What is Computational Thinking?

Computational thinking is a set of problem-solving skills and techniques that allow us to approach complex problems in a structured way that a computer can help solve. It is the foundation of all programming and algorithm design.

The AQA 8525 specification focuses on two core aspects of computational thinking: decomposition and abstraction. Both are used before any code is written — they are thinking tools, not programming concepts.

Algorithms

Before covering decomposition and abstraction, it is important to understand what an algorithm is, because both techniques are used to help create one.

An algorithm is a sequence of steps that can be followed to complete a task. Key points:

• An algorithm must have a clear starting point and produce a result
• Each step must be precise and unambiguous
• A computer program is an implementation of an algorithm — it is the algorithm written in a programming language
• An algorithm is not a computer program — it is a plan or design that can be expressed in pseudo-code, flowcharts, or plain English before any code is written

Decomposition

Decomposition means breaking a problem down into a number of smaller sub-problems, so that each sub-problem accomplishes an identifiable task. Each sub-problem might itself be further subdivided until each part is small enough to be solved directly.

Why decompose?

• Large problems are overwhelming — decomposition makes them manageable
• Sub-problems can be worked on independently or in parallel by different people
• Each sub-problem can be tested separately
• Solutions to sub-problems can be reused in other projects

Example: Building a school library system

The main problem — "build a library system" — can be decomposed into:

• Manage book catalogue (search, add, remove)
• Manage member accounts (sign up, log in, view history)
• Handle borrowing and returning books
• Send overdue reminders
• Generate reports for the librarian

Each of these could be decomposed further. For example, "manage member accounts" could break into: create account, verify login credentials, display borrowing history, update personal details.

Abstraction

Abstraction is the process of removing unnecessary detail from a problem, focusing only on what is relevant to the solution.

When solving a problem, there is always more information available than is needed. Abstraction means identifying and keeping only the essential details while ignoring or hiding everything else.

Why abstract?

• Makes the problem simpler to understand and solve
• Prevents getting distracted by irrelevant details
• Produces a more general solution that can apply to similar problems
• Reduces complexity, making programs easier to design and maintain

Example: A sat-nav app

A sat-nav needs to represent roads and routes. The real world has enormous detail. Abstraction removes what is not needed:

Example: A school timetable

A timetabling program needs: teacher names, subjects, rooms, class sizes, available times. It does not need: teachers' shoe sizes, room paint colours, subjects' historical origins, students' home addresses.

How Decomposition and Abstraction Work Together

These two techniques are used together at the start of any project:

1. Decompose the big problem into manageable sub-problems
2. Abstract each sub-problem — identify only the data and processes that are needed
3. Design an algorithm for each sub-problem
4. Implement the algorithms as a computer program