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  2008 Jeannette M. Wing

Outline Computational Thinking Research and Education Implications 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 CT: 1.5 Years Later Jeannette M. Wing

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, … Thinking computationally not programming Fundamental skill not rote skill Computing not computers: pervasive computing/computers is passe: 20th Century! “used by” ambitious. Certainly needed to function in modern society In research: long-term; in education: nearer-term (outreach, diversity, changing society’s view of our field) J.M. Wing, “Computational Thinking,” CACM Viewpoint, March 2006, pp. 33-35. http://www.cs.cmu.edu/~wing/ CT: 1.5 Years Later Jeannette M. Wing

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! CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

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) CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

Research Implications CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

CT in Other Sciences, Math, and Engineering 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 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 CT: 1.5 Years Later Jeannette M. Wing

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 - 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 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 CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

CT for Society 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 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 CT: 1.5 Years Later Jeannette M. Wing

CT for Society Entertainment Arts Sports - 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 CT for Society 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 CT: 1.5 Years Later Jeannette M. Wing

Educational Implications CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

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. CT: 1.5 Years Later Jeannette M. Wing

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, …). The Central-Queue policy is provably optimal when job size variability is low.  WFM (in NY) goes with the bank-line scheme because (maybe) there is less variability in job size because prices are so high, people buy less. Still should have an express lane for < 8 items or less. CT: 1.5 Years Later Jeannette M. Wing

Two and A Half Years Later CT: 1.5 Years Later Jeannette M. Wing

External Community Response CT: 1.5 Years Later Jeannette M. Wing

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

Research Impact CT: 1.5 Years Later Jeannette M. Wing

“Computational Thinking,” Andrew Hebert (Director, MSR/Cambridge), p CT: 1.5 Years Later Jeannette M. Wing

Volume 440 Number 7083 pp 383-580, March 23, 2006 CT: 1.5 Years Later Jeannette M. Wing

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Spearheaded by Alan Bundy CT: 1.5 Years Later Jeannette M. Wing

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

CACM Viewpoint Translated into Spanish and Chinese CT: 1.5 Years Later Jeannette M. Wing

Reach Through NSF CT: 1.5 Years Later Jeannette M. Wing

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 ~650 Data to Knowledge, 500 Understanding Complexity, < 200 Virtual Organizations 90 PDs (~35 from CISE) and 6 (4 from CISE) admin staff across the agency helped CT: 1.5 Years Later Jeannette M. Wing

CDI Response 1800 Letters of Intent, 1300 Preliminary Proposals, 200 Final Proposals, 36 Awards FY08: ~$50M invested by all directorates and offices 21 + 15 = 36 CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

Range of Societal Issues Addressed Cancer therapy Climate change Environment Visually impaired Water CT: 1.5 Years Later Jeannette M. Wing

Educational Impact CT: 1.5 Years Later Jeannette M. Wing

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 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, … CT: 1.5 Years Later Jeannette M. Wing

CT: 1.5 Years Later Jeannette M. Wing

© 2008 Microsoft Corporation CT: 1.5 Years Later © 2008 Microsoft Corporation Jeannette M. Wing

Reach Through NSF CT: 1.5 Years Later Jeannette M. Wing

CISE CPATH Broadening Participation in Computing 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 CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

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.) CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

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!” Women@SCS Roadshow Image of Computing Task Force: Jill Ross, Rick Rashid, Jim Foley CT: 1.5 Years Later Jeannette M. Wing

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

Two and a Half Years Later: Education ACM-Ed CRA-E CSTA Computing Community NSF Rebooting K-12 National Academies workshops Computational Thinking 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 CPATH BPC AP CT: 1.5 Years Later Jeannette M. Wing

Challenges and Opportunities for Computer Science Reality Check: Challenges and Opportunities for Computer Science Looking Ahead: CT: 1.5 Years Later Jeannette M. Wing

Big Data and Big Systems Big Science means Big Data and Big Systems CT: 1.5 Years Later Jeannette M. Wing

LIGO and its successor, Advanced LIGO, are instruments whole sole goal is to detect gravitational waves (of cosmic origin). Einstein’s Theory of General Relativity predicted the existence of gravitational waves but we have never observed them. These instruments are made up of lots and lots of mirrors in such a way that simplistically speaking if a gravitational wave passed through it would perturb a light beam from a laser. The international LIGO Scientific Collaboration (LSC) is a growing group of researchers, some 600 individuals at roughly 40 institutions, working to analyze the data from LIGO and other detectors. LIGO's mission is to directly observe gravitational waves of cosmic origin. These waves were first predicted by Einstein's Theory of General Relativity in 1916, when the technology necessary for their detection did not yet exist. CT: 1.5 Years Later Jeannette M. Wing

