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.
2 years ago

Machines of the future teacher pack

  • Text
  • Activities
  • Timings
  • Feedback
  • Teams
  • Powerpoint
  • Develop
  • Workshops
  • Workshop
  • Crest
  • Examples
This resource is published under an Attribution - non-commercial - no derivatives 4.0 International creative commons licence (

Step-by-step guide

Step-by-step guide Pre-project preparation 1. Read through the background information in the pack, and explore this infographic from the Royal Society to familiarise yourself with the topic. 2. It’s worth having a quick play around with: • Quick Draw, • Teachable Machine, • Teachable Machine (demo version), • Shadow Art, • Imaginary Soundscape and • Giorgio Cam. These are tools that students will explore in Workshop 3. You might want to ask them to just look at two or three of these. 3. Download the PowerPoint and print out copies of the CREST Discovery Passport and workshop handouts. Follow the printing instructions on page 5 and adapt for your groupings. 4. Divide the class up into groups of 3-6 students. There are six different roles that can be doubled up for smaller groups. Starter (20min) 1. Use the PowerPoint and instructions on page 10 to introduce machine learning. 2. Introduce CREST Discovery Awards and handout CREST Discovery Passports to the students. Video (10min) Watch the video on slide 7 of the PowerPoint. Use the following prompt questions to recap and check the students’ understanding. Check answers in slide notes: • What did you learn that you didn’t already know? • How does the machine know what to do? • How does the machine know which images are bees and which are threes? • How does the machine improve? This video is referenced in the Workshop 2 student handouts. Quiz (10min) Use the quiz on slide 9. Read out each question and take ideas from the class about whether or not the product in question uses machine learning. See if the class can figure out which tools exists, which ones use machine learning, and which ones are completely fictional. Take a show of hands to decide whether to select ‘yes’ or ‘no’ and then reveal and read out the answer. Workshops These three interactive workshops will develop students’ understanding of machine learning. Activities for the workshops are designed to be student led, with hands-off teacher supervision. Split the class into three groups and set up a rotation for the three 30 minute workshops. Give out the workshop handouts and materials. Put up timings on the board. Workshop 1 – Would you trust a machine?: students will sort different potential machine learning jobs based on their usefulness and how much they would trust a machine to do the job. If possible, print the sheet in A3 to give students more room. Workshop 2 – Machine learning now: students will look at video case studies, investigating how machine learning works in a real-world context, how different data sources are used in AI systems, and illustrating how these tools use machine learning. All students should complete the Netflix activity. You then have the option of handing out the basic flowchart (simpler) or the flow diagram (more complex) for your students to use with case studies 2 and 3 depending on their age and ability. 8

Step-by-step guide Workshop 3 - Teach a machine: in their groups, students experiment with machine learning using a range of different AI powered tools. Students will explore how machine learning uses examples, rather than instructions, to make decisions, and how the more examples (or data) we train the machine with and the more varied these examples, the better it will be. 1. Whilst students are completing the activities, drop in on the sessions and use prompt questions to guide any groups that are struggling. 2. After the students have completed all three workshops, bring the class back together again and discuss each in turn. Reveal the answers for workshop 2 on the PowerPoint. Research and Planning Students work in their teams to research ideas and start to develop their own concept for a machine learning tool. 1. Split students into groups of 3–6. Give each group a Planning handout (found on page 9 of the Student Pack), a team roles sheet, a copy of the Idea Development handout (found on page 10 of the Student Pack) and an A3 copy of the idea sheet (separate). You may want to allow them time to research online in which case each group will need an internet connected device. 2. Divide up the roles between students in each group using the descriptions on the PowerPoint: Project Manager, Software Lead, Research Lead, Risk Lead, Design Lead, Marketing Lead. If the groups are smaller they can double up the roles. 3. Support the groups to identify problems, generate ideas and carry out relevant research if they have online access. 4. Encourage students to consider their idea and ask, 'Is it machine learning?' design of the machine learning tool. The teams will work together to develop their concept, draw a scale model, and start to think about marketing considerations for their product. 1. Go over the design outputs slide on the PowerPoint and bring up the prompt questions to guide the students in their designs and presentations. 2. Hand out poster making materials to the teams. 3. Encourage the teams to draw some draft ideas before creating their design. 4. Ask questions to help students develop their ideas further. Prompt them to identify the different machine learning elements in their design: data, algorithm, output, feedback, improvement. 5. Once students have completed their design, they will need to put together all their work into a 5 minute presentation format. Encourage them to think about who will do the explaining during the different elements of their presentation. Encourage each student to present and discuss what their role in the project was. Presentations Allow the students time to finalise their 5 minute presentations. Ask each group to deliver their presentation. Allow time for yourself and the rest of the class to provide constructive feedback and have a chance to ask questions. Plenary Time for students to reflect on their learning and complete their CREST Discovery passport. Design This section focuses on a more detailed 9


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