Computational thinking involves a range of problem-solving skills essential for computer science and applicable across many disciplines. Decomposition, abstraction, and pattern recognition are indeed core components, but “All of the above” is essentially the correct answer as computational thinking encompasses these and other related strategies.

Decomposition involves breaking down a complex problem into smaller, more manageable parts. This makes the overall problem easier to understand and solve because you can tackle each smaller component individually.

Abstraction focuses on identifying the essential information needed to solve a problem while ignoring irrelevant details. This simplification allows you to create models or representations that are easier to work with, reducing complexity.

Pattern recognition is the ability to identify similarities and trends within data or problems. Recognizing patterns helps you apply previously learned solutions or develop generalized approaches that can be used in multiple situations.

Beyond these, computational thinking also includes algorithmic thinking, which is designing a step-by-step procedure or set of instructions (an algorithm) to solve a problem. Data analysis is crucial; it involves collecting, organizing, and interpreting data to gain insights and make informed decisions. Logical reasoning is another essential component, using deductive and inductive reasoning to arrive at valid conclusions and test hypotheses.

Other problem-solving strategies related to computational thinking skills include evaluation and generalization. Evaluation involves assessing the effectiveness and efficiency of a solution. Generalization is the ability to adapt a solution to solve similar problems in different contexts. Computational thinking skills provide a framework for problem-solving, algorithm design, and data analysis, applicable to many fields.