The way of thinking that breaks a task into steps, finds the pattern, and writes down exactly what happens. Its value outside computing is less in the code than in the precision it forces.
State the process precisely enough that something else could carry it out
Wing described computational thinking as a fundamental skill for everyone, not only for computer scientists: decomposition, pattern recognition, abstraction and the design of step-by-step procedures. Brennan and Resnick separate its concepts, practices and perspectives. Its claims to transfer beyond computing are contested, and Denning has argued they were overstated. It is strongest where a process can be written down and has not been. It is weakest where the thing that matters cannot be made precise.
Computational thinking
In ordinary life
The recipe that fails for a friend
You give a friend your recipe. It fails. You watched yourself cook it a hundred times, and it turns out half of it was never written down: the pan you always use, the point where you turn the heat down because it smells right. The recipe was a summary. The process was in your hands.
Computational thinking is the habit of writing the process down precisely enough that someone, or something, else could carry it out. Doing so exposes every step you were doing without noticing. Papert saw this decades before the term existed. Children programming a turtle to draw a shape had to make their own thinking explicit, and debugging it taught them something about thinking.
Abstraction is the harder half. Deciding which details of a process matter, and which can be hidden, is a judgement about what the process is for. A timetable, a marking scheme and a referral procedure are all abstractions of this kind. Most people only notice them when one fails.
The educational experience
Every step, written down
Every page follows the same school on the same kind of day. The school has decided to increase students’ use of generative AI, and the plan is due for review. The five people and the situation stay fixed. Only the way of thinking changes.
All five, interconnected
One day 9 beats
The five threads as they were written, interleaved, so the hand-offs between them are visible. Pick a person to follow their day on its own. The people are illustrative, and the same five appear on every way’s page.
08:00
Mr Osei, before school
Adoption in history is at thirty per cent. His explanation is that some teachers are reluctant. He asks the computing department to find out what is actually happening, expecting confirmation.
09:15
Nadia and a computing teacher
They start with one question. What are all the steps between a teacher deciding to use the tool and a student using it? Nadia suggests writing it as a flowchart, one box per step, and not skipping any step because it seems obvious.
10:30
Ms Lim, being interviewed
Ms Lim walks them through her week. Decide to use it. Request class access. Wait for the request to be approved. Check each student has a signed consent form. Chase the ones who have not. Try again. Nadia counts eleven steps before a single student opens the tool.
11:45
Nadia, drawing the flowchart
She notices a loop. A missing consent form sends the teacher back to the start of the approval step, not just to the missing student. One absent form blocks a whole class. She marks it in red.
12:30
Rob, reading an email
He gets a reminder that Nadia’s consent form is missing. He is sure he signed it. He did, on paper, three weeks ago. The flowchart shows paper forms go to the office, wait to be scanned, and are matched by hand.
14:00
Mr Osei, seeing the flowchart
He had a story about reluctance. The flowchart shows a procedure in which one missing form in a class of thirty resets the whole request, and most forms wait nine days to be scanned. Ms Lim was not reluctant. She was stuck in a loop.
15:20
Priya, on a call with the school
She reads the flowchart and recognises her own guidance in step four. The guidance says every student in a class must have consent before the class is enabled. It was written to protect children. It was never written as a procedure, so nobody checked what it did when a form went missing.
16:00
Same call
They agree a change of one rule. Students with consent can be enabled individually, and the rest join as their forms arrive. The protection stays the same. The loop disappears.
19:30
Nadia and Rob
Nadia shows Rob the flowchart with his form in it. He is pleased to find he was right about signing it. She is pleased that the bug was in the process and not in the code.
The explanation everyone had was about people’s attitudes. Writing the process down step by step, with no step skipped for being obvious, found a loop in a well-meant rule. The computational part was not the flowchart symbols. It was the insistence on precision about what actually happens next.
Implications
What this way of thinking does to teaching and learning
The same five dimensions for all four ways. This is the part that can be tabulated. The two narratives above are the part that can be argued with.
What it notices
Steps that were being done without anyone writing them down, and the conditions that send a process back to the start. It notices repetition that could become a rule, and the point at which an informal routine becomes too complicated to carry in anyone’s head.
What it cannot see
Anything that loses its meaning when made precise. A flowchart of a good lesson captures the sequence and misses the judgement. Computational thinking tends to treat what cannot be specified as noise, and in schools that is often where the important thing is.
It can also see a process without seeing its purpose. A perfectly efficient consent procedure is only good if consent is the right thing to be checking.
What it asks of teachers and students
Precision, and patience with the gap between what you meant and what you said. Debugging is the core experience: the process did exactly what you wrote, which was not what you intended. Grover and Pea note that schools often teach the concepts without the practices, so students learn what a loop is without ever debugging one.
Good work writes a process someone else could follow and shows where it breaks. Weak work produces a flowchart of the obvious steps and hides the difficult one inside a box labelled "process".
Where the values sit
In the abstraction. Deciding which details a procedure can safely ignore is deciding what does not matter. O’Neil’s account of scoring systems in schools and hiring shows what happens when that decision is made carelessly and then automated. A rule applied precisely at scale carries its values further and faster than any person could.
How it gets hollowed out
A coding club renamed as a thinking curriculum, and the claim that programming in a block language makes students better at everything. Denning’s critique is that the transfer claims were never well evidenced. The hollow version teaches syntax and calls it thinking. The strong version teaches precision about processes, which is useful long after the syntax is forgotten.
Reading
Where it comes from
Published works only. Start with the first on the list.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
The short essay that made the case for computational thinking as a skill for everyone.
Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. Basic Books.
Children programming as a way of making their own thinking explicit, long before the term.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Paper presented at the annual meeting of the American Educational Research Association, Vancouver.
Concepts, practices and perspectives as three separate things to teach and assess.
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.
What schools are teaching under the name, and the gaps.
Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39.
The critique of overstated claims that computational thinking transfers everywhere.
O’Neil, C. (2016). Weapons of Math Destruction. Crown.
What happens to the values inside a procedure once it is automated at scale.