Learning Objective
Understand that IoT is essentially automated data collection and logging.
Term 2 Project: The Data Logger
Theory: Data Logging Concept
A strong project moves through four stages: plan, build, test with evidence, and explain.
What Is IoT, Really?
Here is the big picture: IoT is really about automated data collection. A fitness tracker or weather station reads its sensors again and again, stores the readings and looks for patterns. This week you log data by hand, and see why a device that does it 24/7 is so useful.
Understanding Data Logging
Data logging means recording measurements over time. The result is a time-series dataset: a list of values, each tagged with a timestamp (the time it was taken). Log the temperature every 5 minutes for a day and you get 288 readings. A single measurement tells you the current state. Logged data tells the story of how conditions change.
Technical Details: From Measurement to Insight
A good log records three things: the timestamp (when), the sensor value (what was measured) and the sensor ID (which sensor). Your MakerBuddy shows live values but does not save them, yet. Fast changes like motion need frequent readings. Slow changes like daily temperature need fewer.
Real-World Applications
Smart agriculture logs soil moisture to plan watering. Fitness trackers log heart rate, steps and sleep. Predictive maintenance in factories logs machine vibration to spot problems before a machine breaks.
For Teachers
This project pulls together Term 2's sensors. Hand logging is meant to be tedious, so students feel why automation matters before they write rules in Term 3. Have them graph their data, and ask what they would miss logging every 30 minutes instead of every 5.
For Parents
This week your child records temperature, light and motion readings by hand for a whole lesson. They soon find it tedious and easy to get wrong, which shows why smart devices log data automatically, day and night. Turning readings into patterns is what makes technology "smart".
Try It: Be the Data Logger
Interactive demo · no kit needed
Play with a pretend MakerBuddy board and its web page, right here.
Follow the steps one at a time. Got the kit? You can switch to the real board inside.
Hands-On Activity: Manual Data Logging
Activity Duration: Full class period (45-60 minutes)
Record the sensor readings by hand for the whole lesson. Your table becomes a time-series dataset that shows patterns.
Step-by-Step Instructions:
- Form Teams: Work in pairs. One writes in the logbook, the other reads the dashboard. Swap roles halfway.
- Create Your Logbook: Draw a table with columns: Time | Temperature (°C) | Humidity (%) | Light Level | Distance (cm) | Motion (🚶 Motion or 😴 None) | Notes. Add a row for every 5 minutes of the lesson.
- Ensure Sensors Are Connected: With the USB cable unplugged, plug the DHT11 into 25, the PIR into 18 and the HC-SR04 into its HC-SR04 connector (VCC to VCC, GND to GND, Trig to 15, Echo to 2; the LDR is built in). Power up. Check that the DHT11 cards don't show 0.0 and Distance doesn't show 999.0.
- Record Starting Time: Write down the exact time you start. That is the timestamp for row 1.
- Take Initial Readings: Copy every sensor value from the dashboard into row 1. Write the numbers exactly.
- Set a 5-Minute Timer: Use a phone timer or the classroom clock. Every 5 minutes, fill in a new row.
- Continue Throughout Class: Keep logging for the whole lesson (about 10 to 13 rows, depending on class length). Don't skip a row!
- Note Events: In the Notes column, write what happened: "Window opened", "Lights turned off", "Students entered/left". Notes explain odd readings.
- Calculate Totals: At the end, work out the average temperature, the highest and lowest light level, and how many of your rows show 🚶 Motion.
What Students Should Observe:
- Temperature changes slowly and may rise as the room fills up
- Indoor light stays fairly steady unless the blinds are moved
- Humidity moves a little (breathing adds moisture to the air)
- Motion shows when anyone moves in range; Distance changes when something is placed in front of the ultrasonic sensor
- Logging by hand is tedious and easy to forget, so automation would be much better
Challenge
Your Mission: Data Analysis Detective
Turn your raw readings into findings: spot patterns, make a graph and draw conclusions about your classroom!
Challenge 1: Graph Your Data
Draw a line graph of temperature over time:
- X-axis: Time (each 5-minute interval)
- Y-axis: Temperature in °C
- Plot each data point and join them with lines
- Label both axes clearly and give your graph a title
Analysis Questions:
- Did the temperature go up, go down or stay the same?
- Can you tell what caused any temperature changes?
- What was the temperature range (highest - lowest)?
Challenge 2: Pattern Detection
Use your whole table to answer:
- When was it hottest? At what time? What might explain it?
- When was there most movement? Count the rows that show 🚶 Motion. Could people have moved between your readings without you seeing it? What would an automatic logger catch?
- Did light level change? If so, what caused it?
- Temperature vs. Motion correlation: Was it warmer when there was more movement? (More people means more body heat!)
Discussion Questions:
- How often did you forget to log on time? What if this were a critical medical monitor?
- If you logged every 1 minute instead of every 5, how would your workload change? And your graph?
- What patterns might you find over 24 hours? A full week? A full year?
- How could automatic logging stop human mistakes, like wrong numbers or missed rows?
Bonus Challenge: Use today's pattern to predict tomorrow's temperature at this time. Check it tomorrow! Weather forecasts work the same way: past patterns predict the future.
Real-World Connection
Weather stations log data just like you did today, but automatically, every few seconds, 24/7/365. Years of their data help forecasters predict storms, track climate change and give warnings that save lives!
Key Takeaways
✓ Data Logging
Data logging records measurements over time, each with a timestamp. IoT devices do it automatically, thousands of times a day.
✓ Pattern Recognition
Logged data shows patterns, like the room warming up as it fills. Patterns help us explain causes and predict what comes next.
✓ Manual vs Automated
Logging by hand is tedious, error-prone and stops when people leave. Automated IoT logging is continuous, precise and never forgets.
✓ Time-Series Data
Time-series data pairs each value with the time it was taken. It shows whether things are rising or falling, daily cycles and sudden spikes.
Term 2 Complete
Well done — you have completed the Term 2 project.
Learning Support
Use this quick guide to help every learner understand, practice and explain Term 2 Project: The Data Logger.
For students
Try it in three steps
- Say it: explain the main idea in one simple sentence.
- Show it: Work in pairs. Log Temperature, Humidity, Light Level, Distance and Motion from the dashboard every 5 minutes, then compare your log with an automatic one.
- Stretch it: Synthesize sensor knowledge to understand the importance of historical data.
Checkpoint: What was your input, what decision or process happened, and what was the output?
For teachers & parents
Coach without taking over
- Before: ask the learner to predict what will happen and why.
- During: use prompts such as “What changed?” and “What could you test next?” instead of giving the answer.
- After: ask for a 30-second explanation, one piece of evidence and one improvement.
Look for: safe handling, correct vocabulary, a result the learner can demonstrate, and an explanation in their own words.
Did you get it?
Three quick questions. Pick an answer to see whether it is right, and why.
1What is data logging?
2What does logged data show that a single reading cannot?
3Why is automatic logging better than writing readings by hand?