Computational thinking: how computers can inspire us to solve problems
21st Century Skills

Computational thinking: how computers can inspire us to solve problems

Robo Wunderkind Team

An approach to problem-solving that stems from computer science can actually help everyone. We teach it to children as young as five.

What is computational thinking?

Computational thinking basically means solving problems the way a computer would. This might sound off-putting to some – after all, aren’t we, humans, above that? Surely, a computer cannot process the complexity of human decision-making! That is true, but, after all, we’re the ones who designed the way computers work. Now, we’re starting to see that this problem-solving method can find its use beyond computer science. We’re making use of it in all the Robo Wunderkind lessons.

Although this approach stems from computer science, it is being applied in humanities and arts alike and can be useful regardless of profession. (Same goes for Robo Wunderkind – although it teaches coding and robotics, it can also be used for teaching arts, sciences, and humanities.)

So how does it work?

Computational thinking consists of 4 steps:

  1. Decomposition: breaking a problem down into smaller parts that are easier to manage
  2. Pattern recognition: find a repeating trend within the small parts
  3. Abstraction (or pattern generalization): identify general principles and filter the unnecessary parts
  4. Algorithm design: identify and organize the steps required to solve a problem into rules, insights, principles

Here are just a few examples of how to put it to use.

Decomposition can be applied to any analysis of a problem. In literary analysis, you don’t just analyze a chunk of text. Instead, you break it down into smaller parts like themes, motifs, tone, stanzas, lines, rhyme, and more. And only once you analyzed all of these can you say what the whole poem is about. We encourage children to analyze the tasks they perform with Robo before they start working to solve them.

Pattern recognition is common across all disciplines. For example, as an epidemiologist trying to cure a disease, you need to look at different cases and look for common patterns. How did people get infected? How long is the incubation period? What places have they visited? What symptoms showed up first? We build our lessons in a way that makes it easier for children to build on previous knowledge and apply it to new cases.

Abstraction is like a selection. In computer science, this would be the part where you’d write a program or code. Or, when creating a recipe, this would be the time to perfect it. What ingredients can we leave out, what are those we absolutely need for a good meal? (Hint: you need good dough for a good pizza, but you really don’t need pineapple!)

Algorithm design means a coherent step-by-step manual. How do you construct this piece of furniture? How do you install a program? How do you make a good pizza? How do you build a flashlight with Robo Wunderkind?

Computational thinking & Robo Wunderkind

With the help of computational thinking, you can analyze existing data, interpret and visualize complex phenomena, make predictions based on what you see and create simulations on how to solve complicated issues. You basically break down a problem into small pieces, work on each individually and bring everything back together to solve it.

This is how we structure our lessons with Robo Wunderkind.

  1. Decomposition: what is Robo’s task? What does he need to achieve it? How can we help him?
  2. Pattern recognition: motors power the wheels that help Robo move around, the main block makes sounds to communicate, the servo motor helps him turn around…
  3. Abstraction: so what parts will we need if Robo, say, needs to travel to Toy Town? It needs to move, look around, communicate with other toys…
  4. Algorithm design: to help Robo get to Toy Town, attach the motors and wheels to the main block with connectors, to make sounds, program the main block, to look around, attach the servo motor, and so on.

Through play, children learn the basics of computational thinking, which they can then apply elsewhere when they encounter problems. The principles are simple. They can be applied to nearly any issue.

Take a look at out our curriculum to learn more. Or, talk to us personally at ISTE 2019. We will be at booth #1051 and can’t wait to discuss how edtech can be used in your classroom!

Try this hands-on STEAM tool supported by a library of curriculum resources with your students! Get in touch with us to learn more about it.
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