GCSE Computer Science (WJEC)

Topic Summaries

ALGORITHMS AND PROGRAMMING: Computational thinking

Key terms for algorithms

  • Algorithm: a clear, step-by-step set of instructions designed to solve a problem or complete a task. Algorithms must be precise, unambiguous, and finite. Generally, algorithms follow an input-process-output sequence:
Concept Definition Example
Input Data received by the program A user’s age typed into a form
Process Actions performed on the data Calculate the user’s birth year
Output Result displayed or returned Showing the user’s birth year on screen

    • This sequence can also be split into the following categories:
      • Sequence: instructions executed in a specific, fixed order. Every algorithm relies on sequence as a basic control structure.
      • Iteration: repeating a set of instructions until a condition is met. This includes count-controlled loops (e.g. FOR) and condition-controlled loops (e.g. WHILE). 
      • Selection: making decisions within an algorithm based on conditions. This often involves using IF, ELSE, or CASE statements.
  • Decomposition: breaking tasks down into smaller chunks, to make them easier to understand and solve.
  • Abstraction: simplifying tasks by removing unwanted/unnecessary information and creating general solutions.
  • Pattern recognition: the ability to recognise patterns in data and to build algorithms to make use of them, allowing solutions to be reused or applied more efficiently.
  • Logic: using reasoning to determine the correct sequence of steps in an algorithm. This often involves true/false conditions and logical operators.
  • Flow diagrams: a diagrammatic representation of an algorithm using standard symbols that help to visualise sequence, iteration, and selection.
  • Pseudocode: a structured, English-like way of writing algorithms that is not tied to any specific programming language.
  • Validation: checking that input data is reasonable and within expected limits. This prevents incorrect or harmful data from being processed.
  • Efficiency: how well an algorithm uses time and memory. More efficient algorithms solve problems faster or with fewer resources.

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