How to answer suggest questions is to propose one or more plausible ideas that fit the evidence, context and subject knowledge. Strong suggestions are not random possibilities: they are relevant, scientifically or logically possible and connected to the information available.
Suggest questions often appear where more than one answer could be acceptable. That flexibility can mislead students into thinking anything reasonable will earn marks.
This guide explains how to generate plausible suggestions, use clues from the question, distinguish suggestion from certainty, avoid impossible or irrelevant ideas and match the number of suggestions to the marks.
This guide is written for students, parents and educators who want a practical answer to how to answer suggest questions in exams. The purpose is to turn the command word into a repeatable operating system: identify the task, select the correct depth and evidence, construct the response, check it against the question and then practise again on a changed prompt.
The 60-Second Answer
Read the exact problem and count how many suggestions are requested. Use the source, data or context to identify constraints. Generate a plausible idea consistent with subject knowledge. State it directly and, if marks allow, link it to the evidence or mechanism that makes it reasonable. Use distinct suggestions rather than repeating one idea in different words.
Working definition: A suggest question asks the learner to propose a plausible answer, reason, improvement, explanation or action consistent with the evidence and subject context, often when more than one response may be acceptable.
Why Suggest Questions Matters
The command allows flexibility but still operates inside disciplinary constraints.
A plausible suggestion usually emerges from evidence, known mechanisms or recognized limitations rather than imagination alone.
Students also need to understand that suggestion language may be less certain than commands asking for a known fact.
The useful standard is whether the learner can recognise the command word in an unfamiliar assessment and produce the right type of reasoning without relying on a tutor, template or memorised answer.
The Hidden Problem: Open-Ended Does Not Mean Anything Goes
Students sometimes generate creative but irrelevant possibilities because the word suggest sounds permissive.
Assessment credit still depends on compatibility with the evidence and course knowledge.
The method therefore begins with constraints before brainstorming possibilities.
The Operating Model
Decode → Count Suggestions → Identify Constraints → Generate Plausible Idea → Link to Evidence → Distinguish → Check Feasibility.
Each stage has a different function. Interpretation identifies what the command requires. Selection chooses the relevant material. Construction turns that material into a markable response. Checking verifies scope, completeness and precision. Delayed retesting shows whether the method transfers beyond one familiar question.
How to answer suggest questions: Step by Step
1. Identify what kind of suggestion is needed
Is the task asking for a reason, improvement, precaution, explanation, action or future step?
Do not answer a different category.
2. Count how many are requested
Use wording and marks to estimate the number of distinct suggestions.
Avoid duplicates.
3. Extract constraints
Use data, source information, apparatus, context or stated conditions.
Suggestions must fit the situation.
4. Generate a plausible option
Use subject knowledge to propose an idea that could work or explain the evidence.
Avoid impossible or irrelevant speculation.
5. Link it to evidence where useful
State why the idea fits the observed pattern or problem.
This strengthens plausibility.
6. Keep suggestions distinct
When several are needed, use genuinely different mechanisms or actions.
Do not paraphrase one point.
7. Use appropriate certainty
Suggestion does not always imply proof.
Use language matching the evidence.
8. Check feasibility and relevance
Ask whether the suggestion could actually operate in the stated context.
Remove decorative ideas.
What Strong Performance Looks Like
- The requested suggestion type is clear.
- The number of suggestions matches the task.
- Constraints are used.
- Ideas are plausible.
- Evidence links are visible.
- Suggestions are distinct.
- Certainty is appropriate.
- Feasibility is checked.
These indicators are behavioural rather than personal. They can therefore be taught, rehearsed, checked and improved.
Common Failure Modes
1. Random possibility
The idea is imaginable but unsupported.
Use the question constraints.
2. Wrong suggestion type
The task asks for an improvement but receives a cause.
Identify the category first.
3. Duplicate points
Two suggestions are the same mechanism with different words.
Use distinct ideas.
4. Impossible mechanism
The answer contradicts subject knowledge.
Check feasibility.
5. No evidence link
A plausible idea is disconnected from the stimulus.
Explain why it fits.
6. Overcertainty
A suggestion is presented as proven fact.
Calibrate language.
7. Too many weak ideas
The learner lists many guesses.
Prioritize the strongest requested number.
8. Ignoring context
A generic answer would apply to any experiment or passage.
Use specific constraints.
