Introduction to computational thinking

Introduction to computational thinking

Before computers can be used to solve a problem, the problem itself and the ways in which it could be resolved must be understood. Computational thinking techniques help with these tasks.


What is computational thinking?

Computers can be used to help us solve problems. However, before a problem can be tackled, the problem itself and the ways in which it could be solved need to be understood.

Computational thinking allows us to do this.

Computational thinking allows us to take a complex problem, understand what the problem is and develop possible solutions. We can then present these solutions in a way that a computer, a human, or both, can understand.

The four cornerstones of computational thinking

There are four key techniques (cornerstones) to computational thinking:

  • decomposition - breaking down a complex problem or system into smaller, more manageable parts
  • pattern recognition – looking for similarities among and within problems
  • abstraction – focusing on the important information only, ignoring irrelevant detail
  • algorithms - developing a step-by-step solution to the problem, or the rules to follow to solve the problem

Each cornerstone is as important as the others. They are like legs on a table - if one leg is missing, the table will probably collapse. Correctly applying all four techniques will help when programming a computer.

The four cornerstones of computational thinking are decomposition, pattern, abstraction and algorithms.

Computational thinking in practice

A complex problem is one that, at first glance, we don't know how to solve easily.

Computational thinking involves taking that complex problem and breaking it down into a series of small, more manageable problems (decomposition). Each of these smaller problems can then be looked at individually, considering how similar problems have been solved previously (pattern recognition) and focusing only on the important details, while ignoring irrelevant information (abstraction). Next, simple steps or rules to solve each of the smaller problems can be designed (algorithms).

Finally, these simple steps or rules are used to program a computer to help solve the complex problem in the best way.