2.1.1 Computational Thinking Quiz

What is abstraction in computational thinking?

• Simplifying complex systems by focusing on essential details

• Complicating simple systems by focusing on non-essential details

• Ignoring all details of a system

• Adding unnecessary complexity to a system

How is abstraction used in problem-solving?

• By adding unnecessary details to the problem.

• By simplifying complex problems into more manageable components or ideas.

• By ignoring the details and focusing only on the big picture.

• By making problems more complex and difficult to understand.

What is decomposition in computational thinking?

• Solving a problem without breaking it down

• Combining multiple problems into one

• Ignoring the complexity of a problem

• Breaking down a complex problem or system into smaller, more manageable parts

How does decomposition help in solving complex problems?

• By solving the entire problem at once

• By making the problem more complicated

• By breaking down complex problems into smaller, more manageable parts.

• By ignoring the problem altogether

What is pattern recognition in computational thinking?

• The ability to identify and analyze patterns within data or problems.

• The process of memorizing patterns

• The ability to create random patterns

• The inability to recognize patterns

How is pattern recognition applied in real-world scenarios?

• Animal tracking, plant identification, and traffic management

• Music recognition, handwriting analysis, and barcode scanning

• Weather forecasting, stock market analysis, and language translation

• Facial recognition, speech recognition, and recommendation systems

What is algorithm design in computational thinking?

• Using trial and error to solve a problem

• Drawing a picture of the problem

• Creating step-by-step instructions to solve a problem or perform a task using a computer

• Guessing the solution without any plan

How are algorithms designed to solve specific problems?

• By guessing and checking different solutions

• By using random trial and error

• By breaking down the problem into smaller subproblems and creating a step-by-step solution for each subproblem.

• By ignoring the problem and hoping it goes away

What is generalization in computational thinking?

• Using trial and error to solve a problem

• Creating a general solution that can be applied to different problems

• Creating a specific solution for a single problem

• Focusing on the details of a particular problem

How can generalization be used to solve different types of problems?

• By ignoring patterns and principles

• By focusing on the specific details of each problem

• By identifying common patterns or principles that can be applied to different types of problems.

• By only solving one type of problem and not others