Qsite Presentation computational thinking 2013


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Powerpoint of talk given to QSITE Conference, at Siena College, Sippy Downs, Sunshine Coast, Australia on 30th Sept. 2013.

This is almost identical to the ELH presentation so if you have listened to that SlideCast don't worry about this one - I didn't record the audio this time, though in hinddight I should have as the conversation after the talk was great and the emphasis was different.

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  • These is a potential tsunami coming – of needed change; of lack of qualifications; of serious redefinition of some of what we teach, not just how we teach.Before continue – how many IT teachers; how many maths or science or engineering? How many disliked maths at school – do you have similar feelings towards coding?
  • George Siemens – Learning Analytics – from admin sideSylvia – maker movement as part and parcel of CTThe teaching of CT – LA from the student side
  • I am not political and I'm not on any school board, but I am disappointed with how little focus there is on technology and computer technology in our schools.
  • The essence of computational thinking is in ‘thinking like a computer scientist’ when confronted with a problem. Among other things, this entails thinking logically and algorithmicallyunderstanding not only notions of flow of control in a programmatic solution but also how to systematically break down a problem and then compose an algorithmic solution.
  • At a deeper level, children need to "learn to conceptualise the problem they're creating the code to solve. It's actually very creative.“
  • Situation at SPLC <10%
  • I will address Australia’s response later on in this pres
  • It's not just the money and jobs that make Computing such an exciting profession
  • ?? comment re ‘climate change’ – will see the full picture from the big data some day
  • Peter Grant former CIO Qld govt.
  • CQUniversity uses augmented reality to coach train drivershttp://www.computerworld.com.au/article/523438/cquniversity_uses_augmented_reality_coach_train_drivers/?utm_medium=newsletter&eid=-6787&utm_source=computerworld-today-pm-edition
  • 1) A greener world 2) Smart cities - high-tech infrastructures where capabilities like integration & mobility are key. 3) A safer world - both physical and logical threats must be given equal attention, & a world in which government regulation will play an evermore critical role. 4) A simpler world - end users will demand & expect an overload of data manageable, easy and useful. 5) An emerging world - global economies are all intertwined; debt challenges will reshape business models. 6) A world of service - just about everything can be delivered as a service; cloud will play big.
  • Together then, uni’s & businesses are recognizing the need for computational thinking skills and therefore putting pressure on the K-12 educational sector to incorporate CT
  • I will now flesh this out a little
  • dna template base codes (T A C G) transcripted to RNA bases (A U G C) - then translated into 20 character alphabet of animo acids
  • Another paper (von Ballmoos et al., 2009) states: “The rotational mechanism of the ATP synthase demands ingeniously designed interfaces between rotor and stator subunits, particularly between the rotating c ring and the laterally abutted subunit a, because rotation speeds up to 500 Hz must be tolerated in the absence of a stabilizing rotor axis. This proteinous interface also acts as the critical scaffold for torque generation and ion translocation across the membrane. To prohibit charge translocation without rotation, ion leakage at the interface must be efficiently prevented.” Another good example is detailed in this paper titled: ‘Sequence-Specific Peptide Synthesis by an Artificial Small-Molecule Machine’ Science, Vol. 339 no. 6116 pp. 189-193 (11 January 2013):“Here, we report on the design, synthesis, and operation of a rotaxane-based small-molecule machine in which a functionalized macrocycle 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 nonribosomal 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). A macrocycleensures processivity during the machine's operation (reminiscent of the way that subunits of the ribosome clamp the mRNA strand) and bears a catalyst--a tethered thiol group--that detaches the amino acid building blocks from the strand and passes them on to another site at which the resulting peptide oligomer is elongated in a single specific sequence, through chemistry related to nonribosomal peptide synthesis.” They write that their machine "is a primitive analog of the ribosome." An analog is this case being a copy. A copy of a far more sophisticated design. To create such complex, even if primitive, molecular motors requires these scientists to generate the complex and specified information of their designs which is then used in making the motor. Information that reliably indicates design has such high levels of such ‘complex and specified information’ (or ‘specified complexity’).For a good overview see - http://www.evolutionnews.org/2013/05/atp_synthase_an_1072101.htmlhttp://www.sciencemag.org/content/339/6116/189.abstractSomething is complex if it is unlikely, and it is specified if it matches a pre-existing pattern.
