Abstraction; Decomposition; Algorithmic thinking.

Principles of computational thinking: Abstraction; Decomposition; Algorithmic thinking.

2.1.1 Computational Thinking

Computational Thinking — Note 1 of 1

Understanding of these principles and how they are used to define and refine problems

What you need to know

Computational thinking is a set of problem-solving techniques used in computer science. The three key principles are decomposition (breaking problems into smaller parts), abstraction (removing unnecessary detail to focus on what matters), and algorithmic thinking (creating step-by-step solutions). These principles help programmers define, understand, and solve complex problems systematically.

What to know

Pattern recognition identifies similarities or recurring features in problems, allowing existing solutions to be reused.
Abstraction example: 'storing year groups instead of full dates of birth' or 'showing student IDs instead of full details'.
Decomposition example: 'splitting the program into separate subroutines for each event' or 'breaking the database into separate tables'.
Decomposition makes problems easier to debug, maintain, and split across a team of developers.

Key terms

Decomposition: Breaking a problem into smaller, manageable parts that can be solved individually.
Abstraction: Removing unnecessary detail to focus on relevant information.
Algorithmic thinking: Identifying the main steps needed to solve the problem and the SEQUENCE that the steps are completed in.

Exam technique

Tip:You must apply these concepts IN CONTEXT. Generic definitions alone will not score marks — explain HOW abstraction or decomposition is used in the given scenario.

Ready to test yourself?

Practise 2.1.1 questions

Next topic →The inputs, processes, and outputs for a problem2.1.2 Designing, creating and refining algorithms

Back to Computational Thinking

Revise another OCR GCSE topic: Designing, creating and refining algorithms