Computational Thinking

Computational thinking is the thought process involved in expressing problems and their solutions in ways that a computer can execute. AQA identifies four key aspects: abstraction, decomposition, pattern recognition, and algorithm design.

Abstraction

Abstraction means filtering out unnecessary information and focusing on what is important to solving the problem.

• Data abstraction — representing only the essential data (e.g. a map shows roads, not every blade of grass)
• Procedural abstraction — hiding how something works (a function) while exposing what it does
• Problem abstraction — extracting the key problem from a complex real-world scenario

Example

A city map abstracts real geography by showing roads, landmarks, and districts only — ignoring trees, buildings' internal layouts, etc.

A sorting function abstracts the sorting algorithm — the user calls sort(arr) without needing to know whether bubble sort or merge sort runs internally.

Decomposition

Decomposition means breaking a complex problem into smaller, more manageable sub-problems that are easier to solve individually.

Why decompose?

• Smaller problems are easier to understand and solve
• Sub-problems can be solved independently and in parallel
• Solutions can be reused (sub-problems may appear in other contexts)
• Easier to test and debug individual components

Example

// Building a school management system decomposed:
1. Student registration module
2. Timetabling module
3. Attendance tracking module
4. Grade recording module
5. Report generation module

Pattern Recognition

Pattern recognition involves identifying similarities or repeated patterns in problems or data. Recognising patterns allows solutions to be generalised and reused.

• Finding the same sub-problem recurring (e.g. sorting appears in many applications)
• Recognising sequences or structures in data
• Using known algorithms when a new problem matches a familiar pattern

How These Work Together