  • Both linear digital genetic prescription using ac material symbol system and epigenetic “regulation” in molecular biology are aspects of formal control.PI arises not only out of high Shannon-bit uncertainty, but also out of high “Fit” (functional bit) content found in Functional Sequence Complexity
  • Information has three significant meanings that are important when considering the information of life: ‘functional’, ‘Shannon’ and ‘prescriptive’. including the bijective codon-based coding system (for symbolic translation) that involves transcribing, communicating, and translating the symbolic triplet nucleotide block-codes into amino acids of the proteins.
  • DNA is a storage medium that specifies all information needed to support the growth, metabolism, parts manufacturing, etc. for a specific organism via gene sub-programs.The smallest genome (though not autonomous) found so far is in the psyllidsymbiontCarsonellaruddii, which consists of a circular chromosome of 159,662 base pairs ... This genome has a high coding density (97%) with many overlapping genes with sub-coded information and a second genetic code characterizing alternative splicing ... ”“No man-made program comes close to the technical brilliance of even Mycoplasmal genetic algorithms. Mycoplasmas are the simplest known organism with the smallest known genome, to date.”– David L. Abel and Jack T. Trevors, “Three Subsets of Sequence Complexity and Their Relevance to Biopolymeric Information,” Theoretical Biology & Medical Modelling, Vol. 2, 11 August 2005, page 8
  • Consider fro a moment if and where these skills & disciplines are taught currently in the KLAs?Some use of algorithms in Maths & Science & critical thinking part of most disciplines in some way perhaps some innovation in science, technology & even hospitality but little in English and Maths
  • While schools have taught many of these areas in the past, opportunities are now being presented where schools can fully embrace those areas traditionally part of a Computer Science type course, but also introduce the fascinating new areas of endeavor such as Cryptography and Computational Biology. Coupled with the increasing enabling of application development and deployment by Senior School students, such as in the creation and deployment of mobile games using Corona and Lua for example, students are able to be powerfully enabled as creative producers, not just passive users.
  • So how might this be implemented in the classroom – let me just focus on the Computational Thinking component and in particular the coding aspect
  • While there are tools for the lower grades such as Kodable and Hopscotch I will focus on the 10-12 area
  • Lower levelsKodable & Hopscotch
  • show eBook template designed for use by students – add words, images and speech
  • ask Fady up to give some feedback on his experiences with these issues
  • http://www.cs4fn.org/programming/noughtscrosses/
  • Mention Nim? Other games using same approach - transferability
  • adaptability, fiscal responsibility, personal accountability, environmental awareness, empathy, tolerance ...
  • Autonomy, mastery, and purpose are three concepts that Dan Pink explained with great clarity in his 2009 TED talk. The essence of Pink's talk is that tangible rewards (a paycheck, a grade, a promotion) are only effective at motivating people to a certain point.Pasted from <http://electriceducator.blogspot.com.au/2011/01/reflection-fedex-project.html>   http://youtu.be/rrkrvAUbU9Y
  • Check out EstoniaHow can you help? – Ideas?
  • Qsite Presentation computational thinking 2013

    1. 1. Implementing the Digital Technologies Curriculum in the High School Justification, Issues & Ideas Paul Herring St Peters Lutheran College
    2. 2. Digital Technologies embraces Computational Thinking Justification: • What is Computational Thinking & why is it important Issues: • Tales from the Tablet face – doing Computational Thinking in the classroom – issues and potential Ideas: • The future of Computational Thinking – some suggestions Computational Thinking is @ the core:
    3. 3. • “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
    4. 4. • “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
    5. 5. • “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.” • Code – the new literacy http://www.youtube.com/watch?v=tfiw511eAB8 Prof. Jeannette M. Wing
    6. 6. • "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?
