In the “old days,” learning was about gathering and recalling facts and figures. And while there’s definite value in data retention, it doesn’t necessarily prepare a child for life. So, if you’re looking to give your children or pupils a head start, consider the value of computational thinking.
Learning in the twenty-first century is about preparing children for jobs that don’t exist yet (more about this later). So, computational thinking is the path to versatile learning that equips a child to think and problem-solve rather than recite — preparing them for whatever the future job market demands.
This article looks at the many advantages of computational thinking for kids, exploring how logical thinking is a skill for life — not just the classroom.
Ready? Let’s get started.
What is computational thinking?
You might be put off by the term, perhaps assuming that it’s about forcing creative human beings to think with the cold-hearted logic of a computer.
But actually, all of us engage in Computational Thinking (CT) every day of our lives.
For example:
We’re rushing to leave the house and can’t find our keys. We get to the train station, and the train has already departed.
We’re left with problems to solve. In a nutshell: that’s CT.
In the simplest terms, CT is problem-solving.
But if we really pick it apart, it’s the process of:
- analyzing a problem when it occurs, and
- identifying the most appropriate solution.
What are the four elements of Computational Thinking?
We could break CT down into the following processes:
- Decomposition
This is the process of breaking a problem down into smaller, more manageable parts.
Ultimately, decomposing a problem is the key to finding a reasonable and practical solution, helping large, seemingly insurmountable challenges feel approachable.
- Pattern Recognition
This is the ability to look at each element of the deconstructed problem to identify:
- Similarities
- Recurrent structures, and
- Trends
Effectively, pattern recognition helps us make informed decisions and even predictions based on the evidence and information from Step 1.
- Abstraction
This is about simplifying the problem and making it easier to visualize the solution.
Abstraction is the process of:
- channeling the essential details, and
- sifting out the irrelevancies.
Essentially, discarding the irrelevant details helps us see the solution more clearly.
For example:
You baked a cake, and it failed to rise.
So, you go back to the beginning with a tick list:
- Did you include all the ingredients in the right amounts?
- Did you preheat the oven to the correct temperature??
- Did the oven reach the required temperature?
- Did you leave the cake in the oven long enough?
This tick list helps us decompose the problem.
Then, you look for patterns:
- You check the ingredients left on your work surface – it appears you measured them all out and added them to the mix.
OK — you can extract this from further consideration.
However:
- Accurately recalling analog events that happened in the past (like preheating the oven) is difficult to determine once the moment has passed.
So, you can’t rule this one out. Instead, you can make a note to ensure that you definitely preheat the oven next time, adding a new step to the process (an algorithmic solution).
But:
- It’s also tricky to determine whether your oven reached temperature. So, it could be that it didn’t reach optimal heat.
Again, this is an analog problem that’s difficult to audit — but you can negate the problem by buying an oven thermometer and using it next time (again, adding a new process to the algorithm)
Additionally:
- If you set a timer when you put the cake in the oven, you probably baked it for the right amount of time.
So, you can abstract this point from the problem.
A solution
This means the solution is most likely to have something to do with the oven: inadequate preheating or not reaching the right temperature.
As long as you mitigate these problems in future, you’ve potentially solved your problem by abstraction.
- Algorithmic thinking
So you rewrite the instructions (algorithm) to include the new stages that will hopefully solve the problem next time.
An algorithm is essentially a list of step-by-step instructions that successfully lead to a completed task. So, if it didn’t work last time, you revise the list and try again.
So, the fourth stage of CT is about gathering the salient elements of Steps 1-3 and building a logical, efficient, and effective solution.
CT in everyday life
Essentially, we go through these thought processes daily, making decisions and abstractions in the blink of an eye. And it’s essential for children to develop these problem-solving skills because it equips them for life.
Because no matter how much we might try to shield our kids from challenges, they will have to face them eventually.
Indeed, the more your children face CT problems from an early age, the more adept they become at handling the complex learning and social situations they will encounter as they progress through their educational careers.
Why is computational thinking important for learners?
Classroom students of the present are the adults of the future. And, effectively, school prepares them for jobs that quite likely don’t exist yet.
Think about it: if you grew up in the 80s, there was no such thing as a:
- Social media influencer
- Blockchain analyst
- Podcast producer
- Freelance copywriter
- Cloud Architect
- Uber driver
- Drone operator
- Big data scientist
The list goes on. We weren’t trained for those jobs because they hadn’t happened yet.
So, the “content acquisition” of old (as in learning facts and figures) may not equip future adults with the skills for life they’ll need.
This has prompted mainstream educators around the world to move from the content acquisitional model of learning to one of higher-order thinking skills.
Therefore:
CT is a higher-order thinking skill that will equip the future generation for an ever-evolving employment market.
Computational thinking helps learners develop tech-based solutions
While CT isn’t strictly confined to technology, computational thinking helps learners consider how they might utilize tech to aid the problem-solving process.
Because if there’s “an app for that,” it makes sense for today’s learners to use it.
Computational thinking helps children:
- Develop and improve their problem-solving skills
- Ask bold questions and make creative decisions
- Evolve into independent, free thinkers.
What are the advantages of computational thinking?
In the words of Albert Einstein, “Insanity is doing the same thing over and over and expecting different results.”
On the other hand, CT teaches learners to identify problems and devise original solutions, encouraging them to find repeatable steps, using algorithmic language to express them.
- CT creates problem-solvers
Computational thinkers are natural problem solvers.
