1 / 65

School: St Peters Lutheran College

We welcome our next speaker. Name : Paul Herring. Title: Computational Thinking in the Senior School: New Traffic on an old Road. School: St Peters Lutheran College. Some of my qualifications/authority to speak on this issue:

bern
Download Presentation

School: St Peters Lutheran College

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. We welcome our next speaker Name: Paul Herring Title: Computational Thinking in the Senior School: New Traffic on an old Road School:St Peters Lutheran College

  2. Some of my qualifications/authority to speak on this issue: Physicist – ‘Microwave Refraction in the Lower Troposphere’ Professional Computer Programmer – ‘EDOMS’ IT Consultant: Trainer & Author of Office Application Textbooks, Systems Integrator; Hardware & Software Sales and Support; Teacher: HOD IT, HOD Maths, Campus Manager, Asst. Dean of Studies, IT Strategic Development Advisor, Adult Ed. QLD Education Department Panels: ITS (IPT, Physics) About Paul Herring M.Sc(Physics), Dip. Tchg., MACS (Snr) CP, Cert III (IT) , Cert IV

  3. Visual Basic.Net Lua(Corona) Lingo (Director) Action Script (Flash) Javascript Scratch small Basic DB Scripting • Filemaker Pro 12 & Access 2010 GameMaker Lego Robotics ... Recent Teaching of Coding in: The New Traffic: Computational Thinking The Old Road: Computer Science/Programming

  4. What is Computational Thinking & why is it important Tales from the Tablet face • doing Computational Thinking in the classroom • issues and potential The future of Computational Thinking • some suggestions Overview

  5. “Every era demands--and rewards--different skills. In different times and different places, we have taught our children to grow vegetables, build a house, forge a sword or blow a delicate glass, bake bread, create a soufflé, write a story or shoot hoops. Now we are teaching them to code. We are teaching them to code, however, not so much as an end in itself but because our world has morphed: We need to teach coding to help our students craft their future.” • https://www.edsurge.com/guide/teaching-kids-to-code The 4th R (with no R!): Reading, wRiting, aRithmetic & Computational Thinking

  6. “Fast forward to 2020. What job skill must you have? • Coding What we do know is, for the foreseeable future, coding is one of the most important and desirable skills there is, no matter how it evolves.” • http://mashable.com/2013/04/30/job-skill-future-coding/ Gary Stager: 3 game changers: • fabrication (3D printing); • physical computing (robotics); • programming - ground swell of coding - see http://www.inventtolearn.com/about-the-book/ Coding is the new black

  7. “Computational thinking will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, let’s add computational thinking to every child's analytical ability. Computational thinking is an approach to solving problems, building systems, and understanding human behavior that draws on the power and limits of computing.” Prof. Jeannette M. Wing

  8. "Computational Thinking is a fundamental analytical skill that everyone, not just computer scientists, can use to help solve problems, design systems, and understand human behavior. As such, ... computational thinking is comparable to the mathematical, linguistic, and logical reasoning that is taught to all children. This view mirrors the growing recognition that computational thinking (and not just computation) has begun to influence and shape thinking in many disciplines • Earth sciences, biology, and statistics, for example. Moreover, computational thinking is likely to benefit not only other scientists but also everyone else • bankers, stockbrokers, lawyers, car mechanics, salespeople, health care professionals, artists, and so on.“ • from the preface of COMPUTATIONAL THINKING - REPORT OF A WORKSHOP ON THE SCOPE AND NATURE OF COMPUTATIONAL THINKING - (c) National Academy of Sciences. What is Computational Thinking?

  9. "Computational Thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent.“ - Cuny, Snyder, Wing “Computer science is having a revolutionary impact on scientific research and discovery. Simply put, it is nearly impossible to do scholarly research in any scientific or engineering discipline without an ability to think computationally. The impact of computing extends far beyond science, however, affecting all aspects of our lives. To flourish in today's world, everyone needs computational thinking.“ • Center for Computational Thinking at Carnegie Mellon University What is Computational Thinking?

