Computational thinking consists of 4 key parts which are sometimes split into sub-parts. In a nutshell, it is a set of problem-solving methods that involve expressing problems and their solutions in ways that a computer could execute. You often hear teachers complaining that their students are unable to solve new, unfamiliar problems, give up too easily and can't think logically- if taught and reinforced consistently, computational thinking can create a good problem solver who is able to tackle issues and find a programmed solution.
Decomposition is breaking a problem down into component parts, which are easier to tackle. This might be the first step you take in solving a jigsaw puzzle.
Abstraction involves simplifying the problems, getting rid of unnecessary information so that you can focus on the parts that matter. Think of the ‘you are a bus driver, 7 people get on at the first step. 3 get off at the next, but 9 more get on […]. What colour are the bus driver’s eyes?’ riddle.
Pattern recognition, sometimes called generalisation, is the ability to spot when situations or problems have been repeated. This could be drawing on similar situation in the past or trying to identify areas in the current problem that could use the same solution.
Algorithmic thinking is designed to generate a series of instructions to solve a problem, this is often mistaken for creating a programmed solution but is not necessarily linked to computer-based solutions at all.
These skills can be useful in computing, but are also applicable in other situations.
They can be especially useful in answering exam questions, for example computing’s Ethical, Cultural and Environmental questions:
Generalisation – Have I seen this type of question before? How did I answer it in that exam/lesson? What element do I need to talk about?
Abstraction – Identifying the key words and command words. Am I actually answering the question being asked?
Decomposition – Who do I need to talk about in the question (stakeholders)? What aspects are involved?
Algorithmic thinking – Structuring the response, making sure that the response shows how this impacts ethics, culture and the environment?
The skills are also used in subjects such as Maths, Science and Product Design but may not have as much focus on structuring items in this way. It can be really useful in everyday situation, outdoor education and initiative based tasks to help the students to focus on using their prior knowledge or actually attempting to create a solution.
Is it time to bring ‘Computational Thinking’ into your classroom? If so how do you develop the skills in the students?
There are two main schools of thought on this process, both of which are linked to modelling the process. They then create a dialogue between either teacher and student or student and student, before moving this dialogue into a monologue. This begins as a spoken process (which can make the students feel a little self-conscious) before moving to an inner-monologue, once the stills have been established.
The two schools of thought run in similar ways:
Devil’s Advocate (DA) approach, where one person suggests ideas and the other ‘picks holes’ in this until the best solution is found. This can seem very negative but does create some effective problem solvers, as they are eventually able to spot the problems on their own and therefor adapt their solutions ‘in situ’.
It wouldn’t work because of this…
Dialectical Inquiry (DI) takes a different approach, aiming to build on the idea first given and improve it. This is much more constructive than the DA approach, which can be beneficial for students with lower confidence. This, again, could be more suited for pair work as it develops the confidence in the students before setting out on their own.
It would be better if we did it this way…
If you would like more information about Computational Thinking and how it can be applied in your subject, please get in touch with George West!