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.

1. Implementing the
Digital Technologies
Curriculum in the High
School
Justification, Issues & Ideas
Paul Herring
St Peters Lutheran College

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. • “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. • “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. • “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. • "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. • "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. “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. “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. • "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. • 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. • ‘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. 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. 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. ‘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. Career Growth
STEM = Science, Technology, Engineering and Mathematics

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

18. Degrees vs Jobs – USA Stats
http://code.org/stats

19. Degrees vs Jobs – USA Stats
http://code.org/stats

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. “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. 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. 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.  “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. (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. 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. 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. • “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.  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.  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. 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. 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. • “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. • “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. • “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. • 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. • “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. “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. • 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. 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.  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. • 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. • 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. Computational Thinking in the Classroom

45. Uses LUA

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

48. Google’s Blockly – see the code

50. Agent Sheets
http://www.agentsheets.com/

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

52. Edmodo & LearnStreet
Javascript
& Python

53. Code Remix/Transfer Issues

54. • 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

55. Scratch: Pong vs Giving Change

56. • Scratch implementation
Change algorithm

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

58. 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:

59. • 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

60. Ciphertext becomes:
ANOCNIEVETTNWOYAESPXRTSEHUPEETMRAZITOZZN
Cryptography

61. Working with weak AI

62. 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:

63. 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:

64. 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?

65. • ‘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

66. 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

67. 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?

68. • 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:

69. • 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

70. • 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:

71. 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

72. • 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: