Year 2 · 45 minutes · Classification
How does a machine name a creature it has never seen?
This classifier remembers labelled examples. It measures how far a new creature is from each example using two invented features: antenna length and shell width. It copies the label of the closest example. A label is supplied by a person; it is not hidden inside the creature. This tiny nearest-neighbour model calculates distances but does not understand living things.
Draw four invented creatures with differing antenna lengths and shell widths. Give two the label Dune and two Reef. Place them on a large two-axis floor or desk map. Do not use real animal taxonomy as ground truth.
Compare lengths and count spots using two features. Useful earlier investigations: f-sort Use pairs: one child chooses or points while the other traces or checks. Swap after one case. An adult records the child’s words; drawing and movement are equally valid evidence.
Australian Curriculum Version 9 · Digital Technologies: AC9TDI2K02, AC9TDI2P02. 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.
Students describe antenna and shell before reading labels. Place paper examples on the map.
Ask: “Is Dune a measurement or a name we supplied?”
Listen for: “A name; shell width is a measurement.”
Mark the unknown creature’s nearest example before revealing distances.
Ask: “Which example do you expect the machine to copy?”
Listen for: “The one closest on the map.”
Move the unknown creature using two coordinates. Watch distance lines and the predicted label. Keep training examples fixed and record points on opposite sides of a change.
Ask: “Did the machine see a real creature, or just two numbers?”
Listen for: “Just the numbers we gave it.”
Change the label of the closest training example without moving it or the query.
Ask: “Why did the name change when the features did not?”
Listen for: “The machine copied our changed label.”
Find a wrongly labelled example, repair it and test three fixed query locations. Keep an original prediction snapshot.
Ask: “What evidence supports your repair?”
Listen for: “The label disagreed with the rule we had agreed for our invented creatures.”
Each pair gives one oral explanation supported by a distance trace.
Ask: “Does the closest example guarantee the right label?”
Listen for: “No; the example could be wrong or missing.”
The label lives inside the creature.
At the starting query location, identify the nearest labelled creature before revealing the prediction.
Relabelling the nearest example changes the prediction without changing any creature features. Equal distances also require a tie rule.
Repair one deliberately inconsistent label, then evaluate the same three unknown creatures before and after.
Ask the student to point to the training label responsible for a prediction. If they appeal to the creature’s personality, return to the actual numeric inputs.
Use a one-dimensional antenna line first and tangible rulers. Allow a partner to operate coordinates while the student predicts.
Find a tie and specify a deterministic tie rule; compare copying one neighbour with voting among three.
A repaired training set with three fixed-query prediction traces.
Use only fictional creatures. No classmates, face images or cultural material are classified. Labels describe invented habitats, not worth or ability.
Compute nearest-neighbour votes over a dense query grid on MPS/CUDA. Compare one and three neighbours, and measure how relabelling one training example changes the boundary.
| Criterion | Beginning | Secure | Extending |
|---|---|---|---|
| Representation | Confuses feature and label | Names features and labels separately | Explains what the representation omits |
| Prediction evidence | Guesses a creature name | Identifies the nearest example | Uses distance and relabelling tests to explain a change |
Australian Curriculum Version 9 · Digital Technologies
References: AC9TDI2K02, AC9TDI2P02. Read the current source (checked 2026-09-07).
Evidence to assess: A repaired training set with three fixed-query prediction traces.
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 lengths and count spots using two features. Ask the learner to demonstrate it before choosing the level of support.
Preparation: allow about 10 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.
For young learners, show one picture or case at a time. Accept pointing, movement, a drawing or adult transcription. Read the question aloud; explain the numeric model privately to the adult. Do not assess keyboard speed or independent reading.
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.
Find the closest teaching example using weighted squared distance. Ties use the first supplied example.
Starting parameters: Feature A importance = 1
4 teaching rows; 1 new cases. Ties use supplied order.
| feature A | feature B | chosen | squared distance | equally close |
|---|---|---|---|---|
| 4 | 5 | A | 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: 5; chosen: A; squared distance: 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: 5; chosen: A; squared distance: 5; equally close: 1. I can trace it using this mechanism: Find the closest teaching example using weighted squared distance. Ties use the first supplied example. 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: Make a creature that belongs near a boundary. Ask a partner which feature changed its neighbours. Require the original and changed evidence and this boundary: Changing features or a tie rule can change the label; these groups are invented.
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.