What is Abstraction in context of Computational Thinking? How is it used in solving problems?
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Answer:
In computational thinking , when we decompose problems, we then look for patterns among and within the smaller problems that make up the complex problem. Abstraction is the process of filtering out – ignoring - the characteristics of patterns that we don't need in order to concentrate on those that we do.
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Abstraction in the context of computational thinking is the process of identifying essential details of a problem and ignoring or suppressing unnecessary details. It involves focusing on the most important aspects of a problem while ignoring irrelevant details.
Abstraction is used in solving problems by breaking them down into smaller, more manageable sub-problems or components. This allows us to simplify complex problems and focus on specific aspects that are relevant to the solution. For example, when designing a program, abstraction can be used to break the problem down into smaller parts, such as defining functions or classes, and then building upon these parts to create a complete solution.
Abstraction is also useful in modeling real-world systems or situations, such as in scientific or engineering applications. By abstracting away irrelevant details, we can create simplified models that help us understand and predict how these systems will behave.
In addition, abstraction allows us to create reusable solutions that can be applied to similar problems. For example, a programming function that solves a particular type of problem can be reused in different programs or applications that require similar functionality.
Overall, abstraction is a key component of computational thinking and is essential for solving complex problems and designing efficient and effective solutions.