What is Computational Thinking?
Computational thinking may be termed as a “problem-solving process” which involves training the brain to analyze and find a plausible solution to a given problem. In simple terms, it is the ability to break down a problem in a structured form and use different faculties like arts, creativity, problem-solving to design the solution. This involves understanding the way a computer solves a problem, taking simple steps one after the other which are ideally repetitive in nature.
Problem vs Instruction
Each problem is a unique challenge that must be solved. It could be a situation, a person, or a thing that could create a problem. For example, a non-working appliance is a problem. An instruction is the direction for operation while doing a task. For example, the flow of problem-solving techniques while fixing a non-working appliance, usually listed on the user manual to fix the common problems.
How is coding connected with Computational Thinking?
Coding implies a way of communicating with our computer and telling it what to do and how to do it. Computational thinking involves breaking down a problem into smaller parts and finding a repeatable path that the computer can understand and execute to get the desired solution.
Basically, it involves making algorithms to perform the given tasks. Once the algorithm is designed, the actual task can be delegated to a computer. Coding provides a fun way of learning computing to the students and experimenting with their own ideas.
Laying the building blocks in these early years can help them develop the skill of systematic approach towards solving a problem, thereby developing computational thinking skills. Computational thinking and programming are thus interconnected.
Cornerstones of Computational Thinking
For a better understanding of computational thinking, it is important to understand the four cornerstones of computational thinking:
Decomposition - involves breaking down the tasks into small bits, or steps. This is where a complex problem is broken into a simpler problem
Abstraction - unwanted details are removed from the steps while focusing on only what is important.
Algorithm - This involves formulating a roadmap or a kind of workflow, i.e. a step by step approach to solve a problem. Here, conditional logic is an important aspect. This means that a particular step or series of steps can be performed only when certain conditions are met. For example, a dustbin has to be emptied only when it is full. Otherwise, it would be useless to empty a partially filled bin.
Pattern recognition - In a way, it helps students to make connections between similar problems and experiences.
A student can master the art of computational thinking over time. For this, learning coding is a very helpful, rather essential tool. It discourages rote learning and makes a child inquisitive and mentally active as well.
What is a good algorithm?
Human algorithms can rely on intuitions, initiations, knowledge, decision-making, and experience. Computer algorithms, however, rely on the quality of the algorithm written. A good algorithm is the only way to achieve computational thinking for coding.
A good algorithm is always accurate and has one interpretation. It has the apt details that lead to completion of the flow. It doesn’t carry the burden of redundant details. The algorithm must be practical in its purpose, covering all possible and plausible scenarios. The instructions must be well-ordered.
Real-Life Examples of Computational Thinking
Our everyday life is filled with several tasks we accomplish using computational thinking. If we only look around, there are a set of problems waiting to be solved.
For example, imagine a situation of buying a new car within the budget. First, we’ll break down the budget, and list down the number of options. We now pick the fuel-type and the color of the car based on facts and consensus.
Thus, we remove all the options that aren’t relevant anymore. We go on to the next step of checking reviews from experts, and the features that are must-haves. Next, we take a test drive to find out the best option available.
Even tasks such as going out for a movie and dinner involve computational thinking. We have to pick the restaurant and the movie show, and time it so that neither is missed.
Activities and Lessons to Develop Computational Thinking
Pre-coding lessons and activities are available to help children ease into the world of coding. They involve the development of computational thinking. Some of the beginner languages children are taught are primarily pre-coding languages.
We are used to giving children answers to remember, but what about the problems they will eventually face academically, as well as in life? Instead, it is advisable to teach them the problem-solving techniques. Computational thinking is this criteria that can help them fix their own problems in the future, while also learning to code.
Introducing coding to children at an early age helps them in getting an opportunity to inquire, investigate, apply, create and at the same time lay the foundation stone of computational thinking in them. This will empower our children to create the future.
Why is computational thinking important?
Computational thinking is an essential component of real-world problem solving. It enables children to crack the complex problems by breaking them down into its parts and solving them with ease. It also is the stepping stone to computational coding.
What is computational thinking in computer science?
In computer science, the process before programming is defined as computational thinking. Computational thinking in programming is the step where the problem is broken down into simpler steps, making problem-solving easier and programming more methodical.
What are the 4 stages of computational thinking?
The 4 stages of computational thinking programming, as per BBC, are decomposition, pattern recognition, abstraction, and algorithms. At the ‘decomposition’ step, we break problems into simpler parts. At the ‘pattern recognition’ step, we look for the identifiable similarities between or with the problem. At the ‘abstraction’ step, we disregard information that isn’t useful. At the algorithm level, we design workflow to solve the problem.