Similar to Mathematical Thinking, Computational Thinking encompasses Problem solving, Modeling, Data analysis & interpretation and Statistics & probability.[1] There are four processes in Computational Thinking, which are decomposing, abstraction, pattern recognition and algorithms. As the matter of fact, the essence of computational thinking is "Abstraction" and "Automation".
Computational thinking can be applied not only to Programming, Data mining or Algorithmic, but actually for different industries, the way of thinking about computational thinking can change. As an example, for computer science disciplines, computational thinking is more focused on design and construction. For mathematics, computational thinking tends to be more logical, more reasoning and deductive. And for the physics discipline, experimentation and verification are more in line with its computational mindset.
Decomposing can be understood as breaking down complex problems into more manageable ones. Recognizing the available, more applicable tools to solve the problem. In real life, evaluate potential strategies and solutions and select the optimal solution given the time, team and tools. Using Algorithms, it would be possible to specify distribution strategies to solve complex problems, or to use incremental design: the design is tested periodically by splitting the problem.[2]
The development and use of computational thinking is also becoming more of a concern in everyday life. It is more about generating SOLUTIONS than PROBLEMS, whether they are complex, unsolvable problems or unsolvable problems in the scientific field. Computational thinking helps people to solve problems and overcome them in a more clever way.
References1: “Similarities and Differences between CT and MT.” Review of Educational Research, American Educational Research Association, Washington, 1972.
References2: Yuk, YI. What Is Computational Thinking, 2021.