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Computational thinking, because of its name, is often misunderstood as being interrelated to technology (computers, in particular), making it often overlooked by those educators, parents, or students who are not technologically inclined. However, this skill is crucial to the problem-solving process in both digital and non-digital applications.

Computational thinking is more related to math and algorithms than it is to digital technology. It refers to “computing” a solution by breaking down a problem into its separate parts and discovering the effective steps that reliably and effectively resolve the problem. In math, this might look like the “order of operations,” which defines how to decompose and solve a linear math problem.

What is the Purpose of Computational Thinking?

The purpose of computational thinking is to be able to solve complex problems in a structured, effective and repeatable way. Computational thinking, while drawing on principles from computer science and mathematics, can be applied not only to mathematical or technology-related problems, but to real-world problems as well. Therefore, computational thinking provides an effective and repeatable process for solving complex issues regardless of whether they are technologically dependent.

How Does Computational Thinking Work? 

In a previous article defining computational thinking, we discuss how computational thinking identifies a clear, defined step-by-step solution to a complex problem. But how, exactly, does one utilize computational thinking to define this solution?

Whether the problem to be solved is in a technological environment or is “offline,” (that is, not related to technology), computational thinking helps to approach, understand, analyze and resolve the problem in an effective and efficient manner. The process is as follows:

  1. Decomposition. First, the problem is decomposed into smaller, more manageable parts. This helps the problem-solver more effectively understand the problem while being able to eliminate those parts that are irrelevant.
  2. Pattern Recognition. In the next step, pattern recognition, the problem solver identifies patterns or connections between the different parts identified during decomposition—or even to other previously-solved problems. The purpose of this step in computational thinking is to further simplify the problem as well as to begin identifying areas of the problem that may be solved similarly.
  3. Abstraction. Decomposition and pattern recognition empower the problem solver to use abstraction to identify the most relevant information within the problem while eliminating that which is either repeated elsewhere or irrelevant. This simplifies an otherwise complex problem and creates a more efficient environment for the individual to identify how the different parts of the problem may be solved.
  4. Algorithmic Thinking. Algorithmic thinking is the process of defining a step-by-step solution to the problem. The key to an algorithmic solution is that it should be able to be replicated for a predictable and reliable outcome (in other words, for those familiar with billiards, “slop shots” don’t count). The benefit of having a replicable solution is that it is more certainly a reliable outcome if the result can be repeated. In addition, a well-defined replicable solution may be more effectively used in part or in whole to resolve other issues.

Why is Computational Thinking Important?

Computational thinking is an important future-ready skill for students and adults alike. This sophisticated process for problem-solving empowers the learner with more effective tools to solve complex problems as well as to produce more effective processes in the future.

  • Problem solving. The most well-known benefit of computational thinking is the increased ability to solve complex problems. Just like how computational thinking provides effective steps to solve a complex problem, the process of computational thinking, itself, is a computational solution for solving complex problems.
  • Automation and efficiency. Computational thinking is essential in the automation of tasks and processes, which means it’s critical for such applications as coding and automation. The applications of these are far-reaching, from science and engineering to marketing, sales, social sciences, big data and more.
  • Data Analysis. In the age of big data, computational thinking is essential for processing and interpreting vast amounts of information. It helps in extracting meaningful insights and making data-driven decisions.
  • Innovation. Computational thinking is a driver of innovation. At its core, computational thinking helps to solve complex problems, which is the same basis that inspires innovative solutions to these problems. Without the ability to problem-solve using computational thinking, it would be difficult to define and replicate innovative solutions to modern problems.
  • Career opportunities. The ability for an individual to use computational thinking to problem-solve empowers the individual with a “soft skill” that is highly valued in most industries and leadership positions. From manufacturing to finance, technology to healthcare and beyond, these industries actively seek individuals who can solve complex problems and drive innovation.

Final Thoughts

Computational thinking is a critical skill that, when used effectively, can not only solve complex problems, but also drive innovation in a wide range of applications. For students, computational thinking is a critical future-ready skill that can empower them in their education, future careers and beyond. Staff Writers Team

Staff Writers

Founded in 1999, provides educators with solutions to prepare their students with critical digital skills. Our web-based curriculum for grades K-12 engages students as they learn keyboarding, online safety, applied productivity tools, computational thinking, coding and more.

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