WJEC 2.1 Computational Thinking

What is computational thinking?

• Thinking like a computer.

• Techniques used to solve complex problems.

• Computer programming

What is a complex problem?

• A problem that is not easy to solve or understand at first.

• A problem that you can solve straight away.

• A problem that is too hard so you should try to solve it.

Which of these are pillars of computational thinking?

• Decomposition: Breaking down data, processes, or problems into smaller, manageable parts

• Pattern Recognition: Observing patterns, trends, and regularities in data

• Abstraction: Identifying the most important details and discarding the unimportant aspects to make the solution manageable

• Algorithm Design: Developing the step by step instructions for solving this and similar problems

• functional programming - treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data

Breaking a complex problem down into smaller problems and solving each one individually.

• Decomposition
• Abstraction
• Programming
• Algorithmic Thinking

Picking out the important bits of information from the problem, ignoring the specific details that don't matter.

• Decomposition
• Abstraction
• Programming
• Algorithmic Thinking

A logical way of getting from the problem to the solution.

• Decomposition

• Abstraction

• Programming

• Algorithmic Thinking

Which of these would not be involved in decomposing a problem?

• Thinking about how the problem could be divided into smaller parts
• Working out who could help you solve a part of the problem
• Adding more parts to the problem so it becomes more complaex

Which of these is an example of decomposition?

• Breaking the problem of organising a cake sale into smaller parts, such as who will bake the cakes and when to hold the cake sale.
• Taking the problem of baking a cake and thinking about how we can make it the best cake possible.
• Looking at what different kinds of cake can be made.

When is a computer most likely to be used when using computational thinking?

• During decomposition
• At the end, when programming a computer
• When writing algorithms

Why are these icons a good use of abstraction?

• They tell us what they mean without needing to use the name

• They are difficult to understand

• They don't explain what they are