what i learned
I've gained a wealth of crucial skills and knowledge in computational thinking that I may use to different problem-solving and decision-making domains outside of computer science. The core of computational thinking is the process of breaking complex problems down into smaller, easier-to-manage parts. It highlights cutting out irrelevant details to concentrate attention on the main elements of the issue. The ability to identify recurrent themes and structures in data or problems—which informs the development of effective algorithms—makes pattern identification a valuable skill. My toolkit for solving problems now includes the capacity to write sequential programmes, or algorithmic thinking. Data is also very important because we need to understand the format of the data and how the data is stored, which is very important for data management and analysis. Automation is also very key. We can set the computer to automatically execute commands to complete the process, thus greatly improving efficiency. Computational thinking not only improves my logical thinking ability, analysis and reasoning skills, but also cultivates my creativity. The essence of computational thinking lies in the creation of “logical artifacts” that externalize and externalize human ideas in a form that can be interpreted and “run” on a computer. Therefore, it focuses on computational abstractions and representations, i.e. computational artifacts and the way they are composed are interesting in their own right and not just as models of some scientific phenomenon. The current scientific discourse surrounding the concept of “computational thinking” is multidisciplinary, with contributions from computer science, cognitive science, and education. I've studied advanced subjects with broad applications, such as network connectivity, cybersecurity, mathematical modelling, and ethical issues, in addition to these fundamental concepts. Computational thinking, which places a heavy focus on interdisciplinary adaptability, now forms the basis of my approach to a variety of academic subjects and career goals. It enables me to successfully handle challenging issues, streamline procedures, and reach well-informed conclusions. Applied in everyday life, company management, engineering projects, and scientific research, computational thinking has produced a strong foundation for creative and effective problem-solving. There is great potential to further develop educational environments and scenarios centered on computational thinking from multiple perspectives. From a computer science perspective, "representational flexibility" is desirable as handing over choices related to data structures and other abstractions to us learners.
Computational thinking will have a profound impact on courses to come, and not just in computer science. It gave me a systematic approach to problem solving and made me better at breaking down complex problems into manageable parts and abstracting away core concepts. This way of thinking will strengthen my logical and analytical skills and help me better understand and apply various concepts and theories. I will use pattern recognition skills to identify recurring patterns in problems and improve the efficiency of problem solving. Additionally, I will be better able to apply algorithmic thinking and design detailed steps to solve various problems. This approach is not only useful in math and science courses, but also plays a key role in a variety of other subjects. Computational thinking also taught me how to process data effectively, which is very useful in research and laboratory courses. In summary, computational thinking will be a powerful tool in my academic journey, improving my problem-solving and decision-making skills no matter what subject I study.
reference
Beecher, K. 2017. Computational thinking: A beginner's guide to problem-solving and ... [Online] Available at: https://www.amazon.com/Computational-Thinking-beginners-problem-solving-programming-ebook/dp/B072MGKS96 [Accessed: 25 October 2023].