Example of dataset used in the pilot
Satelite thermal imaging maps of building heat loss, mapping residential roof space for solar photo-voltaic panels for electricity generation, and monthly household electricity consumption.
We believe our approach is unique because it aims to solve three key challenges to learning data literacy skills:
1. Curating datasets
Few datasets are rich enough to be used for data literacy education. The Urban Data School aims to draw on local datasets gathered from real cities, including Smart City initiatives, and make them useful for students and teachers.
2. Equipping students with the basics
Making sense of data is hard for beginners. The Urban Data School aims to provide students with basic data skills to help them understand the information they are engaging with.
3. Learning in context
The Urban Data School curricula aims to connect datasets to school subjects, such as geography, and locally relevant issues, such as air quality.
Over the last 18 months we have worked with students and teachers from Milton Keynes to develop and test a digital educational platform. This prototype hosts and enables hands-on interaction with datasets, tools and materials. It has provided the minimum resource needed for teachers and children to begin using real-life urban data to teach and acquire data skills.
Feedback from over 100 teachers and children participating has been highly positive. We are currently using these pilot results to improve the tools and platform, with the goal of making Urban Data School a scalable approach to data literacy.
Video clip below was filmed in Wavendon Gate School in 2016