The big idea: Computational thinking is a set of techniques for making a hard problem solvable.
Break it up, spot what repeats, ignore what does not matter, and write down the steps. None of it requires a computer.
One example throughout: Organising a school sports day: hundreds of students, dozens of events, a fixed number of tracks and one afternoon.
Each concept below is that same problem, handled a different way.
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Decomposition — break it up
Split one large problem into smaller ones that can be solved separately.
Sports day becomes: register students · assign events · build a timetable · allocate tracks · record results. Each is small enough to think about on its own.
Pattern recognition — spot what repeats
Find the parts that are the same, so one solution serves many cases.
Every track event needs the same steps: call the entrants, run heats, record times, rank them. Solve that once and it covers the 100 m, the 200 m and the relay.
Abstraction — ignore what does not matter
Keep only the detail the problem needs and drop the rest.
For the timetable, a student is an ID, a list of events and a year group. Their favourite colour and their shoe size are real facts that make the problem harder for no benefit.
Algorithmic design — write the steps
Set out the steps in order, precisely enough for someone else to follow without asking questions.
"For each event, in start-time order: call the entrants, run it, record the results, then move to the next."
Abstraction in code works the same way:
A function
- You call average(marks) by its name
- You get the result without thinking about the loop inside it
An object
- A Student keeps only what the program needs: name, marks, addMark()
- How they are stored stays hidden
In both, the detail is hidden and only what is needed to use it is shown.
Abstraction is the one people get wrong: It does not mean being vague. It means deciding what to leave out, deliberately and for a reason.
A map leaves out the colour of every house. That is not carelessness; it is what makes the map usable.
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In that order, usually
- Decompose first, so you have pieces small enough to hold in your head
- Recognise patterns across the pieces, so one solution covers several
- Abstract each piece to just what it needs
- Design the algorithm for each piece, then join them up
But not rigidly
- Spotting a pattern often suggests a better decomposition
- Writing the algorithm reveals detail you abstracted away too soon
- Real work loops back through the four several times
- The order is a starting point, not a procedure
No programming required: The guide is explicit: computational thinking does not necessarily involve programming.
It is how a problem is approached. Planning a sports day, designing a database or diagnosing a fault all use the same four techniques.
How this is tested — you must name the concept AND show it being applied to the scenario in front of you. It comes up two ways:
Paper 2 — working with code
- Describe the four concepts, 3-4 marks
- Identify which concept a described action uses
- Apply one concept to a stated problem
Paper 2 — the algorithmic-thinking question
- Apply the concepts to a problem given in words
- Explain how they make it solvable — no code needed
The classic trap: Defining abstraction as "simplifying". It is deciding what to leave out, and why. An answer that names what was dropped and what was kept earns the mark; "making it simpler" does not.
A supermarket wants a system to reorder stock automatically. Describe how each concept of computational thinking would be applied.
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