What happens when a tiny water flea, an iPhone, and a roomful of collective data become your most powerful revision tool?
My Year 11 Investigating Science students walked into the last lesson of the term knowing the theory - reliability, validity, accuracy. What they needed extra practice in was wrestling with all three concepts at once, with real data that they had collected themselves.
The test subject? Daphnia - freshwater water fleas. Microscopic, ethically unproblematic to test on, and conveniently transparent. You can watch their hearts beat through their exoskeletons. It turns out that's quite useful when you're trying to measure the effect of caffeine on heart rate.
Why This Investigation?
In NSW, Australia, the HSC Investigating Science course is built entirely around the scientific process. It isn't content-heavy - it's more of a thinking course. By the end of the year, students need to apply concepts like reliability, validity, and accuracy with precision, not just define them. I'd revisited these ideas across the term, but I wanted a revision lesson with real-world application: first-hand data, real variables to control, and enough richness in the results to have an actual scientific argument.
Daphnia ticks every box. They're sensitive to water conditions, their heart rate is measurable under the microscope, and because they're invertebrates, they don't fall under the same ethics codes as vertebrate organisms. Not to mention, students find microscope work genuinely interesting and engaging.
Learning Intentions and Success Criteria
Learning Intentions:
- Conduct a practical investigation into the effect of caffeine on heart rate in Daphnia
- Describe features from the practical investigation that improve reliability, validity, and accuracy
Success Criteria:
- I can follow a practical procedure accurately and safely
- I can use technology to conduct an accurate practical investigation
- I can analyse a practical investigation in terms of reliability, validity, and accuracy
How the Lesson Ran
The 50-minute lesson was structured to move from a quick review of key terms to application in the investigation.
- Bell ringer (or ‘Do Now’): Students received the method on a worksheet and immediately identified variables and wrote a hypothesis. They weren't waiting for me - they were thinking from minute one.
- Test subject intro: I introduced Daphnia, walked through why they're useful, and explained the ethical rationale for using invertebrates.
- Lab work: Groups were each assigned a single caffeine concentration and ran four trials. Each group filmed their Daphnia through the microscope using an iPhone, AirDropped the footage to their Mac, then opened it in iMovie to trim a standardised 20-second clip and slow it down for counting heartbeats.
- Data pooling: Groups entered their averaged heart rate data into a shared Keynote graph on the board. As each group contributed, the graph was built in real time.
- Discussion: As a class, we brainstormed factors in the experiment that impacted reliability, validity, and accuracy. Working from the worksheet, students evaluated the investigation across all three criteria and proposed two improvements.
Unexpected Learnings
I expected to guide students through iMovie. I didn't need to. They troubleshot it themselves - and more importantly, they made the methodological call to standardise clip length across every trial without being prompted. This lesson taught me that a good method isn’t always the one you plan - sometimes the class improves it for you.
As the data came in, students observed some unexpected results. Heart rate increased with caffeine concentration - until the highest concentration, where it dropped. Not what the science predicts. This was an opportunity to review how this relates to accuracy. We checked expected values and saw that our results didn’t match, giving us evidence for a source of inaccuracy in our method. That anomaly gave us a concrete, student-generated example of what accuracy actually means: the gap between experimental values and what you'd expect to see in reality. Three concepts I'd spent a term teaching - and suddenly, all of them were alive in data my students had generated themselves. The students could readily apply these core concepts to the new context and were engaged and fascinated by our subject matter. It was refreshing to see pairs of students in awe of what they could see under the microscope.
What I'd Change
First, I'd push for two lessons. A small class means limited repeats, and the reliability analysis would be sharper with more data. Second, I'd replace the manual data entry into Keynote with a shared collaborative Numbers document. Students need to see the raw trial data, not just the averaged result on a graph. Watching the spread of values across trials tells a different story, and a more honest one, than a single plotted point.
Tips for Running This
- Film through the microscope eyepiece by holding the iPhone camera directly over the lens - it takes a moment to find the right position, but once it locks in, the footage is clear.
- Standardise clip length in iMovie across all trials (20 seconds works well) so heart rate counts are directly comparable.
- Ensure the Live Video feature on the Keynote is connected to the iPhone so that you can model the techniques to the class at the start of the lesson. I found it helpful to connect the iPhone to the Mac with the cord rather than rely on wireless.
- Deliberately choose Daphnia of similar sizes across trials - this is a great validity conversation in itself.
- If your data produces an anomaly, resist the urge to explain it away. Let the students argue about it first.
I've attached the student worksheet (in Pages for you to edit) and the Keynote used in this lesson. The Keynote includes the live graph slide where students entered their group data.
Worth noting that this is all possible as the school invested in a set of managed iPhones in light of the ‘No Phone’ policy. In the science lab, iPhone is a powerful and easily accessible data logger that replaces a lot of other expensive specialised tools - perfect for simple measurements of time, light, motion and sound in addition to the camera and video functions.
What are some other contexts in which you can use the iPhone as a scientific instrument for data collection? I’d love to know how other schools use tech tools to make scientific investigations more engaging.
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