The Einstein@Home program processes data from the LIGO and GEO instruments using Fast Fourier Transforms. The resulting signals are then analyzed using a method called matched filtering. This method involves the computation of hypothetical signals that might result if there were a physically plausible source of gravitational waves in the part of the sky being examined. The measured signal is then compared to the hypothetical signal. A matching signal is a candidate for further examination by more sophisticated analysis. CT: 1.5 Years Later Jeannette M. Wing

Examples The goal is to detect neutrinos in the high energy range. The instrument is made up of an array of strings of optical sensors, each string inserted into a a big hole in the ice. The IceCube Neutrino Detector is a neutrino telescope currently under construction at the South Pole. Like its predecessor, the Antarctic Muon And Neutrino Detector Array (AMANDA), IceCube is being constructed in deep Antarctic ice by deploying thousands of spherical optical sensors (photomultiplier tubes, or PMTs) at depths between 1,450 and 2,450 meters. The sensors are deployed on "strings" of sixty modules each, into holes in the ice melted using a hot water drill. CT: 1.5 Years Later Jeannette M. Wing

CT: 1.5 Years Later Jeannette M. Wing

It is estimated to generate 15 PB annually once in operation. One hope is that the LHC will be able to observe the Higgs Boson which would then confirm predictions and fill in the gaps in the Standard Model of theoretical physics. The verification of the Higgs Boson would be a step in the search for a Grand Unified Theory (in particular the Higgs boson would help explain why gravitation is weak compared to the other three forces). The LHC is a huge circular tunnel of superconducting magnets where you shoot two proton beams in opposite directions toward each other to cause the collision. It is estimated to generate 15 PB annually once in operation. The Large Hadron Collider (LHC) is a particle accelerator complex intended to collide opposing beams of 7 TeV protons. Its main purpose is to explore the validity and limitations of the standard model, the current theoretical picture for particle physics. This model is known to break down at a certain high energy level. CT: 1.5 Years Later Jeannette M. Wing

ALMA is a telescope made up of 80 antennas looking into the celestial sky, to probe the cold Universe (regions that are optically dark but shine brightly in the millimeter portion of the electromagnetic spectrum), making observations possibly only now in space. At its finest resolution, it will reach a factor of ten better than the Hubble Space Telescope. The Atacama Large Millimeter/submillimeter Array (ALMA), will be a single research instrument composed of up to 80 high-precision antennas, located on the Chajnantor plain of the Chilean Andes in the District of San Pedro de Atacama, 5000 m above sea level. ALMA will enable transformational research into the physics of the cold Universe, regions that are optically dark but shine brightly in the millimeter portion of the electromagnetic spectrum. Providing astronomers a new window on celestial origins, ALMA will probe the first stars and galaxies, and directly image the formation of planets. ALMA will operate at wavelengths of 0.3 to 9.6 millimeters, where the Earths atmosphere above a high, dry site is largely transparent, and will provide astronomers unprecedented sensitivity and resolution. The up to sixty-four antennas of the 12 m Array will have reconfigurable baselines ranging from 150 m to 18 km. Resolutions as fine as 0.005" will be achieved at the highest frequencies, a factor of ten better than the Hubble Space Telescope. CT: 1.5 Years Later Jeannette M. Wing

CT: 1.5 Years Later Jeannette M. Wing

NEON is planned to be a continental scale set of distributed sensor networks linked by advanced cyberinfrastructure. It is to study impacts of climate change, land-use change, and invasive species – on ecology. It’s actually a configuration of a set of fixed towers of sensors and a movable suite of sensor arrays that will sweep the continent. CT: 1.5 Years Later Jeannette M. Wing

OOI is a networked infrastructure of sensors to be deployed off the coast of the Pacific Northwest (Juan de Fuca strait) to look at global, regional, coastal effects due to changes in many aspects of the ocean: physical, biology, chemical, ecological changes. CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

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 CT: 1.5 Years Later Jeannette M. Wing

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

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, … Thinking computationally not programming Fundamental skill not rote skill Computing not computers: pervasive computing/computers is passe: 20th Century! “used by” ambitious. Certainly needed to function in modern society In research: long-term; in education: nearer-term (outreach, diversity, changing society’s view of our field) CT: 1.5 Years Later Jeannette M. Wing

Thank you!

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