Data supplement
The core technology of solving problems by computational thinking is the abstraction and automation of solving problems;
A series of thinking activities covering the breadth of computer science, such as problem solving, system design and understanding of human behavior, were first put forward by Zhou in March 2006. 20 10 Professor Zhou pointed out that computational thinking is a kind of thinking process related to formal problems and their solutions, and its problem-solving representation should be effectively implemented by information processing subjects.
Computational thinking is based on the ability and limitation of computing process, and is executed by people and machines. Computational methods and models make us dare to deal with problem solving and system design that individuals cannot accomplish independently.
Abstraction in computational thinking completely transcends the physical concepts of time and space and is completely represented by symbols, among which digital abstraction is only a special case. Compared with mathematics and physical science, abstraction in computational thinking is richer and more complex. The biggest feature of mathematical abstraction is that it abandons the physical, chemical and biological characteristics of real things, but only retains its quantitative relationship and spatial form, and the abstraction in computational thinking does not stop there.
Computational thinking was put forward by Professor JeannetteM M. Wing in 2006. Computational thinking is a problem-solving thinking process, which can be divided into four steps. In the process of Google's computational thinking, these four steps are:
(1) Decomposition: Break down data, processes or problems into smaller and more manageable parts.
(2) Pattern recognition: observing the patterns, trends and laws of data.
(3) Abstraction: the general principle behind the formation of cognitive model.
(4) Algorithm design: Write out a series of detailed steps to solve a problem.