What is computational thinking And key skills required for it

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

Computational thinking helps students to develop skills that are attractive for student employment opportunities. Computer science is the fastest-growing job market, and students with skills in coding are highly sought after job applicants. While hard technology skills are essential, it is the softer skills of reasoning and problem solving that employers really find attractive. These skills for success are the key to understanding why computational thinking is so valuable.

While there are obvious benefits for future employment, it is the development of critical thinking and emotional competencies that set up students for long term success. When children learn computational thinking skills, it helps them to develop skills essential for not only STEM subjects, but also across the social sciences and language arts. In fact, a recent study demonstrated that computational thinking skills were highly correlated with a non-verbal measure of intelligence.
Some of the key skills required for Computational Thinking
Pattern Recognition
Pattern Abstraction
Algorithm Design

Decomposition is breaking down complex problems into smaller, more manageable chunks. It allows the students to assess the problem at hand and figure out all of the steps needed to make the task happen.
Decomposition is an essential life skill in the future when students and adults need to take on larger tasks. Students will learn ways to delegate in group projects and build time management skills.

Pattern recognition
Pattern recognition is simply looking for patterns in the puzzles, and determining if any of the problems or solutions that we have encountered in the past could apply here?
There are lots of ways to teach pattern recognition in the classroom. Younger students may benefit from exploring patterns using colored or music blocks. Students may learn about patterns by looking at the periodic table or exploring the patterns seen in the multiplication charts.

Pattern generalization and abstraction
Pattern generalization helps students learn to identify the details that are relevant to solving the problem and ignoring the details that are not irrelevant to the issue at hand. Identifying the crucial information in a problem and disregarding irrelevant information is one of the hardest parts of computational learning.

Younger students may benefit from a building activity where a variety of extra pieces and objects are given that are not part of the design. Students will have to understand which pieces are essential to the design and which are irrelevant.

Algorithm design
Algorithm design is setting out the steps and rules which are needed to follow in order to achieve the same desired outcome every time. An easy way to teach this concept to young learners is to give them a task and tell them to write down the steps.