computer with
teaching the concepts?
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Long-term view
• Analogy
– What physics did for itself decades ago.
– What math did and continues to do periodically.
• It’s NOT just curriculum design.
– How do children learn what when?
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Short-term agenda (suggestion)
• Workshop 1
– Identify
concepts/principles/skills
• In parallel, work with or
track other efforts and
organizations.
• Community input
• Workshop 2
– Propose one or more models of
“sequencing” the concepts
– Propose a strategy for
incorporating it in an early
grade or K-12 curriculm.
– Next steps?
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Thanks for Helping to Spread the Word!
Make computational thinking commonplace!
To fellow faculty, students, researchers,
administrators, teachers, parents, principals,
guidance counselors, school boards, teachers’ unions,
congressmen, policy makers, …
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Thank you!