Computational Thinking

Tim Berners-Lee Reflection

Computational thinking is made up of four main processes:


I. Introduction to computational thinking

Computational thinking is a type of analytical thinking that is shared with mathematical thinking, engineering thinking, and scientific thinking (Wing 2008, p. 3717). There is a common misconception that people think of computational thinking as programming or a mode of thinking that can only be used in the computer science field. Nevertheless, computational thinking is a basic and important skill that every individual should develop since it is directly related to our everyday lives. Wing (2006, p. 35) argued that everyone can benefit from applying computational thinking in everyday life and it is a computational concept that people use to perform everyday tasks, communicate and engage with others.

When people face problems, computational thinking can help us to clearly understand the problem itself and logically find possible solutions to the problem. It's just like when programmers write programs. In our lives, computational thinking is used for everyday things such as cooking, baking pastries, planning travel routes, completing assignments, or hosting a social gathering, etc.

II. Application areas of computational thinking

Computational thinking is suitable for everyone and has a very wide range of applications. First, computational scientists can use computational thinking to better analyze and process data and to design algorithms or develop new software and programs. Second, in the field of education, many children start learning programming at an early age and are exposed to computational thinking as a way to develop their logical thinking skills, which can help them later in their future academic life. Third, computational thinking can also help hospitals analyze patient data. This allows doctors to develop a plan that is better suited to their patients and facilitates their recovery. It has been argued by Wing (2008, p. 3717) that computational thinking can also have an impact on statistics. Machine learning makes it possible to identify patterns and anomalies in large data sets, such as astronomical maps, credit card purchases, and store receipts. She also argued that computational thinking is changing economics and it is used in advertising placement, online auctions, etc.

III. Importance of computational thinking

Computational thinking plays an important role in both learning and career development by helping us to thoroughly analyze problems and transform complex problems into simple ones. I believe that learning and using computational thinking will enable me to overcome the challenges I face in my studies and my future career. Learning computational thinking can assist me in being more logical and in having clearer goals when writing websites. For example, I can break down a web page into smaller parts and consider the structure, content, style, and layout of each component. When I get stuck, I can evaluate the prior examples the teacher gave and look for similarities between them to find a workable solution. I can also build the steps needed to complete each small part. This will enable me to have a clearer idea and not make a lot of mistakes when writing the code and then face constant revisions. At the same time, I believe that by using computational thinking, I will not feel overwhelmed when I encounter challenges in my future academic or career. I think that I can try to solve problems by following the four steps of computational thinking in a structured manner. Therefore, using computational thinking will not only help me to do programming more logically but can also be helpful in my daily studies as well as in my future personal development and can make me more confident to deal with complex problems.


References:

Wing, J.M. 2006. Computational thinking. Communications of the ACM 49(3), pp. 33-35. doi: 10.1145/1118178.1118215
Wing, J.M. 2008 Computational thinking and thinking about computing. Phil. Trans. R. Soc. A 366(1881), pp: 3717–3725. doi : 10.1098/rsta.2008.0118