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OCR H446 2.2.2 Computational solvability and problem recognition
Part 1 of 14 · H446 2.2.2 · Computational methods
OCR H446 2.2.2 begins with a question students often skip: can this problem be solved computationally at all? Working from a rehearsal-room allocation request, the worksheet teaches them to turn a vague human aim into precise inputs, rules, stored state and testable outputs before any algorithm is chosen.
Students will:
- identify the inputs, rules, stored state and outputs hidden inside a described need
- rewrite an ambiguous requirement so that a program could execute and test it
- explain why naming sequence, selection and iteration does not answer a question about computational methods
- judge whether a loosely worded request is solvable as stated, and say what information would make part of it computable
- recall the central distinctions from memory in a closed-book check
Inside: 7 explanation cells, 1 multiple-choice question, 2 fill-in-the-blanks cells and 2 written answers. 20 marks, about 20 to 30 minutes.
Series: H446 2.2.2 · Computational methods, part 1 of 14.
Shared by Coding PathwayVerified teacher
- 12 cells
- About 30 minutes
- CC BY-SA 4.0
- Shared 31 Aug 2026
- Updated 1 Oct 2026
Preview
The whole resource, exactly as a class sees it. Answers and marking are held back.
Computational solvability and problem recognition
A community venue wants software to allocate rehearsal rooms. A request such as ‘choose the best room’ is not yet a computational problem because the word best has no agreed meaning. Before choosing an algorithm, the developer must identify the data, rules and result precisely enough for a computer to follow.
By the end, you will be able to
- identify the inputs, rules, stored state and outputs of a problem;
- judge whether a problem is computationally solvable as stated;
- distinguish an ambiguous human aim from a computable specification;
- explain how recognition shapes the method chosen.
From a need to a computable problem
A computational problem needs data that can be represented and calculations or rules that can be followed without guesswork. Any changing state must be stored, and the output needs a testable definition of success. Decomposition can split the need into smaller problems. Abstraction can remove details that do not affect the required result. These features help make a problem suitable for computational methods, but the proposed method must still finish using realistic time and storage.
A computer may support a human decision without replacing it. For example, it can rank rooms using capacity and access requirements, while a coordinator resolves a sensitive welfare judgement that has no agreed numerical rule.
Worked recognition: rehearsal-room allocation
The phrase give every group the best room is not yet precise. Refine it:
- inputs: group size, required equipment, access need and requested time;
- stored state: room capacity, equipment, availability and existing allocations;
- rules: a room must be free, large enough and meet every required feature;
- output: a room identifier or a clear no-room result;
- success: every allocation satisfies the constraints and no room is double-booked.
The word best has been replaced by testable constraints. That change makes a computational solution possible.
Which change most directly makes ‘choose the nicest available room’ computationally solvable?
- AReplace ‘nicest’ with agreed, measurable ranking criteria
- BStore a photograph of every room
- CUse a faster processor
- DAsk the computer to make a human judgement
- decomposition
- abstraction
- calculations
- decoration
Guided checkpoint: repair the statement
Draft statement: Automatically give students a fair amount of support.
First identify one ambiguous word. Then propose measurable input data, state a rule that the program can execute and name an output that can be checked. Keep the human aim, but translate the part delegated to software into precise terms.
Rewrite the draft support statement as a computationally solvable problem.
Include at least two inputs, one unambiguous rule and one measurable output.
Students type their answer here.
Common examination trap
Sequence, selection and iteration are programming constructs, not answers to ‘identify a computational method’. First recognise the character of the problem: does it involve exploring choices, extracting patterns, estimating a good next move, modelling load, overlapping stages, or making structure visible? The recognised features point towards a method.
A festival organiser says: ‘Use a computer to create the perfect running order.’ Explain whether the problem is computationally solvable as stated. Then describe the information needed to make a useful part of it computable.
Develop a judgement, not just a list. Consider ambiguity, inputs, constraints, state and success criteria.
Students type their answer here.
Closed-book checkpoint
Complete each sentence from memory. There is no answer bank and correctness is held for teacher review.
Review your understanding
Before submitting, check that you can explain the central distinction in your own words, expose the intermediate state that supports your answer and apply the method in an unfamiliar context.