Two computer scientists talking at computers with code on the screens

What is computational thinking?

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Computational thinking (CT) is a problem-solving technique that imitates the process computer programmers go through when writing computer programmes and algorithms. This process requires programmers to break down complex problems and scenarios into bite size pieces that can be fully understood in order to then develop solutions that are clear to both computers and humans. So, like programmers, those who apply computational thinking techniques will break down problems into smaller, simpler fragments, and then outline solutions to address each problem in terms that any person can comprehend. 

Computational thinking requires:

  • exploring and analysing problems thoroughly in order to fully understand them
  • using precise and detailed language to outline both problems and solutions
  • applying clear reasoning at every stage of the process

In short, computational thinking encourages people to approach any problem in a systematic manner, and to develop and articulate solutions in terms that are simple enough to be executed by a computer – or another person. 

What are the four parts of computational thinking?

Computational thinking has four foundational characteristics or techniques. These include:

Decomposition

Decomposition is the process of breaking down a problem or challenge – even a complex one – into small, manageable parts.

Abstraction

Also known as generalisation, abstraction requires computational thinkers to focus only on the most important information and elements of the problem, and to ignore anything else, particularly irrelevant details or unnecessary details.

Pattern recognition

Also known as data and information visualisation, pattern recognition involves sifting through information to find similar problems. Identifying patterns makes it easier to organise data, which in turn can help with problem solving.  

Algorithm design

Algorithm design is the culmination of all the previous stages. Like a computer programmer writing rules or a set of instructions for a computer algorithm, algorithmic thinking comes up with step-by-step solutions that can be followed in order to solve a problem.

Testing and debugging can also occur at this stage to ensure that solutions remain fit for purpose.

Why is computational thinking important?

For computer scientists, computational thinking is important because it enables them to better work with data, understand systems, and create workable algorithms and computation models.

In terms of real-world applications outside of computer science, computational thinking is an effective tool that can help students and learners develop problem-solving strategies they can apply to both their studies as well as everyday life. In an increasingly complicated, digital world, computational thinking concepts can help people tackle a diverse array of challenges in an effective, manageable way. Because of this, it is increasingly being taught outside of a computer science education, from the United Kingdom’s national curriculum to the United States’ K-12 education system.

How can computational thinking be used?

Computational thinking competencies are a requirement for any computer programmer working on algorithms, whether they’re for automation projects, designing virtual reality simulations, or developing robotics programmes.

But this thinking process can also be taught as a template for any kind of problem, and used by any person, particularly within high schools, colleges, and other education settings.

Dr Shuchi Grover, for example, is a computer scientist and educator who has argued that the so-called “four Cs” of 21st century learning – communication, critical thinking, collaboration, and creativity – should be joined by a fifth: computational thinking. According to Grover, it can be beneficial within STEM subjects (science, technology, engineering and mathematics), but is also applicable to the social sciences and language and linguistics.

What are some examples of computational thinking?

The most obvious examples of computational thinking are the algorithms that computer programmers write when developing a new piece of software or programme. Outside of computer programming, though, computational thinking can also be found in everything from instructional manuals for building furniture to recipes for baking a chocolate cake – solutions are broken down into simple steps and communicated clearly and precisely.  

What is the difference between computational thinking and computer science?

Computer science is a large area of study and practice, and includes an array of different computer-related disciplines, such as computing, automation, and information technology. 

Computational thinking, meanwhile, is a problem-solving method created and used by computer scientists – but it also has applications outside the field of computer science.

How can we teach computational thinking?

Teaching computational thinking was popularised following the publication of an essay on the topic in the Communications of the ACM journal. Written by Jeannette Wing, a computer science researcher, the essay suggested that computational thinking is a fundamental skill for everyone and should be integrated into other subjects and lesson plans within schools. 

This idea has been adopted in a number of different ways around the world, with a growing number of resources available to educators online. For example:

Become a computational thinker

Develop computational thinking skills with the online MSc Computer Science at the University of York. Through your taught modules, you will be able to apply computational thinking in multiple programming languages, such as Python and Java, and be equipped to engage in solution generation across a broad range of fields. Some of the modules you’ll study include algorithms and data structures, advanced programming, artificial intelligence and machine learning, cyber security threats, and computer architecture and operating systems.

This master’s degree has been designed for working professionals and graduates who may not have a computer science background, but who want to launch a career in the lucrative field. And because it’s studied 100% online, you can learn remotely – at different times and locations – part-time around your full-time work and personal commitments.