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OCR H446 2.2.2 Computational Methods consolidation

Part 13 of 14 · H446 2.2.2 · Computational methods

Interleaved revision across the whole of H446 2.2.2, this worksheet deliberately mixes the methods so students have to recognise which one a problem calls for instead of practising each in turn. It runs from rapid retrieval through a backtracking trace and a coded heuristic to two extended evaluations, and introduces no new content.

Students will:

  • match a described problem to the computational method at its centre
  • trace a backtracking search, then identify a dead end and the alternative explored next
  • implement a room-scoring heuristic with a defined tie rule
  • select and justify a different method for each of three needs in one scenario
  • evaluate combining a heuristic with a backtracking search on the same allocation problem

Inside: 6 explanation cells, 2 multiple-choice questions, 2 fill-in-the-blanks cells, 3 written answers, 1 Python task and 1 trace table. 37 marks, about 40 to 60 minutes.

Series: H446 2.2.2 · Computational methods, part 13 of 14.

Shared by Coding PathwayVerified teacher

  • 15 cells
  • About 45 minutes
  • CC BY-SA 4.0
  • Shared 31 Aug 2026
  • Updated 3 Sept 2026

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The whole resource, exactly as a class sees it. Answers and marking are held back.

Computational Methods consolidation

Complete the sections in order, beginning with retrieval and method selection before applying the ideas in integrated contexts. No new required knowledge is introduced.

This worksheet interleaves the full 2.2.2 set. Retrieve the method from the problem features, then apply it. Do not choose sequence, selection or iteration when the question asks for a computational method.

By the end, you will be able to

  • discriminate between closely related methods;
  • trace and implement backtracking;
  • connect data, models, estimates and visual representations to decisions;
  • write applied evaluative answers.

Complete the first retrieval questions closed book.

Retrieval map

  • recognition/solvability: translate need into inputs, rules, state and testable outputs;
  • decomposition/abstraction: divide responsibilities and retain purpose-relevant detail;
  • divide and conquer: solve smaller instances of the same problem and combine;
  • backtracking: reverse a failed choice and explore another;
  • data mining: interrogate large data for patterns, trends, relationships or anomalies;
  • heuristic: guide choices with an informed rule or estimate;
  • performance modelling: vary load and measure behaviour;
  • pipelining: feed stage outputs onwards and overlap items;
  • visualisation: map information into a model that supports understanding or planning.
Multiple choice1 mark

A solver assigns speakers to rooms, reverses an allocation when no room remains for the next speaker, then tries another room. Which method is central?

  • ABacktracking
  • BData mining
  • CPerformance modelling
  • DVisualisation
Multiple choice1 mark

A team raises simulated user load from 100 to 5000 while recording response time and throughput. Which method is central?

  • AHeuristic search
  • BPerformance modelling
  • CDivide and conquer
  • DAbstraction
Fill in the blanks3 marks
Smaller instances plus a combine step indicates gap 1; a score that orders promising choices is a gap 2; large-data pattern discovery is gap 3.
  • divide and conquer
  • heuristic
  • data mining
  • pipelining

Mixed trace

The search tries to reach 6 using distinct values 1 to 4. Read each recursive return as evidence about one branch; a failed include route causes the exclude route to be explored.

Trace table20 marks

Trace the calls and final output.

Enter a value only when it changes; record returned and printed values in order.

Use one row for each statement as it runs. Fill in a box only when that value changes on that row, and leave the rest blank.

ProgramPython
  1. def reach(total, next_value):
  2. if total == 6:
  3. return True
  4. if total > 6 or next_value > 4:
  5. return False
  6. if reach(total + next_value, next_value + 1):
  7. return True
  8. return reach(total, next_value + 1)
  9. answer = reach(0, 1)
  10. print(answer)
Trace table with 9 columns
Rowtotal (reach)next_value (reach)answerReturn valueDepthtotal == 6total > 6 or next_value > 4reach(total + next_value, next_value + 1)Output
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Written answer3 marks

Identify one dead end in the trace and explain the next alternative explored.

Use actual parameter values from your completed trace.

Students type their answer here.

Independent code synthesis

Implement a simple room heuristic. Each room has name, unused_capacity and missing_features. Score it as unused_capacity + 5 * missing_features; return the name with the smallest score. Preserve first-room order in a tie.

Coding task7 marks
def choose_room(rooms):
    pass
Written answer9 marks

A national archive wants to discover changing usage patterns, predict whether a new service can handle peak demand, and explain the planned processing stages to developers. Select and justify one computational method for each need.

For each, name the method, the information it uses or represents, and the decision it supports.

Students type their answer here.

Written answer9 marks

Evaluate the use of a heuristic to allocate archive-processing jobs before a complete backtracking search is used for unresolved conflicts.

Explain how the methods interact, then evaluate speed, solution quality, constraints and scale.

Students type their answer here.

Final closed-book checkpoint

Complete each sentence from memory. There is no answer bank and correctness is held for teacher review.

Fill in the blanks4 marks
Restoring state after a dead end identifies checkpoint gap 1. Predicting behaviour under changing workload identifies checkpoint gap 2. Feeding one software process into the next while items overlap identifies checkpoint gap 3. Mapping information so relationships can be understood identifies checkpoint gap 4.

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.