There are many types of thinking available nowadays. This includes strategic thinking, mathematical thinking, critical thinking, creative thinking, to name a few. Since the publication of Prof Jeannette M. Wing’s article in the Communications of the Association for Computing Machinery (ACM) in 2006, there has been a tremendous increase in interest in Computational Thinking. A simple Google search on this term will provide more than 6 million returns. Humans are much better thinkers than computers, so why do we need Computational Thinking?

Computational Thinking is a thought process that involves dealing with complex problems, formulating the problem, and finally, the solution could be passed to an information processing agent that is either machine, human or both. Computational Thinking is a more natural thought process and straightforward for most learners. Although there are many pillars of Computational Thinking, the most accepted pillars are Decomposition, Pattern Recognition, Abstraction and Algorithm.

Firstly, breaking down the more significant portion of an element/problem into manageable pieces is known as decomposition. A simple example is the Work Breakdown Structure (WBS) within the Project Scope Management knowledge area in the Project Management Body of Knowledge (PMBOK). Once the main chunk is broken down, a scan on recognizing similarities or shared characteristics among the smaller parts can be done. This is the second pillar known as Pattern Recognition, which would be an excellent opportunity to introduce repeated treatments or iterations. This treatment may solve identical problems occurring in several areas. This can also be used to spot outliers, and then certain unique expectations can be acted in advance. It may be too much to digest and overwhelming with all the breakdown pieces and their recognized pattern. With this, the third pillar in Computational Thinking is Abstraction. Abstraction would help in minimizing the visible load where information hiding is applied. This is critical as the higher the level of abstraction, the more information hiding it is; thus, it’s less detailed and can be called the helicopter view. On the other hand, if the level of abstraction is low, it has more information and is messy. The degree or the level of abstraction contributes to the different viewpoints of an observer. The higher level of abstraction contributed to a higher level of agreement, and it is more universal. The abstraction technique is applied mainly for entrepreneurs to perform their elevator pitches. This is the fast and better way to deliver their business ideas to the heart and soul of the investor, without confusing them with nitty-gritty points. The final pillar for Computational Thinking is the Algorithm or the step-by-step instruction of operating on the processes. The processes need to be arranged so that they can be executed sequentially or parallelly. These may also introduce iterations or looping to perform operations that require repetition. The most straightforward application of algorithms is the sequence of instructions when preparing our favourite recipe!

Computational Thinking aligns with the demands of the thinking style that the World Economic Forum (WEF) has advocated for since 2022. Computational Thinking is needed not only for programmers or computer scientists but also for everyone in any available domain. Our novel research on Computational Thinking for Small Medium Enterprises food manufacturers proved to be very effective in framing the problem and offering better solutions. Nevertheless, Computational Thinking is never meant to compete with other types of thinking; it is seen as a complementary approach to increase the thinker’s capability and produce a better solution, which is then delivered to an information processing agent for further action.

About the author:

Dr. Ramesh Zaidi Rozan is an Associate Professor in Information Systems. He is passionate about applying Computational Thinking in technology entrepreneurship for process innovation and mindset. He holds a BSc, M.IT and D. Eng in Information Science and Control Engineering (2007) and a Diploma in Digital Entrepreneurship (2020). In 2021, he completed an Industrial Master Program in Data Scientist from IBM & Simplilearn. For more information on his exciting ventures, visit


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