The Concept of Computational Thinking and its Applications

The concept called computational thinking is one house lots of benefits. In this guide, we shall discuss the term computational thinking and why it is important. If you are curious enough, then read through this guide to find out what it entails.

What is Computational Thinking?

Computational thinking is the step that comes before programming. It’s the process of breaking down a problem into simple enough steps that even a computer would understand. We all know that computers take instructions very literally, sometimes to comic results. If we don’t provide computers with instructions that are precise and detailed, your algorithm might forget vital actions that most people take for granted.

For example, consider a simple activity like brushing your teeth. At first, it sounds like a simple enough task, but in fact, brushing your teeth involves many simple steps. First, you’ll need a toothbrush and toothpaste. You’ll need a sink with cold water. You’ll need to put the toothpaste on the brush. Don’t forget to turn on the water and run your brush underneath. As you see, such a simple activity actually involves many steps, if you miss one step or put one out of order you might end up with a huge mess!

Strategies for Incorporating Computational Thinking

Teaching Decomposition

Teaching decomposition to young learners means that students are invited into problem-solving scenarios. Teachers share complex, multi-step problems and facilitate conversations that help students to break them down. While students at these ages are not always developmentally ready for multi-step directions or problems, they are ready to be exposed to models of adult thinking. In doing this, students begin to develop a framework of strategic, computational thinking.

Ideas to Try: Teachers might describe a scenario, such as planning a birthday party, that involves multiple steps. This type of task can quickly become overwhelming without an organized to-do list of smaller, more approachable challenges. Students can help to break down the larger task, and the teacher can help to draw or write a visual representation of their thinking, giving students a mental map of how to solve similar problems in the future.

Teaching Pattern Recognition

Pattern recognition, as a cornerstone of computational thinking, begins with the basic ABAB pattern creation that is taught in the primary grades and extends to more complex layers of thinking. Pattern recognition invites students to analyze similar objects or experiences and identify commonalities. By finding what the objects or experiences have in common, young students can begin to develop an understanding of trends and are therefore able to make predictions.

Ideas to Try: To teach students to recognize patterns, you might begin by investigating trees. What do all trees have in common? They all have a trunk. They all have roots. They all have branches. While there are many differences between types of trees, these components are present in all trees.

Next, work with your students to create a collage of trees. Notice how they all have trunks, roots, and branches. Then, talk about how the trunks differ from one another. Some are thick, while others are thin. Some are brown, while others are white. Talk about how the roots and branches differ.

To extend this thinking, invite your students to draw a picture of a tree, labeling the trunk, roots, and branches. Emphasize that while your class’ trees might look different from one another, they are alike in their core components.

Finding patterns simplifies tasks because you can use what you already know. By teaching students to recognize patterns, their awareness of the world around them expands. This helps them to use the patterns they have identified to solve future problems and make predictions about the world.

Teaching Abstraction

Abstraction is focusing on the information that is relevant and important. It involves separating core information from extraneous details.

Ideas to Try: In primary classrooms, teachers naturally teach kids the concept of abstraction with literature as they identify the main idea and key details. To take this one step further, teachers can encourage students to hunt for information, clues, or treasures by giving them a goal as they approach a book or even an experience.

As students listen to a speaker during a school presentation about dental hygiene, a kindergarten class might be hunting for details about brushing your teeth. By teaching students abstraction, they are able to sort through all of the information available to identify the specific information they need. This is an invaluable skill as students read larger texts and are presented with more and more complex information.

Teaching Algorithms

Algorithmic thinking involves developing solutions to a problem. Specifically, it creates sequential rules to follow in order to solve a problem. In the early grades, kids can learn that the order of how something is done can have an effect.

Ideas to Try: To present this idea to students, the teacher might ask them to think about making a sandwich. What should we do first? Second? What if I put the cheese and lettuce on my sandwich before I add the mayonnaise? Conversations about sequence and order develop the foundations of algorithmic thinking.

To get students thinking in algorithms, invite them to design the path from their classroom to the gym by detailing a series of steps. Then, let them try it out! Additionally, invite students to think about their morning routine. What steps do they take to get ready for school each morning? How would the order impact the outcome? Asking students to consider how inputs change the outcome encourages them to be reflective in their thinking and to make changes to their plans to achieve the desired result.

Why is Computational Thinking Important?

This ability to navigate complex information and think in a way that complements technological processes is essential to student readiness.

  • As a foundation for coding and computer science, computational thinking encourages us to reflect clearly on a problem we’re solving and intentionally program solutions for it.
  • As a foundation for technology integration, computational thinking encourages us to consider how we can leverage technology to aid us in solving these problems – to automate certain tasks.
  • And as a foundation for thought, computational thinking encourages us to be diligent and organized in our work, to plan from the outset how we want to solve a problem but to embrace the fluidity of the process as we come to more and more understanding of the data and information we’re navigating.
  • Computational thinking provides a reliable method to cope with different events, regardless of the industry, whether calculating numbers or growing fresh produce. It’s a multi-dimensional problem-solving concept.

Through this, computational thinking builds essential attitudes (the good kind of student attitude) like:

  • Embracing ambiguity with confidence
  • Persisting through iteration and experimentation
  • Practicing teamwork
  • Leading learning with inquiry
  • Situating oneself as a lifelong learner

Students learn to ask bold questions and persist through complexities toward yet-to-be imagined solutions. In applying computational thinking, students collect and analyze resources, think critically and creatively in collaborative environments, and develop a growth mindset by learning to embrace ambiguity and reframe challenges as opportunities, whether with or without technology.

Disadvantages of computational thinking

Difficulties with Prediction and Implementation

While computational thinking provides so many vast problem-solving opportunities for the people that use it, the predictability involved with computational thinking can sometimes be tricky.

With the computational thinking process, it may be difficult to accurately predict markets, trends, users, and all technical influences. As a result, there are too many variables involved that can complicate any given scenario and make it too difficult to model accurately.

Caching, where data is stored in cache memory, is one way to speed up the computational thinking process and make it easier. However, caching can be hard to integrate and necessitates the collection of the most accurate data for whatever the next instruction is.

Also, there are potential problems with the decomposition model in that an event-driven approach may not be possible compared to a procedural approach for programming purposes.

Knowing How Much Computational Thinking Aids Problem-Solving and Creativity

While applying computational thinking can be helpful in many settings, particularly in educational settings, there isn’t sufficient research that quantifies how much computational thinking helps. As a result, there’s no unequivocal measure of the range of its problem-solving abilities or how much it enhances creativity. Skills don’t automatically transfer, and computational thinking doesn’t definitively make someone better unless something is explicitly being taught to someone or a group of people.

In the end, as more people and companies explore the capabilities and potential limitations of computational thinking, it’s clear that such a concept helps people develop sharper thought processes and connect with computers to solve problems effectively.


To conclude, let’s recap that Computational thinking encompasses the mental activity involved in formulating problems to admit computational solutions, leveraging various principles and concepts, including pattern recognition, decomposition, abstraction, and algorithm design.

Considering that computational thinking is research-based and consistently facilitates innovation, it provides its fair share of benefits. However, there are infrastructural and principle-based issues accompanying computational thinking that should equally be considered.

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