Discovery challenges (ages 10-14)


Typically completed by 10-14 year olds, students work collaboratively on a five hour project or challenge in self-managed groups. During the project, they use a CREST Discovery passport to record and reflect on their work. Afterwards, students communicate their findings as a group presentation.

Each pack provides teaching guides, kit lists, example timetables and suggested starter activities to help you run your day. Find out more about CREST Discovery Awards.

There are more CREST approved resources that have been developed by our partners and providers specific to your region.


To browse the packs, click the buttons below or scroll down.
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3 months ago

Machines of the future student pack

  • Text
  • Workshop
  • Improve
  • Films
  • Patterns
  • Netflix
  • Examples
  • Output
  • Flowchart
  • Bees
  • Threes

Workshop 2: Machine

Workshop 2: Machine learning now Case study 1 - Netflix 1. At the beginning, the machine has no idea what you might want to watch. You choose a film. It stores information about the choices you make, the content of the films and the choices other people make. (Input) 2. When you select a film, the machine tracks what you watched and looks for similar films or films other people have watched to make recommendations. (Algorithm) 3. Netflix suggests films you might like to watch. (Output) 4. You can either choose to watch the recommended films or ignore them. (Test) 5. If you choose to watch the recommended films, it shows the system is working and Netflix will continue to suggest these kinds of films to you. (Feedback) 6

Workshop 2: Machine learning now Basic flowchart – to be used with case studies 2 and 3 1. Data (input): What is the content of the data? E.g. pictures of bees and threes from the video you watched at the beginning Where does the data come from? Place card here 4. Test How does the machine know how well it has done? E.g. Checks if the picture has been correctly sorted by comparing its response to the responses given by a human/humans. 2. Algorithm What does the machine or system do with the data? E.g. ‘Look’ for patterns in all the photos of threes and all the photos of bees Place card here Place card here 3. Output What output does the machine produce? E.g. Sorts each photo into either bee or three Place card here 5. Feedback How does the machine use the results to improve its performance? E.g. ‘Remembers’ which answers were correct and incorrect and uses this to improve pattern recognition and identify future pictures more accurately Place card here 7