And this is a valuable skill for life and industry, from the sciences to business to healthcare to — well, everyday life.
- Innovators
Ultimately, we don’t all need to be “inventors” — computational thinkers already know what a wheel does, and they find an innovative way to use the old wheel to solve a new problem.
Computational thinkers are innovators by nature because they’re good at abstraction — identifying and sifting, extracting solutions from problems using existing processes.
Consider the “First Follower: Leadership Lessons from Dancing Guy” YouTube video. It’s had 7.8 million views because it defines something important about life, leadership, and innovation.
In a nutshell, it explores the role of the innovator — the person who sees a great idea and enhances it by reinforcement, using it to solve a new problem that the inventor might have missed.
- Computational thinking encourages evidence-based answers
One of our most significant modern social problems is the reluctance to gather the facts before forming an opinion.
Many people jump on the bandwagon without researching the whole story — they gain a little glimpse of a fact that reinforces their personal bias, and they join the protest, causing unknown levels of havoc.
Well, computational thinkers innately rely on research evidence. So we teach CT students how to use data and resources to produce genuine results.
Google, Microsoft, and Apple recruit and train their staff in CT, recognizing its importance in keeping their in-house thinking competitive and advantageous.
- CT is straightforward to teach
Any teacher reading this article will recognize how the processes of decomposition, pattern recognition, and abstraction are innate in almost all approaches to teaching and learning. After all, learning is often about finding repeatable algorithmic solutions that help pupils develop their knowledge base.
Indeed, regardless of the subject, from the sciences to the arts, computational thinking makes our learners go from unconscious incompetence to unconscious competence.
Because learning is about overcoming challenges and problems and never giving up till the solution emerges.
How CT relates to coding
Of course, computational thinking is a key skill for successful computer coders.
Effectively, computers are adept at endlessly repeating the tedious processes that we, as human thinkers, prefer to steer clear of. But in many ways, we’re better than traditional computers because they just follow instructions whether they work or not; we can work out which stage of the process went wrong.
So, the ability to problem-solve in life is directly applicable to successful coding.
Consider this:
If an algorithm is a string of instructions, like a cake recipe, computational thinking comes in handy when the cake goes flat, or you open the oven to a burnt crust. It means you missed or misread one of the steps — and CT helps us troubleshoot so you can complete the process successfully next time.
It’s why we still need people in businesses. Computer operators spend months stress-testing software processes before the application gets released to the public. So, hopefully, by the time the general public uses the software, there are no bugs that impede operation.
Codemonkey coding games for computational thinking
Codemonkey Jr. challenges learners to program a monkey’s journey to catch bananas and unlock a treasure chest.
With progressive levels that increase the challenge as the child progresses at their own rate, this fun, colorful coding platform helps children develop key CT skills while learning the functions of computer coding.
In fact, the platform is so accessible and fun your kids probably won’t even realize it’s a lesson activity at all.
What is computational thinking for early years?
You’re never too young to begin developing CT.
In fact, the UK government introduced CT into the National Curriculum for Key Stage 1 in 2014 – because developing a logical mind from a young age is advantageous for learning and life.
Some of the approaches they encouraged were:
- Pattern recognition
- Making generalizations
- Making predictions
Logical thinking was encouraged through:
- Asking children to plan activities that involve prediction
- Asking children to test their ideas
- Asking children what they think and why, asking them to recognize what they’ve witnessed and learned.
Some examples:
- Testing materials to see if they sink or float. Then, setting a challenge to build a floating boat.
- Testing materials to see if they’re waterproof. Then, building a rain shelter.
- Testing a range of materials that can float in the breeze. Then, they build a parachute for their toys.
Breaking down CT into tasks for children
A picnic planning exercise is perfect for understanding decomposition, pattern recognition, abstraction, and algorithm.
Ask pupils to:
- Decide what they’d like to eat on their picnic
- Identify which ingredients they’ll need
- Write a shopping list
- Buy the food
- Prepare the food
- Pack the food
- Carry the food to the destination
You can separate each task further by learning how to make sandwiches (they could vote on their favorite fillings), prepare a salad, or bake cupcakes.
While this fun task sounds simple, it asks children to reason, question, and overcome obstacles — all perfect introductions to computational thinking.
FAQs
What is computational thinking?
Computational thinking – in its simplest terms – is simply problem-solving. It’s the ability to break down a problem into its component parts, identify trends and patterns, differentiate between relevant and irrelevant data, and using those steps to come up with a solution to a problem. CT encourages logical thinking that benefits many learning and life situations.
What are the four components of computational thinking?
The four stages of computational thinking (CT) are: decomposition (breaking a problem down into parts), pattern recognition (identifying trends and repetitions), abstraction (sifting through the data to determine what’s useful (and what’s not), and algorithmic thinking (using the previous stages to conjure a repeatable solution to a problem).
Can kids learn computational thinking?
Yes — absolutely. Kids love solving puzzles, playing with building bricks, and making things. So, children as young as five or six are great at CT exercises. Think about asking kids to test materials, like whether a material can float, then task them with building a boat that stays afloat. Learning CT helps children develop an inquisitive mind and prepares them for the jobs they’ll have as adults.
The advantages of computational thinking for kids
I hope you’ve enjoyed reading this article on the benefits of computational thinking for children and young people.
And if you’re ready to dive in, check out some of the ways children can engage with CT exercises in our blog.
Thanks for reading.
