Year 5 · 65 minutes · Feedback loops
Can a recommendation create the popularity it measures?
A recommender chooses what receives exposure. In this model, expected clicks equal exposure multiplied by fixed synthetic click rates. A popularity-only policy uses the resulting clicks to allocate the next round, so an early advantage can grow. Exploration reserves some exposure for less-seen items. These are deterministic expected-value calculations, not measured behaviour from real readers.
Prepare four fictional reading topics with fixed click rates and unequal initial counts. Rehearse reset and single-round stepping so comparisons begin identically.
Describe how collected examples affect a later choice. Useful earlier investigations: y4-balance 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: AC9TDI6P02, AC9TDI6P06. 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.
Allocate counters using previous clicks and calculate expected new clicks.
Ask: “Can an unseen book collect clicks?”
Listen for: “No, it first has to be shown.”
Predict the dominant topic after five rounds with zero exploration.
Ask: “What part of the starting advantage can feed itself?”
Listen for: “More clicks cause more future exposure.”
Advance one round at a time, tracing exposure to clicks to next exposure. Record shares and concentration.
Ask: “Did we change what readers prefer?”
Listen for: “No, the synthetic click rates stayed fixed.”
Inspect the high-click-rate topic with almost no initial exposure.
Ask: “Why did popularity fail to discover this item?”
Listen for: “It barely showed it.”
Reset, reserve an exposure fraction for all topics and replay five rounds. Compare discovery and concentration, acknowledging any short-term click cost.
Ask: “What did exploration make possible?”
Listen for: “The less-seen topic could gather evidence.”
Explain the feedback loop and policy choice with two saved runs.
Ask: “Would clicks alone tell us whether readers benefited?”
Listen for: “No, clicks are only one behaviour.”
Popularity only reflects preference.
Predict which topic gains exposure over five popularity-only rounds with all click rates fixed.
A high-interest topic stays hidden because it starts with almost no exposure, so the system gathers little evidence about it.
Choose an exploration fraction and compare five identical-start rounds with a popularity-only baseline.
Check learners reset both counts and round number. Ask them to point to the feedback arrow and explain its causal role.
Use two topics and physical counters before running four-topic rounds. Provide multiplication results if needed.
Add a decaying memory of old clicks and compare how quickly a newly useful topic becomes visible.
A recommendation policy note with exposure histories and a feedback diagram.
No real student browsing, clicks or reading histories are tracked. Expected clicks are synthetic model outputs and must not be described as observations.
Simulate many initial exposure allocations with fixed fictional click rates. Change exploration and compare hidden-topic exposure and concentration across twenty rounds.
| Criterion | Beginning | Secure | Extending |
|---|---|---|---|
| Loop explanation | Describes popularity as fixed | Connects exposure and later counts | Explains an initial-condition effect |
| Controlled comparison | Compares different starting rounds | Resets and compares equal-length runs | Reports discovery and short-term trade-offs |
Australian Curriculum Version 9 · Digital Technologies
References: AC9TDI6P02, AC9TDI6P06. Read the current source (checked 2026-09-07).
Evidence to assess: A recommendation policy note with exposure histories and a feedback diagram.
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: Describe how collected examples affect a later choice. 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.
Each round adds ten exposures. Mix proportional popularity with a uniform exploration share, then feed those exposures into the next round.
Starting parameters: Rounds = 5, Exploration share = 0.2
3 cases calculated from your supplied inputs. Compare the evidence with your prediction.
| topic | initial | final |
|---|---|---|
| Reef | 8 | 38.31205 |
| Bush | 2 | 13.418442 |
| Sky | 1 | 9.269508 |
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 topic: Reef; initial: 8; final: 38.31205.” 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, topic: Reef; initial: 8; final: 38.31205. I can trace it using this mechanism: Each round adds ten exposures. Mix proportional popularity with a uniform exploration share, then feed those exposures into the next round. 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: Compare two exposure policies from the same starting counts for five rounds. Require the original and changed evidence and this boundary: This is a deterministic feedback model; exposure is not a measured human preference.
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.