    7. 7. • "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?
    8. 8. “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.  Logically organizing 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
    9. 9. “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 Definition for K–12 Education
    10. 10. • "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
    11. 11. • 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
    12. 12. • ‘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 maths curriculum. • 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
    13. 13. The US Scene • In 2012, only 24,782 students in the United States out of over 14 million took the Computer Science Advanced Placement test. This is less than 0.7% of all AP tests taken. This at a time when five of the top ten fastest growing jobs will be in a computer related field and two of the top three top bachelors salaries are in computer science and engineering. - http://tealsk12.org/ • In a 2012 report ... noted that the United States must produce 1 million more professionals in the fields of science, technology, engineering, and mathematics (STEM) over the next decade to regain its global competitiveness. • Though women make up 50.8 percent of the U.S. population, they only represented 22.6 percent of those earning master's degrees in engineering in 2011. - http://www.usnews.com/education/best-graduate-schools/articles/2013/03/14/revamped-engineering-programs- emphasize-real-world-problem-solving
    14. 14. 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 School https://groklearning.com/challenge Australia is worse!
    15. 15. ‘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?
    16. 16. Career Growth STEM = Science, Technology, Engineering and Mathematics
    17. 17. Degrees vs Jobs STEM = Science, Technology, Engineering and Mathematics
    18. 18. Degrees vs Jobs – USA Stats http://code.org/stats
    19. 19. Degrees vs Jobs – USA Stats http://code.org/stats
    20. 20. • “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:
    21. 21. “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:
    22. 22. IT Careers – 4 Streams http://www.new s.com.au/techno logy/sci- tech/robots-to- replace-almost- 50-per-cent-of- the-work- force/story- fn5fsgyc- 1226729696075
    23. 23. 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:
    24. 24.  “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
    25. 25. (based on global energy consumption trends): 1) Comeback of governments 2) Digitization  The Internet of things,  Automation everywhere, and  Intelligent alarming 3) Everything as a service 4) Sustainability 5) 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
    26. 26. 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: 1. Business model disruption 2. Competencies and skill sets of your people 3. Segmentation - end-user solutions - customized and personalized - Simon Fuller and Michael Postula, Schneider-Electric (ACS Seminar: Brisbane 21 August) CT & the Top Megatrends
    27. 27. 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
    28. 28. • “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.” – Monash Uni Courses in Computational Thinking:
    29. 29.  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:
    30. 30.  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:
    31. 31. 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.” – Saket Navlakha & Ziv Bar-Joseph, Lane Center for Computational Biology and Machine Learning Department, School of Computer Science, Carnegie Mellon University. 8 November 2011 Computational Thinking & Biology
    32. 32. Two significant areas: Biosemiotics: • Biosemiotics is the characterization of the symbolic representations within life, which is filled with digitally-coded symbolic messages. Biocybernetics: • Biocybernetics involves self-sustaining systems that integrate different levels of information and its processing, including controls and feedback, within biological systems. CT & Bioinformatics:
    33. 33. • “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
    34. 34. • “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
    35. 35. • “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 information most 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
    36. 36. • 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
    37. 37. • “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
    38. 38. “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 sense logically. “ - see ‘Defending the Apostle Paul: Weighing the Evidence’ Computational Thinking in Theology
    39. 39. • When a time difference of 0.8 millisec makes a significant impact on your financial world, a person with some competence in Computational Thinking is surely better able to appreciate this impact and act on that appreciation. • “It takes you 500,000 microseconds just to click a mouse. But if you’re a Wall Street algorithm and you’re five microseconds behind, you’re a loser.” • “We’re running through the United States with dynamite and rock saws so that an algorithm can close the deal three microseconds faster, all for a communications framework that no human will ever know; that’s a kind of manifest destiny.” • Kevin Slavin TED Talk - http://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world.html Computational Thinking & Business
    40. 40. Many of the concepts, skills, and dispositions are not new. So how is Computational Thinking different 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?
