Computational Thinking

• Definition: Approaches and strategies used to identify solutions to problems.
• Common Methods:
  • Algorithmic Thinking: Creating a sequence of steps to solve a problem.
  • Pattern Recognition: Identifying similarities or patterns within problems to solve them more efficiently.
  • Logical Reasoning: Using logical steps and rules to solve problems and make decisions.

Example:

• Algorithmic Thinking: Creating a recipe (algorithm) for baking a cake, where each step is clearly defined and follows a logical sequence.

• Definition: Simplifying a complex system by focusing on the main ideas and removing unnecessary details.
• Purpose: To manage complexity by reducing information and detail to focus on the relevant aspects of a problem.
• Examples of Abstraction:
  • Abstract Data Types: Defining data structures like lists, stacks and queues without specifying their implementation.
  • Modelling: Creating models to represent real-world systems or problems, like using a map to represent a geographical area.

Example:

• Abstract Data Types: Using a stack to manage a collection of items where only the last item added can be accessed or removed, without worrying about how the stack is implemented.

• Definition: Breaking down a complex problem into smaller, more manageable parts.
• Purpose: To make complex problems easier to solve by tackling each smaller part individually.
• Examples of Decomposition:
  • Task Breakdown: Dividing a large project into smaller tasks or modules.
  • Function Decomposition: Breaking down a programming task into functions, each responsible for a specific part of the task.

Example:

• Task Breakdown: Developing a website by decomposing it into tasks like designing the layout, implementing the frontend and setting up the backend.