Computational thinking was first introduced by Jeannette M. Wing in March 2006 in Wine (2006). According to Sören Werneburg and H. Ulrich Hoppe (2019), the essence of computational thinking (CT) is to create 'logical artefacts' that materialize and formalize human ideas in a form that can be interpreted and 'run' on a computer. human ideas in a form that can be interpreted and 'run' on a computer. In other words, computational thinking is a way of thinking about problem-solving rather than specific subject knowledge, and this way of thinking can be understood as a conceptual way of thinking about computational thinking, using the basic ideas of computer science (CT), which allows people to master how to analyses current information and deal with recent problems. Decomposition, pattern recognition, abstraction and algorithms and modelling.
Decomposition means solving a complex problem and breaking it down into more that can be easily solved, for example, you must make a list of things to be cleaned to complete your task when you want to clean your house. 2003 (Sergios Theodoridis) Pattern recognition is a scientific discipline whose aim is to classify objects into many classes or categories. Pattern recognition is also an integral part of most machine intelligence systems used for decision making in most machine intelligence systems used for decision-making, pattern recognition is also an integral part of this feature pattern to derive logical answers e.g., fingerprint recognition, and speech recognition. Abstraction is the process of eliminating the differences found by the student in the previous step (pattern recognition) because they do not fit the pattern and only focus on the key details, which means that one should learn from the information to filter it wisely. An algorithm is conceptually a list of procedural steps to complete a task, and the execution of the algorithm is automated by the machine, reflecting the automated nature of computational thinking. Modelling is the distillation and re-encapsulation of a current class of problems and specific algorithms, so that their inputs and outputs, a set of reliable and stable, can be used to solve a large class of problems.
Why it is important in your study plan and your current/future career.
Computational thinking is not just for computing disciplines, it can be a mode of thinking to solve problems in all fields and it can make people more organized and efficient in solving problems. that affects all disciplines and fields. Getting used to and using computational thinking will undoubtedly help me in my future studies, and for my studies, it will help me to exercise and develop my problem-solving skills for each new problem that arises in my next computing subjects. For my future career, I intend to work in the Internet industry in the future and computational thinking can be of immense help to me. For example, decomposition, pattern recognition, abstraction and algorithmic modelling and simulation of computational thinking will ill improve my efficiency and learning and reduce my probability of making mistakes when working on future projects or emerging difficulties. Computational thinking is therefore an important mode of thinking that can be applied to a wide range of fields and disciplines.