  10. “Computational Thinking (CT) is a problem-solving process that includes (but is not limited to) the following characteristics: Formulating problems in a way that enables us to use a computer and other tools to help solve them. Logicallyorganizing and analyzing data Representing data through abstractions such as models and simulations Automating solutions through algorithmic thinking (a series of ordered steps) Identifying, analyzing, and implementing possible solutions with the goal of achieving the most efficient and effective combination of steps and resources Generalizing and transferring this problem solving process to a wide variety of problems” • International Society for Technology in Education (ISTE) & Computer Science Teachers Association (CSTA), USA Operational Definition for K–12 Education

  11. “These skills are supported and enhanced by a number of dispositions or attitudes that are essential dimensions of CT. These dispositions or attitudes include: Confidence in dealing with complexity Persistence in working with difficult problems Tolerance for ambiguity The ability to deal with open ended problems The ability to communicate and work with others to achieve a common goal or solution” - International Society for Technology in Education (ISTE) & Computer Science Teachers Association (CSTA), USA Operational Definitionfor K–12 Education

  12. "Computer programming is the new international language of business, and we're not teaching it in schools. Why is that? ... The fact it's not happening in junior highs and high schools is a shame given the demand for developers. There's a huge talent crunch, and people aren't connecting the dots. Parents and teachers are not talking about the need and encouraging it.“ • Aaron Skonnard, CEO of PluralSight(Trains 250,000 professionals globally -$16 million in revenue p.a) The new international language of business

  13. A generation of middle and high school students moves forward without even a cultivated awareness of computational influences on diverse fields of human endeavor.  In high schools and college, misconceptions and sheer lack of awareness about computer science, as well as sub-optimal early introductory Computer Science experiences exact a heavy enrollment toll. Exposure to computing in the K-12 ecosystem could remedy this malaise--provided it’s done right. • Shuchi Grover- computer scientist and educator Lack of Computational Thinking in Curriculum

  14. ‘A survey for the Guardian (UK) shows that so far 33% of boys and just 17% of girls have learned any computer coding skills at school’ ‘Computer science must be taught as a subject in schools or the UK could lose its globally competitive position.’ • Mike Short, President, The Institution of Engineering and Technology, UK ‘Programming should be part of the primary mathscurriculum. Learning to code should be seen in the same way as learning the skill of handwriting so children can then use it as a tool for solving problems in a wider context. • Conrad Wolfram, WolframAlpha.com (From Louise Tickle, The Guardian, Tuesday 21 August 2012) The UK Scene

  15. In NSW (2011) < 6% of Year 12’s studied any IT subject (in terms of the girls it’s under 2%). Yet around 67% took Mathematics. “No student entering a Science or Engineering degree would even consider avoiding Mathematics. Unfortunately, the same cannot be said for either ICT literacy (the equivalent of numeracy) or Computer Science (the equivalent of Mathematics like algebra and calculus).” • Dr James Curran, School of Information Technologies, University of Sydney National Computer Science Schoolhttps://groklearning.com/challenge Australiais worse!

  16. ‘Education Secretary Michael Gove sets out plans for the national curriculum’ (July 2013): Other significant changes .... and perhaps the most significant change of all is the replacement of ICT with computing. Instead of just learning to use programmes created by others, it is vital that children learn to create their own programmes. These changes will reinforce our drive to raise standards in our schools. They will ensure that the new national curriculum provides a rigorous basis for teaching, provides a benchmark for all schools to improve their performance, and gives children and parents a better guarantee that every student will acquire the knowledge to succeed in the modern world. ... schools have a year to prepare to teach it from September 2014. • https://www.gov.uk/government/speeches/education-reform-schools How is the UK responding?

  17. Career Growth STEM= Science, Technology, Engineering and Mathematics

  18. Degrees vs Jobs STEM= Science, Technology, Engineering and Mathematics

  19. “This is an amazing time to go into computing, with unprecedented opportunities. Computers are a ubiquitous and growing presence in all aspects of modern society, and thus there is huge and increasing demand for computing professionals that is far from being met by the profile of today's graduates. Computing-related careers are some of the most versatile, creative, and satisfying career choices you can make, and computational thinking and skills are valuable complements to virtually all other career areas.” • Maggie Eppstein, Ph.D. Chair of Computer Science, University of Vermont Career Prospects:

  20. “Whether your passion is to • uncover the secrets of the human genome, • create intelligent robots, • bring history alive through mobile apps, • prevent terrorism, • understand human social phenomena, • play the stock market, • create digital art, • improve health care, • or invent the technologies of the future, ... computing is central to these and most modern endeavours.” - Maggie Eppstein, Ph.D. Chair of Computer Science, University of Vermont Career Prospects:

  21. IT Careers – 4 Streams

  22. Nobel prize-winner David Hubel of Harvard University (Medicine 1981 -Research on information-processing in the visual system) in 1995: “... This abiding tendency for attributes such as form, colour and movement to be handled by separate structures in the brain immediately raises the question how all the information is finally assembled, say, for perceiving a bouncing red ball. These obviously must be assembled—but where and how, we have no idea.“ • http://www.jameslefanu.com/articles/articlesscience-science%E2%80%99s-dead-end Great questions and careers await:

  23. “Improved technologies for observing and probing biological systems has only led to discoveries of further levels of complexity that need to be dealt with. This process has not yet run its course. We are far away from understanding cell biology, genomes, or brains, and turning this understanding into practical knowledge. The complexity break is very apparent ...” • ‘Systems biology. Modular biological complexity’ by Koch C., Science, August 2012 ‘complexity break’ - the resistance of biological systems to computer analysis. Great questions and careers await:

  24. (based on global energy consumption trends): • Comeback of governments • Digitization • The Internet of things, • Automation everywhere, and • Intelligent alarming • Everything as a service • Sustainability • Geographical shift • Augmented reality, • Wearable devices, and • Home automation. - Simon Fuller and Michael Postula, Schneider-Electric(ACS Seminar: Brisbane 21 August) CT & the Top 5 Megatrends

  25. Smart cities A safer world A simpler world An emerging world A world of service A greener world The three principal ramifications of these trends are: • Business model disruption • Competencies and skill sets of your people • Segmentation - end-user solutions - customized and personalized - Simon Fuller and Michael Postula, Schneider-Electric (ACS Seminar: Brisbane 21 August) CT & the Top Megatrends

  26. Some examples: Monash University - strategic research flagship programs: • Computational Biology • Machine Learning • Modelling, Optimisation and Visualisation University of Queensland: ‘Computational Science’ now a degree major University of Sydney: Computational Science The School of Physics : Junior levels COSC 1003 Introduction to Computational Science COSC 1903 Introduction to Computational Science (Advanced) Senior level COSC 3011 Scientific Computing COSC 3911 Scientific Computing (Advanced) University Recognition

  27. “To understand the living world, biologists must analyze and interpret enormous amounts of data and extremely complex systems. Consequently, they are increasingly dependent on computational approaches that evaluate data and model biological processes. The Computational Workshop for the Life Sciences Classroom is designed for teachers and lecturers in the life sciences, to empower them to inspire and inform their students.” • MonashUni Courses in Computational Thinking:

  28. Understand which aspects of a problem are amenable to computation Evaluate the match between computational tools and techniques and a problem Understand the limitations and power of computational tools and techniques Apply or adapt a computational tool or technique to a new use Recognize an opportunity to use computation in a new way, Apply computational strategies such divide and conquer in any domain. Computational Thinking means being able to:

  29. Apply new computational methods to their problems, Reformulate problems to be amenable to computational strategies, Discover new science through analysis of large data Ask new questions that were not thought of or dared to ask because of scale, but which are easily addressed computationally Explain problems and solutions in computational terms. Computational Thinking for scientists, engineers, & other professionals also means being able to:

  30. Algorithms in nature: the convergence of systems biology and computational thinking “Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. Similar mechanisms and requirements are shared by computational and biological processes - Being applied to problems related to coordination, network analysis, and tracking and vision. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future.” • SaketNavlakha& ZivBar-Joseph, Lane Center for Computational Biology and Machine Learning Department, School of Computer Science, Carnegie Mellon University. 8 November 2011 Computational Thinking & Biology

  31. Two significant areas: Biosemiotics: Biosemioticsis the characterization of the symbolic representations within life, which is filled with digitally-coded symbolic messages. Biocybernetics: Biocyberneticsinvolves self-sustaining systems that integrate different levels of information and its processing, including controls and feedback, within biological systems.  CT & Bioinformatics:

  32. “For functional communication (including controls) to occur, both sender and receiver of each communication step must know the communication protocol and how to handle the message. In each cell, there are multiple OSs, multiple programming languages, encoding/ decoding hardware and software, specialized communications systems, error detection and correction mechanisms, specialized input/output channels for organelle control and feedback, and a variety of specialized ‘devices’ to accomplish the tasks of life” • ‘Programming of Life’ Dr. Donald E Johnson CT & Bioinformatics