How the Strategy Changes Across Subjects
Science
Suggest reasons, method improvements, controls or explanations consistent with data and scientific principles.
The subject standard remains decisive. General exam technique can organise the response, but discipline-specific knowledge determines whether the content is actually correct and sufficient.
English
Suggest interpretations, alternatives or likely motives when the text leaves room for more than one supported answer.
The subject standard remains decisive. General exam technique can organise the response, but discipline-specific knowledge determines whether the content is actually correct and sufficient.
Mathematics
Suggest methods, estimates or interpretations where several valid approaches may exist.
The subject standard remains decisive. General exam technique can organise the response, but discipline-specific knowledge determines whether the content is actually correct and sufficient.
Humanities
Suggest reasons, implications or actions consistent with source evidence and contextual knowledge.
The subject standard remains decisive. General exam technique can organise the response, but discipline-specific knowledge determines whether the content is actually correct and sufficient.
Primary School, Secondary School and Examination Years
Primary school
Teach clue plus plausible idea: what do we know from the question, and what suggestion fits that information?
Use short prompts and concrete examples. Adults can model the response structure, then fade the scaffold as the learner becomes able to select the correct response type independently.
Secondary school
Students should learn to distinguish open-ended plausibility from unsupported guessing and generate distinct answers under mark limits.
Secondary learners should increasingly decide scope, evidence, depth and checking without waiting for a teacher to signal what belongs in the answer.
Before major examinations
Use unfamiliar contexts and mark whether the suggestion fits constraints rather than whether it matches one memorized sentence.
Near major exams, mix command words in the same practice set so the learner must identify the required thinking before starting to write.
A Focused 60-Minute Practice Session
- 10 minutes — classify what kind of suggestion each prompt asks for.
- 10 minutes — identify constraints in five questions.
- 15 minutes — generate and justify plausible suggestions.
- 10 minutes — practise distinct two-point answers.
- 10 minutes — remove impossible or generic suggestions.
- 5 minutes — check certainty language.
The exact timings can change. What matters is the sequence: command identification, independent answer, feedback, targeted repair and fresh retest.
Diagnostic Checklist
- Is the suggestion type correct?
- Is the requested number supplied?
- Are constraints used?
- Is the idea plausible?
- Is evidence linked where useful?
- Are suggestions distinct?
- Is certainty appropriate?
- Is the idea feasible in context?
Use the checklist to identify the earliest decision that failed. A weak response may begin with a command-word error long before the final sentence appears.
Practice Laboratory
Practice 1: Constraint-first
List three facts from the question before suggesting anything.
Use them to filter ideas.
Practice 2: Suggestion category
Sort prompts into reason, improvement, precaution, action and explanation.
Answer one from each.
Practice 3: Plausibility ranking
Rank four suggestions from strongest to weakest.
Explain the evidence.
Practice 4: Duplicate repair
Rewrite two overlapping suggestions into distinct mechanisms.
Check independence.
Practice 5: Generic-to-specific
Turn a vague improvement such as be more careful into a concrete procedural change.
Link it to the problem.
Practice 6: Evidence link
After each suggestion, add because the data/source shows… where appropriate.
Keep concise.
Practice 7: Certainty calibration
Rewrite definite claims as plausible suggestions when proof is absent.
Avoid excessive hedging.
Practice 8: Fresh-context transfer
Use the same reasoning routine on an unfamiliar subject stimulus.
Check whether it remains evidence-led.
Three Student Cases
Case 1: Maren guesses broadly
Maren writes any possibility that comes to mind.
Her tutor makes her list constraints before generating ideas.
Her suggestions become fewer and more defensible.
Case 2: Iona duplicates
Iona gives two suggestions that both mean increase the sample.
She labels the mechanism behind each point before writing.
Distinctness improves.
Case 3: Leonie uses generic improvements
Leonie writes repeat experiment or be careful for every method question.
She is required to identify the actual source of error first.
Her improvements become targeted and useful.
How Parents Can Help Without Taking Over
Parents can support suggest questions by asking the learner to explain the command and response plan rather than supplying content. Useful prompts include: “What does the command word ask you to do?”, “Which evidence belongs?”, “How many parts are needed?”, “What would make this incomplete?”, and “How will you know when to stop?”
Once the learner can perform the decision reliably, remove the prompt. The goal is independent exam control.
