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.


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2 months ago

Machines of the future student pack

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

Workshop 3: Teach a

Workshop 3: Teach a machine Instructions In this workshop you will experiment with machine learning using a range of different AI powered tools. 1. Open Quick Draw: quickdraw.withgoogle.com/ 1. Launch the experiment and spend 5 minutes exploring what it can do. 1. Choose another one of the tools below and do the same. • Teachable Machine: teachablemachine.withgoogle.com/ • Teachable Machine (demo version): teachablemachine.withgoogle.com/v1 • Shadow Art: shadowart.withgoogle.com/ • Imaginary Soundscape: www.imaginarysoundscape.net/ • Giorgio Cam: experiments.withgoogle.com/giorgio-cam 4. In your group, for each tool discuss the questions below. • What does the tool do? • What type of data does the tool use (images, audio, text)? • What is the tool programmed to do? • How do you provide data to the tool? • How does the tool use the data you provide? • How does the tool improve over time? • What data does it have? 10

Planning guide Your challenge: Design a household product that uses machine learning Get started Start by brainstorming ideas. Think about some of the different elements of home life that machine learning could help with. • Chores: What repetitive tasks do you or your family do around the house? Is there a way a machine could learn to do that task? Could it be automated? • Security: What security issues do you have in your neighbourhood? Is there a way that data could be used to help? • Communication: Machine learning is already being used in home communication tools, to help people control their heating and security features remotely, to interact with entertainment devices like the TV or playing music. What other ways could machine learning streamline and improve communication with household devices? Still stuck for ideas? Take a look at some of these ideas for inspiration: • Systems that make recommendations, suggesting products or services that you might like. Like Amazon making book recommendations based on your previous choices, or Spotify suggesting songs you might like based on what you have already listened to. • Systems that organise, such as spam filters or search engines. This often works by looking for patterns e.g. words or phrases. When you search for something online the search engine does not only look for the words you input, but it may also look for words that are associated with the words you typed in. • Voice recognition and response such as virtual personal assistants. Like Alexa or Google Home answering questions, setting alarms or carrying out tasks, using loads of data from other people asking the same things, these assistants can improve their ability to successfully identify what is being asked of them and complete it in an appropriate way. TIP! Your product needs to learn rules from examples or experience. Think about whether your idea is really using machine learning. https://royalsociety.org/topics-policy/projects/machine-learning/what-ismachine-learning-infographic/ 11