  33. “Here, we report on the design, synthesis, and operation of a rotaxane-based small-molecule machine in which a functionalized macro-cycle operates on a thread containing building blocks in a predetermined order to achieve sequence-specific peptide synthesis. The design of the artificial molecular machine is based on several elements that have analogs in either ribosomal or non-ribosomal protein synthesis: Reactive building blocks (the role played by tRNA-bound amino acids) are delivered in a sequence determined by a molecular strand (the role played by mRNA).” • ‘Sequence-Specific Peptide Synthesis by an Artificial Small-Molecule Machine’ Science, Vol. 339 no. 6116 pp. 189-193 (11 January 2013) They write that their machine "is a primitive analog of the ribosome." Computational Biology & Reverse Engineering

  34. “All known life is cybernetic. The key to understanding life is controls, not constraints.... Sophisticated functions must be instructed or actually computed by prescriptive information . Prescriptive informationmost often presents as a linear digital string of symbols representing decision node, logic gate, or configurable switch-setting choices. ” • 'Constraints vs Controls' by David L. Abel, The Open Cybernetics & Systemics Journal, 2010, 4, 14-27 CT & Cybernetics

  35. Prescriptive information is an algorithmic subset of functional information. Prescriptive information contains instructions to accomplish objectives based on data supplied during the execution of an algorithm Biological systems have multiple semiotic coding systems for • transcription • communication • translation ... These message systems use techniques such as • overlapping genes, • messages within messages, • multi-level encryption • etc. Prescriptive information

  36. “From the information perspective, the genetic system is a pre-existing operating system of unknown origin that supports the storage and execution of a wide variety of specific genetic programs (the genome applications), each program being stored in DNA.” Donald Johnson http://www.scienceintegrity.org/FirstGeneCh10.pdf CT & Over-Lapping Gene Coding

  37. “Romans 3:20 “For by works of the law no human being will be justified in his sight, since through the law comes knowledge of sin.” Classic algorithmic selection, or if-then-else construct. This phrase has the logical form:“For <condition B>, since <cause A>” or more clearly, “<Condition B> is true because of <Cause A>”. That is, <Cause or Reason A> leads to the conclusion of <Condition or Statement B>. Now we can analyse this passage by inserting our alternative understandings of ‘works of the law’ into this logical construct, and see whether any actually make senselogically. “ - see ‘Defending the Apostle Paul: Weighing the Evidence’ p60 Computational Thinking in Theology

  38. Many of the concepts, skills, and dispositions are not new. So how is Computational Thinkingdifferent from say, critical thinking or mathematical thinking? It is a unique combination of thinking skills that, when used together, provide the basis of a new and powerful form of problem solving. It is more tool oriented. It makes use of familiar problem solving skills such as: • trial and error, • iteration, and even • guessing in contexts where they were previously impractical but which are now possible because they can be automated and implemented at much higher speeds. How then is CT Different?

  39. algorithms • sequences, • loops/iterations • parallelism, • events, • conditionals/selection • operators, • & data cryptography machine intelligence computational biology search recursion heuristics Critical Thinking skills Entrepreneurial enabling (innovation) • for more detail see ACEC 2012 Presentation The Elements of Computational Thinking:

  40. A return of sorts to the ‘old road’, to the traditional Computer Science course, plus new areas such as: • Game Design, Cryptography & Computational Biology Students are powerfully enabled to be creative producers, not just passive users. Computational Thinking is therefore • expanding horizons & opening new avenues for creativity Where this is leading

  41. One of the two Technology subjects are core to end of Yr 8 Optional at Year 9 &10 ICT for users (embedded/integrated) Digital Technology – for creators/developers Only 4% of curriculum time wise – same as Geography! Application of computational thinking &use of information systems as well as critical thinking skills. May include some online cyber-safety ACARA Digital Technologies

  42. Computational Thinking in the Classroom

  43. Computational Thinking in the Classroom Scratch, Stencyl ...

  44. CT in the Classroom Corona/Lua & Unity 3D

  45. Edmodo & LearnStreet Javascript & Python

  46. Code Remix/Transfer Issues

  47. Decades of research with children suggests that young learners who may be programming don’t necessarily learn problem solving well. And many, in fact, struggle with algorithmic concepts especially if they are left to tinker in programming environments, or if the learning is not scaffolded and designed using the right problems and pedagogies.  Recent research studies suggest that tween and teen student projects may point to apparent fluency as evidenced by the computational concepts used in their projects. However, probing deeper sometimes reveals significant conceptual chasms in their understanding of the computing constructs that their programs employ. • ShuchiGrover, Computer Scientist & Educator Not just about Coding – Algorithmic Design

  48. Scratch: Pong vs Giving Change

  49. Scratch implementation Change algorithm

  50. SDC’s & NS Charts: add nss charts http://structorizer.fisch.lu/

More Related