Being able to turn a complex problem into one we can easily understand is a skill that is extremely useful. In fact, it’s a skill you already have and probably use every day.

For example, it might be that you need to decide what to do with your group of friends. If all of you like different things, you would need to decide:

• what you could do
• where you could go
• who wants to do what
• what you have previously done that has been a success in the past
• how much money you have and the cost of any of the options
• what the weather might be doing
• how much time you have

From this information, you and your friends could decide more easily where to go and what to do – in order to keep most of your friends happy. You could also use a computer to help you to collect and analyse the data to devise the best solution to the problem, both now and if it arose again in the future, if you wished.

Another example might occur when playing a videogame. Depending on the game, in order to complete a level you would need to know:

• what items you need to collect, how you can collect them, and how long you have in which to collect them
• where the exit is and the best route to reach it in the quickest time possible
• what kinds of enemies there are and their weak points

From these details you can work out a strategy for completing the level in the most efficient way.

If you were to create your own computer game, these are exactly the types of questions you would need to think about and answer before you were able to program your game.

Both of the above are examples of where computational thinking has been used to solve a complex problem:

• each complex problem was broken down into several small decisions and steps (eg where to go, how to complete the level – decomposition)
• only the relevant details were focused on (eg weather, location of exit – abstraction)
• knowledge of previous similar problems was used (pattern recognition)
• to work out a step by step plan of action (algorithms)

Source BBC