Seminar focus: audit collection decisions
Use collection scenarios to examine how interviewer behaviour, timing, non-response, and recording choices can change a dataset. Ākonga question one another as participant, collector, and reviewer.
- Seminar move: Three-role evidence hearing with a reliability checklist
- Seminar outcome: A collection-quality verdict supported by specific evidence
🎯 Learning Intentions
- Conduct data collection efficiently and accurately
- Use logical systems (tally marks, spreadsheets) to record data
- Troubleshoot problems during collection
🎥 Media Anchor (8 mins)
Video: Research Skills for Students
- What quality checks should happen while collecting data?
- How do we reduce errors when recording class survey responses?
1. Preparation: Data Tables (10 mins)
Before you collect, you need a place to put the answers!
Activity: Draw a data table in your workbook.
| Name (Optional) | Question 1 Answer | Question 2 Answer |
|-----------------|-------------------|-------------------|
| ............... | ................. | ................. |
| ............... | ................. | ................. |
2. Field Work: Data Collection (30 mins)
This is the main action phase! Students execute their plans:
- Circulating the room to survey classmates
- Going outside to observe (if allowed)
- Distributing digital survey links
Teacher Role: Circulate and ensure respectful interaction. Check that students are recording data, not just listening.
3. Data Quality Check (10 mins)
Review your data:
- Do you have enough responses? (Aim for 20-30+)
- Is any data messy or unclear?
- Did you miss anyone?
4. Next Steps (5 mins)
Homework: Finish collecting any missing data so you are ready to organise it in the next lesson.
📋 Teacher Planning Snapshot
Ngā Whāinga Ako — Learning Intentions
Ākonga examine how a shared dataset was collected and where errors could have entered it.
Ngā Paearu Angitū — Success Criteria
- ✅ I can identify a point in a collection method where error could enter.
- ✅ I can say what record-keeping would make a dataset trustworthy.
Differentiation & Inclusion
Scaffold support: A dataset with three deliberate errors planted for the class to find. Extension: write the collection protocol that would prevent them.
ELL / ESOL: Model finding one error aloud before the class hunts the rest.
Inclusion: Error-hunting is a strong entry point for ākonga who find open-ended tasks difficult.
Mātauranga Māori lens: Careful recording is the practice that makes generational monitoring possible. Data recorded badly cannot be repaired later, in any knowledge system.
Prior knowledge: Students should have basic familiarity with data displays (bar graphs, dot plots). No prior statistical investigation experience required — the PPDAC inquiry cycle provides accessible scaffolding for first-time investigators.
Curriculum alignment
This lesson develops the Level 4 statistical-investigation objective through a real stage of the PPDAC cycle. See the unit curriculum companion for the exact source statement and lesson-to-evidence map.