How Tutors Can Use a Three-Student Small Group
In a three-student tutorial, suggest questions becomes visible because one learner can answer, another can challenge whether the command has been satisfied, and the third can check against a rubric or mark scheme. Roles then rotate.
After discussion, each learner completes a fresh individual question. Shared explanation becomes useful only when it transfers into solo performance.
How to Measure Progress
- Random guessing declines.
- Constraints are used more often.
- Suggestions become more specific.
- Duplicate points decrease.
- Generic method advice falls.
- Evidence links improve.
- Certainty becomes calibrated.
- Unfamiliar prompts become easier.
Marks are the final indicator, but process improvements often appear first: more direct openings, fewer irrelevant sentences, better evidence selection, stronger scope control and faster command-word recognition.
A Four-Week Implementation Plan
Week 1 — Identify constraints
Use short unfamiliar prompts and generate one plausible idea.
Focus on relevance.
Week 2 — Build distinct suggestions
Practise multi-mark questions and mechanism labels.
Avoid duplicates.
Week 3 — Add evidence and feasibility
Support ideas and reject impossible options.
Use several subjects.
Week 4 — Simulate
Complete suggest questions inside mixed exam sets.
Review whether failures were category, plausibility or evidence problems.
Evidence and Responsible Use
Cambridge International’s practical-skills guidance emphasizes reasoning from experiments, data and unfamiliar situations rather than relying only on memorized procedures. Cornell’s exam guidance similarly stresses decoding the question before answering. Suggest-type questions therefore reward plausible, context-sensitive responses rather than unrestricted guessing.
Command-word meanings can vary slightly by subject, school and examination board. Use official syllabus documents, teacher guidance and mark schemes as the final standard, while using this framework to build transferable reasoning habits.
- Cambridge International: Practical skills
- Cornell: How to Tackle Exam Questions
- Princeton: Preparing for Exams
Frequently Asked Questions
Does suggest mean there is more than one possible answer?
Often yes, but acceptable answers still need to fit the evidence and subject knowledge.
Do I have to explain my suggestion?
Use the marks and wording; a brief link often strengthens the answer.
How many suggestions should I give?
Use the requested number and mark allocation.
Can I write any reasonable idea?
It should be plausible in the actual context, not just generally reasonable.
What makes two suggestions distinct?
They should rely on different mechanisms, actions or reasons.
Should I use ‘might’ or ‘could’?
Those can be appropriate when the answer is genuinely a plausible possibility rather than established fact.
How do I suggest an improvement?
Identify the weakness first, then propose a specific change that addresses it.
How do I practise?
Use unfamiliar scenarios and compare your suggestions with mark-scheme examples and subject principles.
Helpful Reading on eduKateSingapore
- How to Read Exam Questions
- How to Answer Describe Questions
- How to Answer Explain Questions
- How to Check Your Work
- How to Answer Experimental Design Questions
- How to Answer Inference Questions
Suggestion Is Constrained Possibility, Not Random Guessing
The learner has room to propose, but the proposal still has to fit the evidence and the subject.
Identify the task. Read the constraints. Generate a plausible idea. Link it to evidence. Keep multiple suggestions distinct. Check feasibility.
Properly taught kids shine a bright light into the future.
Extended Diagnostic Workshops
Workshop 1: Identify what kind of suggestion is needed × Wrong suggestion type
Set up one fresh exam-style question where the learner must practise identify what kind of suggestion is needed while watching specifically for wrong suggestion type. Is the task asking for a reason, improvement, precaution, explanation, action or future step? Require an independent response before any model is shown so the actual decision becomes visible.
The task asks for an improvement but receives a cause. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rank four suggestions from strongest to weakest. Explain the evidence. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 2: Count how many are requested × No evidence link
Set up one fresh exam-style question where the learner must practise count how many are requested while watching specifically for no evidence link. Use wording and marks to estimate the number of distinct suggestions. Require an independent response before any model is shown so the actual decision becomes visible.
A plausible idea is disconnected from the stimulus. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Turn a vague improvement such as be more careful into a concrete procedural change. Link it to the problem. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 3: Extract constraints × Ignoring context
Set up one fresh exam-style question where the learner must practise extract constraints while watching specifically for ignoring context. Use data, source information, apparatus, context or stated conditions. Require an independent response before any model is shown so the actual decision becomes visible.
