Computational thinking: what is it and what are the advantages?

Have you ever heard of computational thinking and have the slightest idea of what it really means.

This is a computational skill that can make a big difference in your life, and today we are going to learn more about it.

What is computational thinking?

Computational thinking is a soft skill fundamental cognitive skill that involves the ability to formulate problems in a way that can be solved by a computer.

It covers a range of mental skills and strategies that are useful in many contexts, not just computer programming, such as art projects and math problems.

The idea of computational thinking is that when faced with a problem-solving challenge, you will reflect on it and separate it into different parts so that you can deal with it more efficiently, in the same way that a computer processor works.

This approach helps people develop analytical skills and solve complex problems, regardless of their field of expertise.

Computational thinking is commonly taught in educational contexts to help students develop fundamental problem-solving, logic, and abstraction skills that are valuable in many areas of life and work.

How is computational thinking divided?

Computational thinking is based on compartmentalizing a problem or challenge you have encountered into different tasks.

Let's see how this division is made, which includes the main characteristics of this type of thinking:

Decomposition

Decomposition is the first step, you should start by dividing a problem into smaller, more manageable parts.

An example that everyone could relate to and understand, a person needs to start packing their things to move and they still have all the rooms left.

So decomposition implies that he should start by dividing the tasks into rooms, and then divide them into categories of objects or items.

For example, if the room is divided, we can understand that within it the person can start with clothes, hygiene items, shoes, etc.

Abstraction

Abstraction is the ability to understand more about the important details of a problem, ignoring more irrelevant aspects, in order to simplify the complexity of your problem or challenge.

Points that can be left aside are identified so that you can focus on what really matters.

When abstracting, people often create models or abstract representations that capture the essential aspects of a problem. These simplified models help in understanding and solving problems.

Abstraction often leads to generalization, where specific concepts are grouped into broader categories. This generalization allows solutions to be applied to a variety of similar situations.

Standardization

It involves identifying patterns or trends in data, problems or situations. This allows the creation of abstractions and generalizations that can be applied to different contexts.

This skill is one of the four main components of computational thinking, along with decomposition, abstraction, and algorithms.

This skill is crucial to simplifying complexity and finding more efficient solutions.

Once patterns are identified, the next step is usually generalization and abstraction.

This involves creating models or abstract concepts that represent the identified patterns, allowing for easier understanding and application of these patterns in different contexts.

This includes creating consistent methods and approaches that can be followed to ensure the quality and effectiveness of solutions.

Algorithms

Finally, the algorithms stage refers to the ability to develop a clear and precise set of steps or instructions to solve a problem. Algorithms are logical sequences of actions that lead to a solution.

They do not need to be followed in a fixed, correct order; they are interconnected activities that are often used simultaneously to address complex problems.

When someone is solving a problem using computational thinking, they might start by breaking down the problem, identifying patterns, abstracting away non-essential information, and finally developing an algorithm for the solution.

How important is this type of thinking?

We often find ourselves overwhelmed with a very large task, whether in our professional or educational lives. An example of this is the TCC, which is a huge task that many people don't even know where to start.

So when you are doing your TCC you may prefer to divide the tasks using computational thinking to make the process more tangible.

Therefore, this can be an essential skill in a world that uses technology all the time and needs more efficiency every day.

Its importance extends beyond the computer programming and influences several areas of personal and professional life.

And what are the benefits?

Of course, being so good, computational thinking must bring benefits, right?

This skill allows people to approach complex problems more efficiently by breaking them down into smaller parts, identifying patterns, abstracting relevant information, and developing algorithms for solutions.

When we apply computational thinking, we learn to make more informed decisions through logical analysis, contributing to more assertive decisions.

The development of efficient algorithms enables the automation of tasks, improving efficiency and allowing people to focus on more complex and strategic activities.

These skills enable people to adapt quickly to new technologies and digital tools, promoting agility and the ability to learn continuously.

In many industries, computational thinking skills are highly valued. The ability to solve problems, think logically, and understand algorithms is essential in many workplaces.

Computational thinking fosters creativity and innovation. By approaching problems systematically, people can discover innovative solutions and create new products, services, or approaches.

It also contributes to the development of cognitive skills such as logical reasoning, critical analysis, problem-solving and abstraction. These skills have broad applications in various areas.

When working on projects involving computational thinking, people often need to collaborate and communicate effectively to develop and implement solutions. This strengthens interpersonal skills.

It is worth remembering that when we talk about computational thinking, it does not only apply to programming, and can be used for any very large task in professional, personal or educational life.