This glossary will help you teach the BBC micro:bit playground survey with confidence. From artificial intelligence to ecosystems, we've got you covered!
We have collated all the key terms specific to the playground survey. A broader Computing/ICT glossary for educators is available from our partners at the Micro:bit Educational Foundation here.
Please be aware that some of the links in this article will take you away from the BBC.
Accelerometer: a sensor that detects movement.
Area: the amount of space taken up by a two-dimensional shape.
Artificial intelligence: the ability of computer software designed by people to perform tasks that usually need human intelligence.
Biodiversity: a measure of how many different species live in an ecosystem.
Code: instructions written in a way that a computer can understand.
Data: information collected for use elsewhere.
Ecosystem: a community of animals, plants and microorganisms and their habitat.
Habitat: the places where plants, animals and other living things exist.
Irregular shape: a shape whose sides and angles are not all the same size.
Machine learning: the application of AI technology where computer software is designed to perform a task quickly and reliably, having been trained by examples of data provided by humans. This training process can be described as ‘learning’ and this is why we use the term ‘machine learning’.
Machine learning model: a set of rules developed by a machine learning system to categorise data.
Natural material: a material obtained from a living thing, e.g. wool, wood or cotton.
Program: a set of instructions written in code that performs a given task.
Regular shape: a shape whose lengths, sides and inside angles are equal.
Software: a set of programs used to perform a task, which is usually more complex than a single program.
Synthetic material: a material that is man-made, e.g. plastic, glass or nylon.
Temperature: a measure of how hot something is.
Thermometer: a device that measures temperature.
Training a machine learning model: providing samples of data categorised and labelled by humans to help machine learning software to build a model.
Testing a machine learning model: evaluating a machine learning model against labelled or known data to see if it is correctly categorising that labelled or known data. If it is not correct, it may need further training data.
Variable: a container for storing data which can be accessed and updated while a program is running.
More from the BBC micro:bit playground survey
BBC micro:bit playground survey
Discover seven cross-curricular activities to find out more about your playground.
Teacher's guide and resources
Find out everything you need to know about the playground survey and download all of the learning resources available.