    41. 41.  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:
    42. 42. • 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
    43. 43. • 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
    44. 44. Computational Thinking in the Classroom
    45. 45. Uses LUA
    46. 46. • A new version of SCRATCH called 'Scratch Jr': "... Children can code scenes in which characters utter words in cartoonlike thought bubbles — and that may entice children to try to read them — but programming the computer to advance the scene’s action does not require that children know how to read. Scratch Jr
    47. 47. Computational Thinking in the Classroom Scratch, Stencyl ...
    48. 48. Google’s Blockly – see the code
    49. 49. Agent Sheets http://www.agentsheets.com/
    50. 50. CT in the Classroom Corona/Lua & Unity 3D
    51. 51. Edmodo & LearnStreet Javascript & Python
    52. 52. Code Remix/Transfer Issues
    53. 53. • 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. • Shuchi Grover, Computer Scientist & Educator Not just about Coding – Algorithmic Design
    54. 54. Scratch: Pong vs Giving Change
    55. 55. • Scratch implementation Change algorithm
    56. 56. SDC’s & NS Charts: add nss charts http://structorizer.fisch.lu/
    57. 57. 1. Do I really understand the problem? (a) What exactly does the input consist of? (b) What exactly are the desired results or output? (c) Can I construct an input example small enough to solve by hand? What happens when I try to solve it? (d) How important is it to my application that I always find the optimal answer? Can I settle for something close to the optimal answer? ... 2. Can I find a simple algorithm or heuristic for my problem? (a) Will brute force solve my problem correctly by searching through all subsets or arrangements and picking the best one? i. If so, why am I sure that this algorithm always gives the correct answer? ii. How do I measure the quality of a solution once I construct it? ... Algorithmic Design & asking the right questions:
    58. 58. • Junior High – Scratch  algorithmic design with SDC’s or NS Charts  Stencyl  Python – Robotics; Conceptual Schema & Information Systems; – Flash/HTML 5 animations – Create Augmented Reality apps – Maker world – (Not about learning apps like Word; Excel, etc) • Senior High – Visual Studio (VB or C++) .Net; Lua; Unity 3D, Filemaker/Access scripting – AI; Computational Biology & Cybernetics; Cryptography & Encryption – Big Data analysis – search sort algorithms – machine learning – Create Augmented Reality apps – Gesture Based Apps – Leap Motion – Statistical analysis – net traffic – eg. Google Adwords (Not about learning software tools like Photoshop; & Access – though may include some multimedia tools like Adobe After Effects.) What a curriculum might look like
    59. 59. Ciphertext becomes: ANOCNIEVETTNWOYAESPXRTSEHUPEETMRAZITOZZN Cryptography
    60. 60. Working with weak AI
    61. 61. Students: • Code for Mobile Apps; • Games Design; • Computational Biology • Cryptography & Encryption algorithmic design • Big Data algorithms • Augmented Reality development “This is why using games as an example is so powerful: If you tell students that they’ll learn how to create a video game, they won’t focus on the math, or the skills they have to learn to get there. They’re going to focus on what they need to do to make the games. If the goal is exciting enough, the steps to get there cease to be serious barriers.” – Les Miller, Professor of Computer Science at Iowa State University Create not consume:
    62. 62. ACS recommendations to assist in achieving a steady production of skilled and qualified entrants into the profession:  In order to convey the in-dispensible role of ICT in our daily lives ICT should be recognized as subject in its own right (from Kindergarten through to Year 12)  ICT should be a mandatory subject up to Year 10.  from ACS ACARA Submission ACS Recommendations:
    63. 63. 21st Century Fluency Project:  Problem Solving  Creativity  Analytical Thinking  Collaboration  Communication  Ethics, Action, Accountability - from ‘Literacy is Not enough’ – Lee Crockett, Ian Jukes & Andrew Churches These are long term goals – are our students developing these skills; are they mandated in the curriculum? What skills will students most need to succeed in the 21st century?
