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NotesComputer ScienceTopic 5.1Computational thinking
Back to Computer Science Topics
5.1.24 min read

Computational thinking

IB Computer Science · Unit 5

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Contents

  1. 1Four ways of thinking about a problem
  2. 2The four concepts
  3. 3How they work together
  4. 4Exam-style question
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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1

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.

2

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.

3

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.

4

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.
IB-style questionDescribe[4 marks]

A supermarket wants a system to reorder stock automatically. Describe how each concept of computational thinking would be applied.

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the term decomposition. [1 mark]

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