Computational Thinking Techniques

Study Notes

Computational thinking is a 4-step problem-solving process
Decomposition is the process of breaking down a large problem into smaller, easier-to-solve parts
Pattern recognition is the ability to find and leverage patterns to efficiently solve problems
Abstraction is the ability to identify and focus on essential information while ignoring irrelevant details
Algorithms are a set of instructions used to solve problems step-by-step
Computational thinking can be applied to real-life scenarios such as navigation, cooking, and even combat strategies.
The process of decomposition helps create manageable smaller tasks, which in turn helps a team work more efficiently
Decomposition can be a useful tool when working on a team as it makes it simpler for each team member to focus on a specific problem
Decomposition helps to simplify the problem and makes it easier to understand
Decomposition makes problems easier to understand and manage because it separates the problem into smaller, more manageable components.

Decomposition breaks down complex problems into smaller, simpler ones.
Abstraction focuses on the essential elements of a problem while omitting unnecessary details.
Pattern Recognition identifies recurring patterns within data.
Algorithm is a step-by-step set of instructions used to solve a problem.
Examples of Computational Thinking:
- Decomposition: Dividing a school project into specific tasks for each team member.
- Abstraction : When planning a restaurant choice with friends, focusing on time and cost constraints.
- Pattern Recognition : Identifying the number of sides in different shapes.
- Algorithm: A recipe for cooking a burger provides a structured set of instructions.
Benefits of Decomposition:
- Easier to solve complex problems by breaking them down.
- Allows for easier task distribution within teams.
- Makes problems easier to understand and manage.
Decomposition can make problems easier to understand and manage.