Patterns and Classification
Humans are great at spotting patterns; computers can learn to spot patterns too, but they need lots of examples; sorting and classification activities as the basis of machine learning
Lesson
Set out your lesson
“Patterns and Classification means practising the idea at the table with a simple screen-free or sandbox check. We will demo once, then your child tries.”
Bring to the table
- Two short recommendation cards or screenshot printouts
- Pencil for compare notes
Extra for the adult
Worked example. A feed keeps showing football because past clicks were football — that can hide other topics (filter bubble).
Optional challenge. For Patterns and Classification, add one harder example after success.
What to notice. Keep one parent note: examples tried; results; support used. This session samples Patterns and Classification, not mastery.
Share and support
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Teaching approaches
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1 approach
Patterns and Classification means practising the idea at the table with a simple screen-free or sandbox check. We will demo once, then your child tries.
Materials: Two short recommendation cards or screenshot printouts; Pencil for compare notes. 1. Say the goal of Patterns and Classification in plain words once. 2. Adult shows one AI suggestion and asks what data might have caused it. 3. Child reviews three AI examples and names one input clue and one possible bias or limit each. 4. Fresh check: one fresh AI example with a limit. Record and stop. Done: three AI examples with an input clue and a limit note. Proof: parent note of examples and results. Stop: End after the fresh check; keep optional challenge for another day.
Family records and proof options
Keep a record of your learning
Save photos, notes, and attendance. Export a proof packet for compliance or portfolio reviews.
Practice this concept
Start practicePatterns and Classification practice
Practice key ideas from Patterns and Classification.
Free · no login · no ads · Practice answers and completion are not saved. Parent observation remains the lesson record.
Learning resources
Extra practice links — they do not replace Gakuva’s teaching approach.
Code.org · Practice link — not the full lesson
AI for Oceans
Before you open · source notes
Train and test a classifier on labeled examples—direct practice with patterns and classification (and what happens with new items).
Free · no login required to play · account optional to save · no ads observed
Before you openSave a screenshot of training labels and a test result, plus a parent note of one correct and one confused classification.
Opening this link does not save learning or assess mastery.
Source, access and review notes
Unknown — link only
Google Creative Lab · Practice link — not the full lesson
Teachable Machine · Image
Before you open · source notes
Adult-guided tool: create two or more image classes, train a quick model, and test new examples. Works best with a webcam and supervision.
Free · no login · no ads observed · webcam optional
Before you openSave a screenshot of at least two classes with training samples and one live test prediction.
Opening this link does not save learning or assess mastery.
Source, access and review notes
Unknown — link only
Check understanding
- Sort a set of items into categories and explain the rules they used
- Explain that computers learn patterns by looking at many examples
- Describe why a computer needs more examples than a human to learn the same pattern
“If you showed your child pictures of cats and dogs, could they explain how a computer could learn to tell them apart — and why it would need many more pictures than a person would?”
Curriculum record
- Type
- Conceptual
- Subject
- Computing
- Domain
- Artificial Intelligence
- Age range
- Ages 7–9