Thinking Abstractly

Understanding abstraction is crucial for problem solving in computational thinking. It helps to simplify complex problems by highlighting vital aspects that require attention, while ignoring irrelevant details.
Abstraction can be explained as the process of capturing only essential characteristics of a problem or system, to be implemented in a solution. Often this is a multi-layered process, where numerous levels of abstraction may be used.
A hierarchy of abstraction layers is commonly used to manage complexity. Each layer isolates specific functions, data or processes, thus improving readability, reliability, and maintainability of the software.
Abstraction in programming often involves creating user-defined data types, functions or methods. It can be realised through encapsulation, where a complex system is contained within a single entity, and presented with an interface that hides its complexity.
Object-oriented programming (OOP) is built around the principles of abstraction. Here, objects are the abstractions of real-world or system entities, and their properties and behaviours are encapsulated in classes.
In algorithms, abstraction can be applied to design an efficient solution. It is an iterative process where each algorithmic step is simplified until a solution is found.
Functional abstraction refers to the process of encapsulating a segment of code in a function, allowing it to be reused across different parts of software. This increases efficiency by reducing redundancy and complexity.
Knowing when and how to abstract, is an important decision making skill in computational thinking. It is a balance between creating a model that is simple enough to understand, but not too simple that it omits important aspects of the problem.
Challenges associated with abstraction include accurately identifying what details to hide or include, the risk of oversimplification, and managing a potentially increased level of complexity if abstractions are layered too deeply.