Bronze Awards are typically completed by students aged 11+. They complete a ten-hour project which is a perfect introduction to STEM project work. Over the course of the project, teams of students design their own investigation, record their findings, and reflect on their learnings. This process gives students a taste of what it is like to be a scientist or engineer in the real-world.
Silver Awards are typically completed by students aged 14+ over thirty hours. Project work at Silver level is designed to stretch your students and enrich their STEM studies. Students direct the project, determining the project’s aim and how they will achieve it. They carry out the project, record and analyse their results and reflect on the project and their learnings. All Silver projects are assessed by CREST assessors via our online platform.
Gold Awards are typically completed by students aged 16+ over seventy hours. Students’ projects are self-directed, longer term and immerse them in real research. At this level, we recommend students work with a mentor from their chosen STEM field of study. All Gold projects are assessed by CREST assessors via our online platform. There are more CREST approved resources that have been developed by our partners and providers specific to your region.
Find out how to build practical CREST projects into secondary science lessons using our free teacher guidance pack. Supporting this guidance are easy-to-use, free-to-download mapping workbooks, which match individual Bronze, Silver and Gold CREST Award projects with each area of the secondary science curricula for England, Wales, Scotland and Northern Ireland. You can download and save your own copy of the relevant mapping workbook via the following links:
To browse the briefs, click the buttons below or scroll down.
Teacher guide AI agriculture The latest developments in machine learning for farming include: autonomous robots for harvesting crops; the use of drones, software based tech, and algorithms to capture and process data about crop and soil health; and, machine learning models to track and predict environmental impacts on crop yield such as weather changes. In this project students will be challenged to think of innovative ways that image, sound and movement recognition could be used for farming and agriculture, and to then design and make their own simple AI powered tool using the teachable machine app. Prompts • What routine jobs are there on a farm? How does the farmer know when they need doing? • How does a farmer use sight and sound in their job? Think about whether a tool that uses image, sound or movement recognition could help them to do that task. • How will you test whether your tool works? 8
Student brief AI agriculture Factors such as climate change, population growth and food security concerns have propelled the agriculture industry into seeking more innovative approaches to protecting and improving crop yield. The latest developments in machine learning for farming include: autonomous robots for harvesting crops; the use of drones, software based tech, and algorithms to capture and process data about crop and soil health; and, machine learning models to track and predict environmental impacts on crop yield. Imagine you are a farmer. You have heard about some new technologies that other farmers are using. One farm has started using moisture sensors, which connect to an app to tell you when your crops need watering, to help reduce water usage. Another has agricultural drones, with near infrared view, to spot blight in a few plants before it spreads. You want to come up with a new way that farming tech could help make your farm more efficient. Design a machine learning powered tool to help improve farming and agriculture. Getting started In this project you will design an innovative way that machine learning could be useful for farming and agriculture. Try out the teachable machine app (see Useful Resources) to help get to grips with how machine learning systems work. Start by reading up on the latest high-tech farming equipment using the ‘Useful resources’ links. Brainstorm ideas for different jobs that farmers need to do regularly, resources that farms use a lot of, and common problems for farms. Maybe you can interview a local farmer. Is there a way that data from images, sound or movement could be used to help do a job a human normally does, reduce the use of expensive resources, or solve a problem? Use your research to come up with a concept that uses machine learning. Things to think about • What kinds of tasks need doing regularly on a farm? How regularly are they done? How do farmers know when to do them? • Farmers have to deal with lots of things that are out of their control - floods, droughts, pests. What measures could they take to protect their crops and animals if they could better predict these events? • What types of things do farmers need to predict? How could data help inform these predictions? • Do your ideas use machine learning? Do they learn from examples, data, and experience? Useful resources • Teachable machine tool https://teachablemachine.withgoogle.com/ • Watch this video about high tech farming https://www.bbc.com/reel/video/p07dgym k/the-high-tech-farming-revolution • Follow the food https://www.bbc.com/future/bespoke/follo w-the-food/ • How high tech is transforming farming https://www.nytimes.com/2019/09/06/busi ness/farming-technology-agriculture.html Health and safety To avoid any accidents, make sure you stick to the following health and safety guidelines before getting started: • Find out if any of the materials, equipment or methods are hazardous using http://science.cleapss.org.uk/Resources/Stu dent-Safety-Sheets/ to assess the risks (think about what could go wrong and how serious it might be). • Decide what you need to do to reduce any risks (such as wearing personal protective equipment, knowing how to deal with emergencies and so on). • Make sure there is plenty of space to work. • Clear up slip or trip hazards promptly. • Make sure your teacher agrees with your plan and risk assessment. 9