A generic answer would apply to any experiment or passage. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rewrite definite claims as plausible suggestions when proof is absent. Avoid excessive hedging. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 4: Generate a plausible option × Duplicate points
Set up one fresh exam-style question where the learner must practise generate a plausible option while watching specifically for duplicate points. Use subject knowledge to propose an idea that could work or explain the evidence. Require an independent response before any model is shown so the actual decision becomes visible.
Two suggestions are the same mechanism with different words. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: List three facts from the question before suggesting anything. Use them to filter ideas. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 5: Link it to evidence where useful × Overcertainty
Set up one fresh exam-style question where the learner must practise link it to evidence where useful while watching specifically for overcertainty. State why the idea fits the observed pattern or problem. Require an independent response before any model is shown so the actual decision becomes visible.
A suggestion is presented as proven fact. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rank four suggestions from strongest to weakest. Explain the evidence. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 6: Keep suggestions distinct × Random possibility
Set up one fresh exam-style question where the learner must practise keep suggestions distinct while watching specifically for random possibility. When several are needed, use genuinely different mechanisms or actions. Require an independent response before any model is shown so the actual decision becomes visible.
The idea is imaginable but unsupported. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Turn a vague improvement such as be more careful into a concrete procedural change. Link it to the problem. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 7: Use appropriate certainty × Impossible mechanism
Set up one fresh exam-style question where the learner must practise use appropriate certainty while watching specifically for impossible mechanism. Suggestion does not always imply proof. Require an independent response before any model is shown so the actual decision becomes visible.
The answer contradicts subject knowledge. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rewrite definite claims as plausible suggestions when proof is absent. Avoid excessive hedging. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 8: Check feasibility and relevance × Too many weak ideas
Set up one fresh exam-style question where the learner must practise check feasibility and relevance while watching specifically for too many weak ideas. Ask whether the suggestion could actually operate in the stated context. Require an independent response before any model is shown so the actual decision becomes visible.
The learner lists many guesses. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: List three facts from the question before suggesting anything. Use them to filter ideas. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 9: Identify what kind of suggestion is needed × Wrong suggestion type
Set up one fresh exam-style question where the learner must practise identify what kind of suggestion is needed while watching specifically for wrong suggestion type. Is the task asking for a reason, improvement, precaution, explanation, action or future step? Require an independent response before any model is shown so the actual decision becomes visible.
The task asks for an improvement but receives a cause. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rank four suggestions from strongest to weakest. Explain the evidence. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 10: Count how many are requested × No evidence link
Set up one fresh exam-style question where the learner must practise count how many are requested while watching specifically for no evidence link. Use wording and marks to estimate the number of distinct suggestions. Require an independent response before any model is shown so the actual decision becomes visible.
A plausible idea is disconnected from the stimulus. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Turn a vague improvement such as be more careful into a concrete procedural change. Link it to the problem. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 11: Extract constraints × Ignoring context
Set up one fresh exam-style question where the learner must practise extract constraints while watching specifically for ignoring context. Use data, source information, apparatus, context or stated conditions. Require an independent response before any model is shown so the actual decision becomes visible.
A generic answer would apply to any experiment or passage. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rewrite definite claims as plausible suggestions when proof is absent. Avoid excessive hedging. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 12: Generate a plausible option × Duplicate points
Set up one fresh exam-style question where the learner must practise generate a plausible option while watching specifically for duplicate points. Use subject knowledge to propose an idea that could work or explain the evidence. Require an independent response before any model is shown so the actual decision becomes visible.
Two suggestions are the same mechanism with different words. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: List three facts from the question before suggesting anything. Use them to filter ideas. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 13: Link it to evidence where useful × Overcertainty
Set up one fresh exam-style question where the learner must practise link it to evidence where useful while watching specifically for overcertainty. State why the idea fits the observed pattern or problem. Require an independent response before any model is shown so the actual decision becomes visible.
A suggestion is presented as proven fact. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rank four suggestions from strongest to weakest. Explain the evidence. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 14: Keep suggestions distinct × Random possibility
Set up one fresh exam-style question where the learner must practise keep suggestions distinct while watching specifically for random possibility. When several are needed, use genuinely different mechanisms or actions. Require an independent response before any model is shown so the actual decision becomes visible.
The idea is imaginable but unsupported. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Turn a vague improvement such as be more careful into a concrete procedural change. Link it to the problem. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 15: Use appropriate certainty × Impossible mechanism
Set up one fresh exam-style question where the learner must practise use appropriate certainty while watching specifically for impossible mechanism. Suggestion does not always imply proof. Require an independent response before any model is shown so the actual decision becomes visible.
