Computational thinking involves a set of problem-solving skills and techniques used to understand and solve complex problems like a computer scientist. While decomposition, abstraction, and pattern recognition are core components, “all of the above” is generally a more accurate answer because computational thinking encompasses additional elements.

Decomposition is breaking down a large, complex problem into smaller, more manageable parts. This makes the problem easier to understand and solve piece by piece.

Abstraction involves focusing on the essential information and ignoring irrelevant details. This simplifies the problem by removing unnecessary complexity, allowing you to create a general model applicable to many instances.

Pattern recognition is identifying similarities and trends within problems or data. Recognizing patterns can help you use solutions that have worked before or predict future outcomes.

Algorithmic thinking is another key part of computational thinking. It involves developing a step-by-step solution or a set of instructions (an algorithm) to solve the problem. This requires careful planning and sequencing of actions.

Data analysis is a component used for examining data to draw conclusions about that information. Data analysis involves looking for patterns and abstractions in data, and is often done using algorithms.

Logical reasoning also underpins computational thinking. It’s the process of using logic and reasoning to develop solutions and evaluate their correctness. It ensures solutions are sound and effective.

Therefore, while decomposition, abstraction, and pattern recognition are fundamental, algorithmic thinking, data analysis, and logical reasoning are integral parts of computational thinking as well. Together, these elements form a powerful toolkit for problem-solving in various disciplines, not just computer science.