A look at curriculum in Australia and what Computational Thinking might look like

1. Computational Thinking and
Curriculum
Nick Reynolds
Learning With Interactive Devices
EDUC90588 – 2015
Melbourne Graduate School of
Education

2. The Curriculum
• There will be a Digital Technologies curriculum
in Victorian Schools in 2015
▫ It is mandated
▫ It has Achievement Standards that can be reached
on their own or as embedded in other Learning
Areas
 Difference between ‘Integrated’ and ‘Embedded’?
• This is important in many ways both
nationally and internationally

3. READ THE DOCUMENT!
• Key Concepts (page 23, ACARA)
▫ These are the building blocks of the curriculum
▫ They tell you why
• Achievement Standards (at end of each year level)
▫ They tell you what should be achieved
▫ Can be seen as “working towards” as well as “at the
end of”
• Content Descriptors (each year level and Scope and
Sequence)
▫ They say what is contained
▫ They provide specific guidance
▫ Provide opportunities to build assessment

4. Key Concepts (p.23)
• Abstraction, which underpins all content, particularly the
content descriptions relating to the concepts of data
representation and specification, algorithms and
implementation
• Data collection (properties, sources and collection of data),
data representation (symbolism and separation) and data
interpretation (patterns and contexts)
• Specification (descriptions and techniques), algorithms
(following and describing) and implementation
(translating and programming)
• Digital systems (hardware, software, and networks and the
internet)
• Interactions (people and digital systems, data and
processes) and impacts (sustainability and empowerment)

5. DISCUSSION
Abstraction Data
collection
data representation
data interpretation
implementation
algorithms
Specification
Digital systems
Interactions
impacts
What do they mean?

6. Key Concepts (p.23)
Abstraction, which underpins all content, particularly the
content descriptions relating to the concepts of data
representation and specification, algorithms and
implementation
• Ignoring what is not relevant
• Breaking a problem into small, easily workable
components
For example, when students are asked how to make toast
for breakfast, they do not mention all steps explicitly,
assuming that the listener is an intelligent implementer of
the abstract instructions (ACARA)

7. Key Concepts (p.23)
• Data collection (properties, sources and collection
of data)
What is collected, measured, calculated (the basis of
digital systems)
• Data representation (symbolism and separation)
How it is shown (represented) in digital systems
• Data interpretation (patterns and contexts)
Making meaning from data

8. Key Concepts (p.23)
• Specification (descriptions and techniques)
Describing, defining and clarifying the problem: I
need to go from A to B
I want golden brown, hot toast for breakfast
• Algorithms (following and describing –
reading and writing)
The ‘menu’ or set of instructions to tell you how to
go from A to B: Go forward 4 steps, turn left (to
avoid table) …
Take bread from packet, turn on toaster, put bread in
toaster, push slide button down.

9. Key Concepts (p.23)
• Implementation (translating and
programming)
Actually writing the code ‘automating the
algorithm’, applying the above steps
LIST: bread, toaster, power, knife, butter …
IF brown … ELSE …

10. Problem:
Provide instructions for someone to go from this
room to the Melbourne Museum.
Specification
Abstraction
Algorithm Implementation
Automation

11. Key Concepts (p.23)
• Digital systems (hardware, software, and
networks and the internet)
The whole lot!
Often overlooked but there are significant
interactions going on between systems every time
something is done digitally:
Connecting a camera
Getting hardware to talk to hardware (or software)
Saving to a network drive

12. Key Concepts (p.23)
• Interactions (people and digital systems, data
and processes)
The relationships between computers (hardware
and software) and people
• Impacts (sustainability and empowerment)
What happens (or could happen) when people use
computers.
Safety, security, development, social connection …

13. Computational Thinking
• Papert’s notion of technology as “objects to think
with” (p. 11)
• Wing (2006) defines computational thinking as “a
way that humans, not computers think” (p. 35).
• “mental tools” and “metal tools” (computers)
• “the power of our ‘mental’ tools is amplified
through the power of our ‘metal’ tools” (Wing,
2008, p. 3718)
• the ability to think computationally (a human
quality) is paramount in achieving outcomes not
achievable without those metal tools.
• “a universally applicable attitude and skill set
everyone, not just computer scientists, would be
willing to learn and use” (Wing, 2006, p. 33).

14. Computational Thinking
• Papert uses the term “think like a computer”
▫ the term does not mean to only or always think
like a computer, rather it is “a powerful addition to
a person’s stock of mental tools” (Papert, 1993, p.
155).
• When Papert asks himself to think like a
computer, he does so knowing that “it does not
close of other epistemologies. It simply
opens new ways for approaching thinking” (p.
155).
Papert, S. (1993). Mindstorms: Children, computers and powerful ideas (2nd ed.). New York: Basic
Books
Wing, J. (2006). Computational Thinking. Communications of the ACM, 49(3), 33-35.
Wing, J. (2008). Computational thinking and thinking about computing. Philosophical Transactions of
the Royal Society A, 366, 3717-3725.

15. Computational Thinking
In its most basic, but possibly its most universally
accepted form, computational thinking requires a
mindset or thinking approach that applies an
understanding of the way computers work (think,
act, function, are programmed) in order to solve
complex contemporary problems
Reynolds, N., Swainston, A. & Bendrups, F (2014 in press). Music Technology and Computational
Thinking: Young people displaying competence. In T. Brinda, N. Reynolds, R. Romeike & A.
Shwill (2014). Proceedings of the KEYCIT2014 Conference, Potsdam, Germany. IFIP, University of
Potsdam, Commentarii informaticae didacticae (CID). (pp. 279-284)

16. Three (general)approaches
• Look at current practice (What am I or my school doing?)
▫ A careful investigation of practice and a re-alignment
to allow specific focus on Digi Tech
• Look at new ways of approaching things (What does the
curriculum want me/let me do?)
▫ Starting point is the curriculum accompanied by a
knowledge of or desire to do something new (coding,
programming)
• Rely on specific knowledge and skill in application
(What do I already know and how can I make it fit?)
▫ Specific content knowledge enables looking at Digi
Tech (or what is already in their program) and expand
to suit.

17. Assessment
What students:
Make
Say
DoWrite
Evidence in those things
Multiple opportunities to collect that evidence
The products and processes
In order for … to happen …
must have happened
Can we create tasks whose very
completion require the student
to have gained the required
skills and knowledge? If we can,
why then do we need to ‘test’
that knowledge?

18. THANK YOU