Computational Thinking: Two and a Half Years Later 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

2CT: 1.5 Years Later 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

3CT: 1.5 Years Later My Grand Vision for the Field Computational thinking will be a fundamental skill used by everyone in the world by the middle of the 21 st 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, … J.M. Wing, “Computational Thinking,” CACM Viewpoint, March 2006, pp

4CT: 1.5 Years Later Examples of Computational Thinking How difficult is this problem and how best can I solve it? –Theoretical computer science gives precise meaning to these and related questions and their answers. C.T. is thinking recursively. C.T. is reformulating a seemingly difficult problem into one which we know how to solve. –Reduction, embedding, transformation, simulation C.T. is choosing an appropriate representation or modeling the relevant aspects of a problem to make it tractable. C.T. is interpreting code as data and data as code. C.T. is using abstraction and decomposition in tackling a large complex task. C.T. is judging a system’s design for its simplicity and elegance. C.T. is type checking, as a generalization of dimensional analysis. C.T. is prevention, detection, and recovery from worst-case scenarios through redundancy, damage containment, and error correction. C.T. is modularizing something in anticipation of multiple users and prefetching and caching in anticipation of future use. C.T. is calling gridlock deadlock and avoiding race conditions when synchronizing meetings. C.T. is using the difficulty of solving hard AI problems to foil computing agents. C.T. is taking an approach to solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science. Please tell me your favorite examples of computational thinking!

5CT: 1.5 Years Later 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

6CT: 1.5 Years Later 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)

7CT: 1.5 Years Later 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

8CT: 1.5 Years Later Research Implications

9CT: 1.5 Years Later 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 - Protein kinetics can be modeled as computational processes - Cells as a self-regulatory system are like electronic circuits Credit: Wikipedia Brain Science - Modeling the brain as a computer - Vision as a feedback loop - Analyzing fMRI data with machine learning Credit: LiveScience

10CT: 1.5 Years Later CT in Other Sciences, Math, and Engineering 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 Credit: NASA Credit: University of Minnesota 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

11CT: 1.5 Years Later CT in Other Sciences, Math, and Engineering Mathematics - 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 Credit: Wikipedia Astronomy - Sloan Digital Sky Server brings a telescope to every child - KD-trees help astronomers analyze very large multi-dimensional datasets Credit: SDSS Engineering (electrical, civil, mechanical, aero & astro,…) - 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 Credit: Boeing

12CT: 1.5 Years Later 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

13CT: 1.5 Years Later CT for Society 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 - Sherlock Project on crime scene investigation Medicine - Robotic surgery - Electronic health records require privacy technologies - Scientific visualization enables virtual colonoscopy Credit: University of Utah Humanities - What do you do with a million books? Nat’l Endowment for the Humanities Inst of Museum and Library Services

14CT: 1.5 Years Later CT for Society Entertainment - Games - Movies - Dreamworks uses HP data center to renderShrek and Madagascar - Lucas Films uses 2000-node data center to produce Pirates of the Caribbean. Credit: Dreamworks SKG Credit: Carnegie Mellon University Sports - Lance Armstrong’s cycling computer tracks man and machine statistics - Synergy Sports analyzes digital videos NBA games Credit: Wikipedia Arts - Art (e.g., Robotticelli) - Drama - Music - Photography Credit: Christian Moeller

15CT: 1.5 Years Later Educational Implications

16CT: 1.5 Years Later Pre-K to Grey K-6, 7-9, 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

17CT: 1.5 Years Later 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.

18CT: 1.5 Years Later Simple Daily Examples Looking up a name in an alphabetically sorted list –Linear: start at the top –Binary search: start in the middle Standing in line at a bank, supermarket, customs & immigration –Performance analysis of task scheduling Putting things in your child’s knapsack for the day –Pre-fetching and caching Taking your kids to soccer, gymnastics, and swim practice –Traveling salesman (with more constraints) Cooking a gourmet meal –Parallel processing: You don’t want the meat to get cold while you’re cooking the vegetables. Cleaning out your garage –Keeping only what you need vs. throwing out stuff when you run out of space. Storing away your child’s Lego pieces scattered on the LR floor –Using hashing (e.g., by shape, by color) Doing laundry, getting food at a buffet –Pipelining the wash, dry, and iron stages; plates, salad, entrée, dessert stations Even in grade school, we learn algorithms (long division, factoring, GCD, …) and abstract data types (sets, tables, …).

19CT: 1.5 Years Later Two and A Half Years Later

20CT: 1.5 Years Later External Community Response

21CT: 1.5 Years Later External Community … Outside of CMU Outside of Computer Science Outside of Science and Engineering Outside of US Impact on research and education through NSF

22CT: 1.5 Years Later Research Impact

23CT: 1.5 Years Later “Computational Thinking,” Andrew Hebert (Director, MSR/Cambridge), p. 20, 2006.

24CT: 1.5 Years Later Volume 440 Number 7083 pp , March 23, 2006

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26CT: 1.5 Years Later Spearheaded by Alan Bundy

27CT: 1.5 Years Later Also, report by Conrad Taylor on my talk at Grand Challenges in Computing Conference, British Computer Society, London, March 2008

28CT: 1.5 Years Later CACM Viewpoint Translated into Spanish and Chinese

29CT: 1.5 Years Later Reach Through NSF

30CT: 1.5 Years Later CDI: Cyber-Enabled Discovery and Innovation 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 … Computational Thinking for Science and Engineering

31CT: 1.5 Years Later CDI Response 1800 Letters of Intent, 1300 Preliminary Proposals, 200 Final Proposals, 36 Awards FY08: ~$50M invested by all directorates and offices

32CT: 1.5 Years Later Range of Disciplines in CDI Awards 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

33CT: 1.5 Years Later Range of Societal Issues Addressed Cancer therapy Climate change Environment Visually impaired Water

34CT: 1.5 Years Later Educational Impact

35CT: 1.5 Years Later Colleges and Universities are Revisiting Curricula Carnegie Mellon: Tom Cortina’s 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 Harvard: Alyssa Goodman, Institute for Innovative Computing Northwestern: Center for Connected Learning and Computer-Based Modeling … Villanova, Haverford, Bryn Mawr, Georgetown, … U Wisconsin-La Crosse, …

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37CT: 1.5 Years Later © 2008 Microsoft Corporation

38CT: 1.5 Years Later Reach Through NSF

39CT: 1.5 Years Later 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

40CT: 1.5 Years Later CPATH Awards Specific to CT 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

41CT: 1.5 Years Later 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., ARSTI (HBCU/R1 Robotics, Touretzky, et al.)

42CT: 1.5 Years Later 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

43CT: 1.5 Years Later Other Educational and Outreach Activities 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!” Roadshow Image of Computing Task Force: Jill Ross, Rick Rashid, Jim Foley

44CT: 1.5 Years Later Two and a Half Years Later: Research Computational Thinking Computing Community NSF CMU Microsoft Center for CT computer science, arts, humanities, … CDI all sciences and engineering NEH, ILMS

45CT: 1.5 Years Later Two and a Half Years Later: Education NSF Computing Community Computational Thinking Rebooting AP CPATHBPC K-12 National Academies workshops ACM-Ed CRA-E CSTA CSTB “CT for Everyone” Steering Committee Marcia Linn, Berkeley Al Aho, Columbia Brian Blake, Georgetown Bob Constable, Cornell Yasmin Kafai, U Penn Janet Kolodner, Georgia Tech Uri Wilensky, Northwestern Bill Wulf, UVA

46CT: 1.5 Years Later Reality Check: Challenges and Opportunities for Computer Science Looking Ahead:

47CT: 1.5 Years Later Big Science means Big Data and Big Systems

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57CT: 1.5 Years Later 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

58CT: 1.5 Years Later 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

59CT: 1.5 Years Later Bigger Picture: Societal and Political Issues Climate Change Energy Environment Economics Human Behavior Sustainability Healthcare National security … Competitiveness, Innovation, Leadership

60CT: 1.5 Years Later 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, …

Thank you!

62CT: 1.5 Years Later Credits Copyrighted material used under Fair Use. If you are the copyright holder and believe your material has been used unfairly, or if you have any suggestions, feedback, or support, please contact: Except where otherwise indicated, permission is granted to copy, distribute, and/or modify all images in this document under the terms of the GNU Free Documentation license, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled “GNU Free Documentation license” (