1
Predict
Students formulate a research question and a hypothesis.
Project Wind
Investigate energy with your own wind turbine.
The Wind Project has students build a wind turbine and use a micro:bit to investigate the electrical energy it generates.
How does it work?
Build a turbine and generate energy yourself
The assignment guides students through the same steps as a small investigation: predict, build, calibrate, measure, compare and conclude. The app runs as a normal website and works with WebSerial in Chrome or Edge. No installation, no student accounts.
2
Measure
The micro:bit sends live data to the browser. The app shows voltage, current, power and energy.
3
Compare
Multiple trials come together in one graph and in a PDF report that students can submit.
The setup
A concrete link between technology and data.
The wind turbine becomes a generator. The micro:bit reads voltage and current, after which the browser calculates how much power and energy the setup produces.
- Works with a micro:bit and a simple measurement setup.
- Has a demo mode for explanation, preparation or devices without hardware.
- Uses local libraries for charts and PDF export.
What will you learn?
STEaM in one compact assignment.
Measuring with sensors
Students see how the turbine generates energy, how the micro:bit measures and how that data is visualised.
Voltage and current
We visualise both concepts through graphs and connect them to a familiar case: charging your smartphone.
Power and energy
The app makes visible what P = V * I en E = som(P * dt) mean.
Investigating
Students compare trials and write a conclusion based on data.
- Measurements
- Voltage, current, power and energy
- Practice
- Demo mode available
- Report
- Graph and trials in a PDF
Place in the learning path
From platforms to projects.
The platforms build computational thinking. The projects use that thinking in a cross-curricular assignment in which students build, measure, test and conclude.
Platforms
STEaM projects
Smart Plants
Arduino, soil moisture, calibration and data.
Robotic hand
Arduino, sensors, servos and movement.
Wind
Micro:bit, energy, voltage and current.
Smart bin
AI, image recognition, data and microcontrollers.
Self-learning games
Getting started with machine learning
Particulate matter
Arduino, air quality, data and poster session.
For teachers
Ready to use in class.
The assignment is made for students around 13-14 years old. The interface helps them step by step without pulling attention away from the physical wind turbine.
- Suitable for short research moments within Computer Science.
- Usable with real hardware or as a whole-class demo.
- PDF export bundles hypothesis, trials, graph, conclusion and reflection.
- Adaptable by teachers comfortable with files and GitHub Pages.
Privacy and licence
Privacy
This project does not use its own analytics or advertising cookies. Input, progress and measurement data are processed in the browser and may be stored locally on the device. A download or export occurs only when the user initiates it. Connected hardware is accessed only after permission is given in the browser. GitHub Pages logs visitors’ IP addresses for security purposes; external services are governed by their provider’s privacy policy.
License
Original project material by Robbe Wulgaert may be used, copied and adapted free of charge for education, provided that ‘Robbe Wulgaert – AI in de Klas’ is clearly displayed, where reasonably practical with a link to the original project. Commercial sale, paid distribution, sublicensing or repackaging as a commercial product is not permitted without prior written permission. Other uses outside education also require prior written permission. Third-party components remain subject to their own licences.
Open the research platform.
Start measuring, test the demo mode or let students compare and report their trials.