Develop Basics of Computational Thinking

Computational Thinking is an essential step that comes right before you learn how to program. It’s the process of breaking down a problem into steps that are simple enough for a computer to comprehend. We all know that computers interpret instructions as we feed it to them. If we don’t give computers precise and detailed instructions, your algorithm may not run properly and perform the tasks that are required.

Take, for example, a simple task like bathing your teeth. You’ll need soap and a bucket of water to begin. After you’ve these things, you will probably need to keep the bucket under a tap and fill it with water. You might also have to turn on the geyser if you need hot water. Now if you forget to do any of these things, you may not be able to successfully bathe. In the same way, the computer needs to be given a step by step instruction of each task in a chronological manner for it to perform it and provide an output.

What is Computational Thinking?

Computational thinking is the systematic approach to a problem and the creation and expression of a solution that a computer can carry out. However, you don’t have to be a computer scientist to think like one! Many quantitative and data-centric problems can be solved using computational thinking. Knowing how to do so will provide you with a foundation for solving real-world, social implications problems.

Basic skills for Computational Thinking

To develop computational thinking, individual needs to develop four major skills:

• Decomposition

Decomposition is the process of breaking down large problems into smaller, easier-to-manage chunks. Decomposition enables a person to evaluate the problem at hand and determine all of the steps required to complete the task. One way to teach older students how to decompose is to build something and only show them the finished product.

When students and adults need to write a complicated and long program, decomposition will help them do it efficiently without missing a single vital step. Students will develop time management skills while learning how to delegate in group projects.

• Pattern Recognition

Pattern recognition is simply looking for patterns in puzzles and determining whether or not any of the problems or solutions we’ve seen before apply here. What have we learned in the past that could assist us in resolving this issue? You know how vital patterns are if you’ve ever made IKEA furniture. When putting together an IKEA drawer unit, the first drawer will almost certainly take longer than the fourth or fifth. We learn how to solve instructions faster and learn from our mistakes when we repeat steps in our build. The time-consuming process of putting together that first part teaches us how to do it more quickly in the future.

• Algorithm design

Algorithm design is laying out the steps and rules that must be followed to achieve the same desired result every time. Giving young learners a task and instructing them to write down the steps is easy to teach them this concept. The steps to making a peanut butter and jelly sandwich are a famous example. Each student should write down all the steps before trading with another student. Make their sandwiches using only the instructions in front of them. This will demonstrate the importance of including small instructions such as “using a knife” or “putting the pieces of bread together” to form the sandwich humorously.

• Pattern Generalisation and Abstraction

Pattern generalisation teaches students how to recognise the crucial details to solve the problem while ignoring the details that aren’t. One of the most challenging aspects of computational learning is identifying the critical information in a crisis and ignoring the irrelevant information. Pattern recognition encourages students to compare and contrast similar objects or experiences to find similarities. Young students can begin to understand trends and thus be able to make predictions by determining what the objects or experiences have in common.

Examples of computational thinking processes:

People’s decision-making, basic arithmetic, and problem-solving are examples of computational thinking. The examples that follow show how computational thinking is applied to real-world problems.

• Using an algorithm to determine the most efficient route between two points based on traffic and other factors such as construction or roadblocks.
• Students use computational thinking skills when deciding whether or not to plan an activity based on weather predictions on an app.
• Baking a cake according to a recipe is an example of an algorithm.
• When determining your spending habits in each category, pattern recognition and decomposition are required when planning a budget.

Conclusion

Developing basic computational thinking is an essential topic for computer science aspirants. Decomposition, Pattern Recognition, Algorithm design, and Abstraction are the four crucial aspects of computational thinking. These notes will be helpful for students preparing for their Class 11 and class 12 board exams.