Computational thinking - we're already doing it

A big challenge with any new information is figuring out what it means, and cutting through the jargon. If you've tried to buy a new TV recently, change your electricity provider or even tackled the juice and smoothie menu in your friendly organic neighbourhood cafe you'll know how it feels to be bombarded with new terms, acronyms and technical language.

There's always a place for precise language which explains a concept clearly. But sometimes using a new term can cover up the fact that you're already familiar with a concept, just by a different name.

The NSW K-6 Science and Technology Curriculum is no different. New South Wales teachers will be teaching 'digital technologies' as part of the Science and Technology subject in 2019. It's an exciting time for teachers, parents and students, who all recognize that it's important for education to keep up with the world around us and future workplace needs. Exciting ... and ... more than a little daunting.

In 2018 we'll be explaining key terms, giving real-life examples of these terms in action, and suggesting where you can go for ideas and inspiration.

Computational Thinking 

Teachers are already familiar with design thinking and scientific thinking. A new concept is 'computational thinking'. This is a method of problem-solving used by humans and computers. It involves using strategies to organise data logically, break down problems into parts, interpret patterns and design and implement sets of instructions to solve problems.

Computational Thinking in Practice

What does this look like in a classroom? Imagine you're planning an imaginary class excursion to the zoo with your students. Let's use computational thinking to plan it out.

What data do you have and how can you organize it? The number of students, venue, date of travel and budget? - that's abstraction, or process of organizing data to focus on the key information without getting bogged in detail. (Luckily in this classroom activity it means you can ignore completing a risk assessment!)

Next, let's break the process into parts: how will you get there? What will you do when you arrive? How will you get back? This is 'decomposition' - breaking things into small enough parts to start designing solutions for. You may do it many times - if you need a bus and a train to get to the zoo, you'd break travel into two further sections: bus travel and train travel. 

Pattern recognition is simply spotting parts of your problem that are similar to problems you've solved before - remember when you went to the aquarium last year? Maybe the travel arrangements were similar to planning a trip to the zoo. Let's see what we did last time and what we can use again.

Lastly, it's time to set out your instructions clearly by creating a well crafted set of instructions: an algorithm. Bring together all of the information and data you've gathered above into a clear set of instructions to make that trip to the zoo amazing! It might look like:

  • pack a bag with a drink bottle, a hat, and a raincoat
  • arrive at school at 8:00 am
  • check that all students and teachers have arrived
  • board the bus heading North
  • exit the bus at the West Street train station
  • board the train to the Zoo
  • (I think by now you know where this is heading.)

Here's an example of computational thinking in action. No computers, no coding, but a key concept in the new Science and Technologies syllabus that is engaging to discuss with students. Try jumbling up the instructions - what happens? Do you get to the zoo? Where are you going on your next excursion?