Computer scientist and educator Dr. Shuchi Grover argue that the so-called '4Cs' of 21st-century learning - communication, critical thinking, collaboration, and creativity - should be complemented by a fifth: computational thinking. Grover argues that this is not only beneficial for STEM subjects (science, technology, engineering, and maths), but also applies to social sciences and linguistics. (Grover, 2018)
The term "computational thinking" was popularised by Jeannette Wing, a computer scientist and professor at Carnegie Mellon University. In her influential 2006 article entitled "Computational Thinking," Wing emphasized that It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use. (Wing, 2006)
Computational thinking is often described as consisting of four key components. These four components are:
Decomposition: breaking down complex problems or tasks into smaller, more manageable sub-problems or components.
Pattern recognition: involves recognizing similarities, trends, or patterns in data or problems.
Abstraction: focuses on essential details while ignoring irrelevant or unnecessary information.
Algorithmic thinking: involves designing step-by-step instructions or algorithms to solve a problem or complete a task.
Computational thinking can be applied to all areas of life and work. For example science and research. Scientists apply computational thinking to design experiments, analyze data and develop mathematical models. Engineers use computational thinking to design and optimize structures, develop signal processing or optimization algorithms, and simulate systems for testing and validation. In business and finance, computational thinking enables professionals to analyze market trends, identify patterns of consumer behavior, optimize business processes, and develop financial modeling and forecasting algorithms. In medicine, it is used to analyze medical data and develop diagnostic systems. In education, computational thinking helps students to solve complex problems by helping them to break them down into smaller parts and develop systematic strategies, among other things.
How computational thinking applies to current learning
At this stage of programming learning, decomposition involves breaking down complex programming concepts or tasks into smaller, more manageable components. Abstraction also focuses on the fundamentals and concepts of programming in programming learning while temporarily ignoring unnecessary details. Pattern recognition involves identifying patterns, structures, or techniques that recur in code. Recognition enables the application of existing solutions to new problems, thus increasing my programming efficiency. Algorithm design breaks down tasks into smaller steps and designs algorithms to solve them. By practicing algorithmic thinking, I learn to analyze problems, design logical solutions, and implement them using a programming language.
The Role of computational thinking in future careers
My ideal career is as a UI designer. I have interned in a UI position before. I think I will be in this profession in the future. I will talk about the importance of computational thinking for my future career based on my work experience. Computational thinking covers all aspects of a career no matter what field or industry I 'm going to work in. For example, computational thinking enhances problem-solving and decision-making in a career. There is also collaboration and communication, where computational thinking promotes effective collaboration and communication skills. It allows me to break down problems into manageable parts, communicate ideas clearly, and collaborate with team members from diverse backgrounds. Next is innovation and adaptability: computational thinking encourages innovation and adaptability. A mindset that applies existing knowledge to new situations. Being able to innovate and adapt to changing environments becomes necessary as the job gets longer. Finally, data analysis and observation are particularly important. Computational thinking skills are invaluable for analyzing data and deriving useful information from it. No matter what field we are in.