    64. 64. • ‘The one thing that I wish I had known about computer science (and programming more generally) earlier is that it is a profoundly creative and interdisciplinary pursuit. • What you choose to apply your problem-solving to is something that demands great ingenuity in how one transforms patterns of the physical world into a digital distillation. • Coding is a process of both synthesis and genesis; not only is it guided by rules and syntax, but also something you create from scratch (like you would with a painting or a novel).’ – Jasmine Tsai Software Engineer, Hackbright Academy Profoundly Creative
    65. 65. Ultimately, the most effective motivators are • autonomy – (the ability to chart your own course), • mastery – (the ability to become an expert at something), and • purpose – (the idea that what you are doing serves a purpose larger than yourself). • Dan Pink – see Ted Talk 2009 • Computational Thinking as a discipline/approach to problem solving can offer all three of these motivators Autonomy, mastery, and purpose
    66. 66. How do we fit Digital Technologies into the Curriculum? • What other subjects need a revolution? • How do we get the teachers with the skills or potential to attain these skills? – Near Peer Coaching – National & State Mentors & Consultants – Computational Thinking & Digital Technologies Conferences – More research on teaching of CT across the Primary & Lower Secondary years • New Coding/Social Media apps – Australian versions of CodeSchool, LearnStreet, Scratch Forum focussed on relevant year levels – Small Business Units Where to from here?
    67. 67. • Today's math curriculum is teaching students to expect -- and excel at -- paint-by-numbers classwork, robbing kids of a skill more important than solving problems: formulating them. • "Rather than topics like solving quadratic equations or factorizing polynomials, Computer-Based Math™ focuses on using the power of math to solve real-world problems like should I insure my mobile, how long will I live, or what makes a beautiful shape, with all their rich and challenging context.“ » see http://computerbasedmath.org/ A Maths Revolution/Reduction would help:
    68. 68. • Traditionalists lost the battle professionally in the mathematical revolution that occurred a century ago but won in education. • Meanwhile, computer science went ahead and got created from the insights of that revolution and turned into the world we now live in. The result? Most K-12 math students and their teachers, us, are unaware of the nature of the mathematical thinking that went on in the 20th century while the technology that surrounds us was built from it! • The ultimate irony - we use 21st century technology, made possible by 20th century math and physics, to teach students how to do 19th century mathematics that they will never use! • - http://climeconnections.blogspot.com.au/2013/08/the-spirit-of-math-20-computational.html A Maths Revolution
    69. 69. • Computational Thinking is now being recognized as vital to our students and our world’s future progress. • Computational Thinking needs to be a core part of the curriculum in our schools • It is time to get serious in supporting the implementation of the ACARA Digital Technologies Curriculum • It is time to help raise up teachers who are willing and able to pick up the baton and become teachers of Computational Thinking • What can YOU do – talk about it; share the vision; share resources; incorporate Computational Thinking into your own learning journey. • Inspire and be inspired! Conclusion:
    70. 70. You should now have some idea of • What is Computational Thinking & why is it important • How we are implementing Computational Thinking in the classroom & some ideas to perhaps follow-up on in this regard • Some sense of the likely future of Computational Thinking as part of the ACARA Digital Technologies curriculum and it’s extension into Year’s 11 & 12 Summary
    71. 71. • Scoop it – my collection of Computational Thinking Resources – http://www.scoop.it/t/computational-thinking-in-digital-technologies • ELH/Computelec Presentation: • http://www.slideshare.net/StrategicITbyPFH/elh-school-tech-2013-computational- thinking • QSITE Computational Thinking Presentation 2012 – my first presentation on this topic – http://prezi.com/pgig8-2dguqs/computational-thinking-in-digital-technologies/ • ACEC Computational Thinking Presentation 2012 – Perth 12 months ago – http://www.slideshare.net/StrategicITbyPFH/computational-thinking-14629222 • ISE Network Blog: – http://isenet.ning.com/profiles/blogs/why-it-should-be-a-foundational-subject-for-all- students-in-the • "Fun" Reading for Students Starting a Computer Science Related Course – http://www.eecs.qmul.ac.uk/~pc/research/education/puzzles/reading/ Further Commentary: