We have progressed past the industrial age and now live in the information age. Your responsibility as part of this task is to contribute to this information — to investigate the use of data and gain insight and clarity into the unknown.
Use a micro:bit to capture real-world data, interpret what it reveals, and present your findings to the class.
This is more than a coding exercise. The goal is for the data to provide information and insight into something that is otherwise difficult to fully understand without it. You'll design an experiment, build and code your measurement system, collect the data, analyse it mathematically, and communicate what you discovered — just like a real scientist, engineer, and data analyst.
Your final presentation will likely be a PowerPoint, but the data is the star. What story does it tell?
This is a STEM assignment — each of the four disciplines plays a role in your project. Below is what each component means for your work.
Investigate the physical phenomenon behind your data. Measuring light? Read about the electromagnetic spectrum, photons, lux vs lumens. Measuring sound? Explore decibels, frequency, the physics of waves. Your science section gives your data context and credibility.
Program the micro:bit to capture data using its built-in sensors — accelerometer, microphone, temperature, light level, and more. You'll configure sampling intervals, log to a CSV file, and think carefully about what data you actually need and how often to collect it.
How you physically set up your experiment matters. Where do you mount the micro:bit? How do you ensure consistency between readings? How do you prevent interference? Engineering is about solving the practical challenges of getting reliable data from the real world.
Graph it. Calculate averages, minimums, maximums, and ranges. Look for patterns and anomalies. Compare two datasets side-by-side. Turn raw numbers into insight — this is where the story emerges. What does the data actually tell you?
The micro:bit v2 has a built-in data logging feature that makes it straightforward to record sensor readings over time.
Full instructions and documentation are available at microbit.org — Data Logging Guide. Read through it carefully before you start coding.
micro:bit v2 — built-in accelerometer, microphone, temperature sensor, light sensor, compass, and Bluetooth.
→ Data Logging GuideThe choice of data collection is completely open-ended — these are just starting points to spark your thinking. The best projects come from questions you genuinely want to answer.
Remember: the best investigation is one where you're genuinely curious about the answer. Talk to your teacher about your idea before you start.
Every submission must include the following six components. Tick each one off as you complete it — your final presentation should clearly address all of them.
Document how your measurement system is physically built and positioned. Show where the micro:bit is mounted, how it's secured, and the environment being measured. A short video walkthrough is even better.
Include the full MakeCode or Python script used to capture your data. It should be readable, with comments explaining what each section does and why you chose your sampling interval.
Include your actual CSV or data table — the numbers straight from the micro:bit. This is the foundation of everything else. Don't edit or filter it; show the data as it was collected.
Turn your data into graphs and charts. Choose the right chart type for your data (line graph for time-series, bar chart for comparisons). Make them clearly labelled with axes, units, and a title.
What does the data tell you? Identify patterns, anomalies, and trends. Calculate key statistics. Answer your original investigation question using evidence from your data. This is the insight — the reason you collected the data in the first place.
Provide scientific context for what you measured. What is the relevant theory? What do experts say? How does your data compare to known benchmarks or published research? Cite your sources.
Your work will be assessed across the four STEM components. The table below describes what each level of achievement looks like.
Work shows early understanding. Steps were taken, but depth, accuracy, or completeness is limited.
Solid understanding demonstrated across most areas. Work is thoughtful, mostly accurate, and clearly communicated.
Exceptional depth, insight, and initiative. Work goes well beyond requirements with real analytical or creative ambition.
| Component | Developing | Consolidating | Extending |
|---|---|---|---|
| S — Science | Basic description of what is being measured. Limited background research. Terminology is used incorrectly or sparingly. | Clear explanation of the phenomenon being measured. Relevant scientific concepts are correctly identified and explained (e.g. decibels, the light spectrum, soil moisture). Sources cited. | In-depth scientific research that connects directly to the data collected. Student can explain why their data behaves the way it does using scientific reasoning. Multiple credible sources synthesised. |
| T — Technology | Code runs but may be copied with little modification. Sensor data is collected but the setup lacks deliberate configuration (e.g. no meaningful sampling interval). | Code is functional and purposefully configured. Appropriate sensor(s) selected. Data is logged at a thoughtful interval with clear column labels. Student can explain what the code does. | Code demonstrates initiative — multiple sensors, error handling, calculated fields, or automation. Student has independently troubleshot and refined their approach. Code is clearly commented. |
| E — Engineering | Basic physical setup with limited thought given to consistency or reliability. Potential sources of interference are not considered. | Measurement system is deliberately designed. Sensor placement is justified. Attempts made to control variables and minimise interference. Setup is repeatable. | Sophisticated physical design that actively controls for variables. Multiple trials or conditions compared. Student has identified and mitigated sources of error and documented their engineering decisions. |
| M — Maths | Raw data presented with minimal analysis. One basic graph or table produced. Limited interpretation of what the numbers mean. | Data visualised clearly in appropriate graph types. Key statistics calculated (mean, min, max, range). Patterns and anomalies identified and discussed. Data linked back to the investigation question. | Multiple visualisations including comparisons between datasets. Statistical analysis goes beyond basics (e.g. rate of change, trend lines, correlation). Anomalies are investigated and explained. Conclusions are fully supported by data. |
| Presentation | Information is conveyed but lacks structure or clarity. Slides are hard to follow or overloaded with text. Data is difficult to read. | Presentation is clear, logically structured, and well-designed. Graphs and visuals are readable. The student can explain their findings and answer questions confidently. | Presentation is compelling and tells a clear story from question to insight. Audience engagement is strong. Student shows genuine enthusiasm and can field in-depth questions, demonstrating deep understanding. |