The answer contradicts subject knowledge. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rewrite definite claims as plausible suggestions when proof is absent. Avoid excessive hedging. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 16: Check feasibility and relevance × Too many weak ideas
Set up one fresh exam-style question where the learner must practise check feasibility and relevance while watching specifically for too many weak ideas. Ask whether the suggestion could actually operate in the stated context. Require an independent response before any model is shown so the actual decision becomes visible.
The learner lists many guesses. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: List three facts from the question before suggesting anything. Use them to filter ideas. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 17: Identify what kind of suggestion is needed × Wrong suggestion type
Set up one fresh exam-style question where the learner must practise identify what kind of suggestion is needed while watching specifically for wrong suggestion type. Is the task asking for a reason, improvement, precaution, explanation, action or future step? Require an independent response before any model is shown so the actual decision becomes visible.
The task asks for an improvement but receives a cause. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rank four suggestions from strongest to weakest. Explain the evidence. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 18: Count how many are requested × No evidence link
Set up one fresh exam-style question where the learner must practise count how many are requested while watching specifically for no evidence link. Use wording and marks to estimate the number of distinct suggestions. Require an independent response before any model is shown so the actual decision becomes visible.
A plausible idea is disconnected from the stimulus. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Turn a vague improvement such as be more careful into a concrete procedural change. Link it to the problem. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 19: Extract constraints × Ignoring context
Set up one fresh exam-style question where the learner must practise extract constraints while watching specifically for ignoring context. Use data, source information, apparatus, context or stated conditions. Require an independent response before any model is shown so the actual decision becomes visible.
A generic answer would apply to any experiment or passage. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rewrite definite claims as plausible suggestions when proof is absent. Avoid excessive hedging. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 20: Generate a plausible option × Duplicate points
Set up one fresh exam-style question where the learner must practise generate a plausible option while watching specifically for duplicate points. Use subject knowledge to propose an idea that could work or explain the evidence. Require an independent response before any model is shown so the actual decision becomes visible.
Two suggestions are the same mechanism with different words. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: List three facts from the question before suggesting anything. Use them to filter ideas. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 21: Link it to evidence where useful × Overcertainty
Set up one fresh exam-style question where the learner must practise link it to evidence where useful while watching specifically for overcertainty. State why the idea fits the observed pattern or problem. Require an independent response before any model is shown so the actual decision becomes visible.
A suggestion is presented as proven fact. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rank four suggestions from strongest to weakest. Explain the evidence. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 22: Keep suggestions distinct × Random possibility
Set up one fresh exam-style question where the learner must practise keep suggestions distinct while watching specifically for random possibility. When several are needed, use genuinely different mechanisms or actions. Require an independent response before any model is shown so the actual decision becomes visible.
The idea is imaginable but unsupported. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Turn a vague improvement such as be more careful into a concrete procedural change. Link it to the problem. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 23: Use appropriate certainty × Impossible mechanism
Set up one fresh exam-style question where the learner must practise use appropriate certainty while watching specifically for impossible mechanism. Suggestion does not always imply proof. Require an independent response before any model is shown so the actual decision becomes visible.
The answer contradicts subject knowledge. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rewrite definite claims as plausible suggestions when proof is absent. Avoid excessive hedging. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 24: Check feasibility and relevance × Too many weak ideas
Set up one fresh exam-style question where the learner must practise check feasibility and relevance while watching specifically for too many weak ideas. Ask whether the suggestion could actually operate in the stated context. Require an independent response before any model is shown so the actual decision becomes visible.
The learner lists many guesses. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: List three facts from the question before suggesting anything. Use them to filter ideas. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.
Workshop 25: Identify what kind of suggestion is needed × Wrong suggestion type
Set up one fresh exam-style question where the learner must practise identify what kind of suggestion is needed while watching specifically for wrong suggestion type. Is the task asking for a reason, improvement, precaution, explanation, action or future step? Require an independent response before any model is shown so the actual decision becomes visible.
The task asks for an improvement but receives a cause. After correction, change one meaningful feature—topic, wording, evidence, mark allocation, data, source or context. Then use this related exercise: Rank four suggestions from strongest to weakest. Explain the evidence. Finish with a delayed retest so success cannot be explained only by immediate memory of the correction.