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 teacher pack

  • Text
  • Activities
  • Timings
  • Feedback
  • Teams
  • Powerpoint
  • Develop
  • Workshops
  • Workshop
  • Crest
  • Examples

Starter activity Starter

Starter activity Starter Activity Objective Understand what machine learning is and identify examples of machine learning in everyday life. Preparation Slide 6 of the PowerPoint shows two images designed to trigger students to think of examples of machine learning in their own lives. More examples are given on page 11 of this pack. The most relevant examples are likely to be different depending on the age and cohort of students. You may want to substitute your own images into the slide or print some out. Timing 20 minutes 1. Introduction Use slides 1-4 to introduce the day and the challenge. Show slide 5 - What is machine learning? Discuss in pairs and then feedback ideas as a whole class. Summarise by explaining that: “Machine learning is a technology that allows computers to learn directly from examples and experience in the form of data. “Machine learning systems are set a task and given a large amount of data to use as examples of how this task can be achieved or from which to detect patterns. The system then learns how best to achieve the desired output.” 10 2. In pairs or small groups Ask students to share examples, from their own lives, that they think might use machine learning. They might also have family members who have experiences of using machine learning. Use the table on page 11 of this pack as well as the images on slide 6 in the PowerPoint to help prompt if they are short of ideas. 3. Discuss Ask students to share some of their ideas, stories and experiences with the class and discuss if and how machine learning is being used. Use the questions on page 12 of this pack to help. You might have to look up some of the answers later as it’s not always obvious if something uses machine learning or not. Ask students to consider how effective it was. Did it work well? Are there examples of things going wrong? 4. Identify Use the examples shared to draw out different ways machine learning is used in everyday life. You could use the images on slide 6 of the PowerPoint to highlight examples that students may have encountered. For each one ask the students how they think machine learning is utilised. Use the table on page 11 of this pack for reference. 5. Summarise Over the last few years we have seen big developments in the field of machine learning. Machine learning is no longer a thing of the future, many of us now interact with systems using machine learning on a daily basis, such as image and voice recognition on social media and virtual personal assistants.

Everyday life examples of machine learning Example of machine learning in everyday life Facial recognition Chatbots Translate apps Online recommendations Where might students have come across it? Apple tablet and phone security Google, Hey, Alexa or Siri chatbots, interacting with customer service chatbots on websites. Website translators. Reading comments in another language on social media. Communicating with others who speak a different language. Learning a new language. YouTube or Netflix. Shopping online. How is machine learning used? Facial recognition is trained using machine learning by processing and classifying thousands upon thousands of images. Some just use non-machine learning AI to mimic human conversation, but some use machine learning to mimic human conversation that either keeps the human chatting longest or achieves the most customer satisfaction based on feedback data. Have you ever used a chat function on a website? Do you think it was a human or a chatbot? Were you asked to rate your satisfaction after the interaction? A lot of translate apps do not use machine learning, they use a lot of pre-programmed rules instead. But some newer tools are starting to use machine learning to build statistics-based translation systems. By looking at millions of examples of already translated material, machine learning can be used to predict how things will be translated through data rather than by following set rules. Some sites, such as Amazon, use machine learning to provide recommendations and encourage users to buy more products. They start by recommending random things and over time the machine learning tool refines the recommendations by analysing the data about what was bought and what was not. Fraud detection Self driving vehicles Students may have come across it if they have an email account with a SPAM filter. They may have family members who have experienced credit card fraud that was spotted by machine learning technology. Students may have heard about this in the media. Rather than programming fraud detection software to look for particular words or phrases, more modern fraud detection software uses machine learning, giving the tool lots of examples of emails or transactions and asking it to categorise it as fraudulent or not. The tool gradually gets better at identifying which emails or transactions are fraudulent or not, although we don’t really know what they are looking for! Self driving cars use machine learning, rather than being programmed to follow the rules of driving. Instead, the software is fed millions of files of videos and images of driving that is good and driving that is bad. Gradually, this improves the way the car drives itself and how it reacts to situations. 11