Year 4 · 60 minutes · Training data
Who disappears inside an average?
A training collection can describe one habitat well and another poorly. This lab learns one-dimensional class centroids separately for two synthetic reef habitats from the available samples. As collection effort changes, sparse estimates can shift and one habitat’s errors can rise. The test set stays fixed. Overall accuracy combines all test cases and can hide a smaller group’s poor result; equal sample counts alone do not guarantee fair outcomes.
Print reef and lagoon training samples and a fixed test batch. Explain that habitats are invented and the labels concern synthetic objects, never people. Rehearse how available samples change the learned centroids.
Compare two groups and describe a fair test. Useful earlier investigations: y2-coverage, y3-holdout Use pairs with predictor/operator roles. Swap after the first comparison. Each learner draws or writes their own explanation using one exact case.
Australian Curriculum Version 9 · Digital Technologies: AC9TDI4K03, AC9TDI4P05. Selected aspects only. This activity contributes evidence; it does not cover the full descriptor or achievement standard. A programming descriptor is not claimed for merely moving controls. ACARA AI curriculum connection · V9 Technologies These are planning connections, not ACARA endorsement or exhaustive descriptor alignment.
Present an overall score and ask what it conceals about two habitats.
Ask: “Could most correct answers come from one place?”
Listen for: “Yes, especially if it has more test cases.”
Predict the underserved habitat’s result when more samples are allocated there.
Ask: “What must we keep fixed to compare fairly?”
Listen for: “The test objects and scoring rule.”
Move collection effort between habitats. Inspect actual training points, learned reference positions and group error counts.
Ask: “Which sample changed the reference position?”
Listen for: “The new point pulled the average toward it.”
Use equal training allocation and inspect remaining overlapping cases.
Ask: “Did equal numbers guarantee equal errors?”
Listen for: “No, the groups can still have different difficulty.”
Choose an allocation and write a report containing both group fractions and overall fraction. Include a remaining misclassified point.
Ask: “How would you explain the result without hiding the smaller group?”
Listen for: “Give each group’s count before the overall result.”
A partner reproduces the chosen allocation.
Ask: “Is a higher average enough to declare the repair successful?”
Listen for: “No, we must check who improved or got worse.”
High overall accuracy means everyone is served.
Reallocate examples to the less represented habitat. Predict its error direction before recomputing centroids.
Equal training counts can still produce unequal errors because the fixed feature distributions overlap differently.
Allocate a fixed collection budget and publish group-level error counts, denominators and one unresolved case.
Check the student did not change test composition to improve the headline. Ask them to calculate one group fraction from the displayed cases.
Use two groups of five test cards and colour plus shape labels so results do not depend on colour perception.
Change collection order and explain how a small sample can make the apparent benefit unstable.
A revised sampling allocation and a two-habitat audit.
No demographic inference about students. Habitat groups are synthetic; fairness in human systems requires broader community judgement than these metrics.
Repeat stratified collection across many deterministic seeds and plot group-error distributions, demonstrating that one favourable split is weak evidence.
| Criterion | Beginning | Secure | Extending |
|---|---|---|---|
| Group evaluation | Reports only the average | Reports both group counts | Explains the aggregate’s weighting |
| Dataset repair | Adds samples indiscriminately | Targets an evidenced gap | Tests repair and acknowledges unequal residual errors |
Australian Curriculum Version 9 · Digital Technologies
References: AC9TDI4K03, AC9TDI4P05. Read the current source (checked 2026-09-07).
Evidence to assess: A revised sampling allocation and a two-habitat audit.
Selected aspects only. This activity contributes evidence; it does not cover the full descriptor or achievement standard. A programming descriptor is not claimed for merely moving controls. Moderate the supplied illustrative responses against your school unit and current achievement standard.
These are planning estimates to test with your class. A short session develops one supported claim; it does not compress the whole senior project.
| Stage | 45 minute focus | 60 minute investigation |
|---|---|---|
| Readiness and prediction | 0–5 | 0–5 |
| Trace the supplied example | 5–13 | 5–15 |
| Author and run cases | 13–25 | 15–35 |
| Counterexample and redesign | 25–35 | 35–45 |
| Explain and discuss | 35–42 | 45–55 |
| Export and handover | 42–45 | 55–60 |
For a longer project, use three 50-minute sessions. Session 1 (0–50): readiness, model, hypothesis and initial cases. Export a project and record the next test. Session 2 (50–100): reopen, check settings, author counterexamples and revise the design. Export the changed project and identify unresolved evidence. Session 3 (100–150): independent peer test, final artefact, individual explanation and moderation. If using two 60-minute sessions, stop at minute 60 after saving the first comparison; use 60–120 for redesign, independent test and defence.
Entry check: Compare two groups and describe a fair test. Ask the learner to demonstrate it before choosing the level of support.
Preparation: allow about 15 minutes to run the starter, print the cards and check a project can be reopened. This estimate has not yet been measured in a classroom pilot.
Read the entry question aloud, model one row, and label the units. Offer the case table as a large-print sheet. Keep mathematical derivations optional until the learner can explain the comparison.
For one device, use a projector: one pair predicts, one operates, and the class records on paper. Swap roles after the first comparison. For individual access, support keyboard controls and a written table equivalent to each visual. Learners may explain orally or with an annotated diagram. Never require personal data, a recorded voice, or a photograph.
Mixed readiness: if the entry check is difficult, use the linked prerequisite and the first two case cards; retain the same central question. If secure, ask the learner to design an unseen test and state which explanation it could disprove.
Compare a new example with group centroids calculated only from teaching rows.
Starting parameters: Feature A importance = 1
3 teaching rows; 1 new cases. Ties use supplied order.
| feature A | feature B | chosen | squared distance | equally close |
|---|---|---|---|---|
| 4 | 4 | A | 8.5 | 1 |
These are authored examples, not work collected from children. Assess reasoning using the lesson rubric, not whether the first prediction was correct.
Beginning: “It worked because the result looks right.” This identifies no exact case, control or measurement. Ask the learner to point to one row and say what happened.
Developing: “In the first case I recorded feature A: 4; feature B: 4; chosen: A; squared distance: 8.5; equally close: 1.” This cites evidence, but does not yet explain how the result follows from the rule. Ask the learner to trace the relevant step.
Secure: “For the first supplied case, feature A: 4; feature B: 4; chosen: A; squared distance: 8.5; equally close: 1. I can trace it using this mechanism: Compare a new example with group centroids calculated only from teaching rows. My result supports a claim about these supplied cases. It does not establish that the same result holds outside them.” Look for an accurate trace, the actual settings and a bounded claim; accept equivalent oral or visual evidence.
Extending: The learner constructs and reruns a new case, reports whether the first explanation survives, and defends a revised design. Use this concrete challenge: Build equal-sized collections with different group coverage. Test the same new creature on both. Require the original and changed evidence and this boundary: A mean can hide subgroups; equal counts do not guarantee representative examples.
Moderation: first assess independently against each lesson criterion. Compare the exact trace or artefact that led to your judgement. Resolve differences using evidence, not polished language. Keep each learner's individual explanation even when the artefact was produced in a group.