Piako Gliding Club, Waharoa Airfield, Waikato. Take State Highway 27 and turn off toward Matamata. The airfield is signposted from the highway. Full details and airfield photographs at glidingmatamata.co.nz.
On-airfield cabins operated by the Piako Gliding Club. Details at msc.gliding.co.nz/accommodation. Overnight stay allows for an evening simulator session and a full day of flying on Monday.
This programme is delivered by Dr Paul Lowe as a Field-Based STEM facilitator resource. Contact Field-Based STEM to arrange. Transport to and from Waharoa is by private vehicles; parent volunteers are welcome and useful for logistics.
Staff to student ratio below 1:6, not including two qualified Piako Gliding Club flight instructors and two flight assistants. Typical group size: approximately 23 students.
A glider converts between gravitational potential energy and kinetic energy throughout its flight. Every metre of altitude is stored PE. Every knot of airspeed is KE. Drag bleeds energy continuously from both. The pilot's task is to manage these conversions to remain airborne as long as possible.
The winch provides a brief, intense energy input: the cable accelerates the glider to flying speed within seconds and the aircraft climbs steeply as KE converts to altitude. The cable releases automatically at the top of the climb. Students consistently describe the winch launch as the most physically arresting moment of the programme.
A thermal is a column of rising warm air. A glider entering a thermal gains altitude without any mechanical energy input. The pilot detects thermals using the variometer: an instrument measuring rate of altitude change. A positive reading means the glider is climbing faster than it is sinking through the air mass itself.
Glide ratio is the horizontal distance covered per unit of altitude lost. A ratio of 30:1 means 30 metres forward for every metre of descent. Higher ratio equals greater aerodynamic efficiency. Glide ratio can be calculated directly from IGC altitude and position data.
The climb rate colour scale on a SeeYou IGC track (orange climbing, blue descending) mirrors exactly what the cockpit variometer was reading during each second of the flight. The IGC file is the variometer's record.
Arrive 4.30pm Sunday. Check in at the flight lounge. Settle into the on-airfield cabins. Brief walkthrough of the airfield layout, the winch position, and the safety perimeter rules before anyone enters the airfield area.
5–6pm: Piako Gliding Club instructors introduce the aircraft, the winch system, the day's programme, and what to expect in the cockpit. Students bring their pre-flight questions.
Students organise and fund their own dinner in Matamata. This is a deliberate programme feature, not a gap: student agency in logistics is part of the PROBLT framework Dr Lowe uses throughout.
Flight simulator at the club introduces the feel of the controls, the instrument layout, and basic cockpit responses before students are in an actual aircraft.
Monday 8am: Dr Lowe's physics briefing, followed by club instructors guiding students through the pre-flight inspection, control surface checks, and GPS logger activation on each aircraft.
Before anyone goes onto the airfield, the club conducts its required safety briefing. This is not optional. Any student who cannot meet the Safe to Fly criteria does not fly.
Each student flies with a qualified instructor. Launches continue throughout the day. Students waiting observe other flights, complete preliminary IGC observation tasks, and record notes in their flight log for use in the classroom session.
After each flight, the student completes their CAA flight log entry and retrieves their personal IGC GPS data file. The file is typically a few kilobytes: small enough to transfer immediately to a phone or laptop for classroom analysis.
IGC is the standard GPS logger format used in gliding and aviation worldwide. Every flight generates a text file recording latitude, longitude, GPS altitude, and barometric altitude at one-second intervals. A 12-minute flight produces a file of roughly 15 kilobytes.
Duration 12 minutes 24 seconds. Winch launch from Waharoa, climbing turn to the left. Thermal found near Waharoa township: the tight circling pattern is clearly visible in the horizontal track. Maximum altitude 1,759 ft (536 m). A long southeastward glide at positive climb rate (orange track) followed by a return to the airfield in descent (blue track). The altitude profile shows the thermal gain as a distinct plateau above 1,500 ft.
Every student's file tells a different story. The question the data generates is not only "how high did I go?" It is "what decisions did the pilot make, and what does the physics say about whether those decisions produced the best energy outcome?"
Every prompt below is anchored in each student's own IGC flight data. Load the file, read the altitude profile, and identify the flight phases before engaging gen AI. The AI's role is to help the student interrogate what the data shows, not to substitute for reading the data. Where the AI's physics explanation conflicts with the student's own numbers, that conflict is the learning task.
Load your IGC file and describe your altitude profile in plain language: where it climbed steeply, where it held steady, where it dropped. Then ask a gen AI chatbot to explain, in physics terms, what was happening at each of those phases. Compare the AI's explanation to what you felt during the flight.
Ask a gen AI chatbot: "During a glider winch launch, what type of energy is the glider gaining and where does it come from?" Identify the winch launch in your IGC altitude profile and check whether the AI's explanation matches what your data shows.
Ingrid's flight reached 1,759 ft in 12 minutes 24 seconds. Look at your own maximum altitude and flight duration. Ask a gen AI chatbot what factors determine how high a glider climbs during and after a winch launch. Does the AI's answer explain the differences between your flight and Ingrid's?
Find a section of your IGC track where altitude was increasing after the cable released. Ask a gen AI chatbot to explain what a thermal is and why it allows a glider to climb without an engine. Does the explanation match what the climb rate colour on your track was showing at that point?
Use your IGC data to find the altitude gained during the winch launch phase. Calculate the gravitational PE gained (PE = mgh; estimate your mass plus the glider's). Ask a gen AI chatbot to help you estimate the work done against drag during the climb. Where did the energy the winch supplied actually go?
Find a section of your IGC track where the glider was in free glide: descending steadily with no thermal and no winch. Use the altitude loss and horizontal distance covered to calculate your glide ratio. Ask a gen AI chatbot what the theoretical best glide ratio is for the aircraft type you flew. Is your result consistent with that figure?
The climb rate on your IGC track is colour-coded in knots. Convert the peak climb rate from your thermal phase to m/s. Ask a gen AI chatbot what atmospheric conditions produce a thermal of that strength. Is the AI's account physically consistent with your data?
Ask a gen AI chatbot to help you construct an energy flow diagram for your complete flight: from winch attach, through launch, through any thermal, through the glide, to landing. Identify every energy input, every conversion, and every loss. Where does the model hold against your IGC data? Where does it fail?
Your IGC file records altitude at one-second intervals. Derive the climb rate for each second by calculating altitude change over time. Plot this against the recorded climb rate value. Ask a gen AI chatbot to explain why satellite-uncompensated and total-energy-compensated climb rate differ. Is the difference significant enough to affect your analysis?
From your horizontal IGC track, identify the circling pattern where the pilot was working a thermal. Estimate the radius of the circle and the time spent inside the thermal. Ask a gen AI chatbot to explain the atmospheric physics determining thermal width, core strength, and duration. Is the thermal you flew consistent with what the physics predicts?
Evaluate your IGC file as a scientific dataset: GPS sampling rate, altitude resolution, sources of systematic and random error, and the conditions under which the data would and would not support a valid physics investigation. Ask gen AI the same question. Where does its assessment match yours? Where does it miss or underestimate sources of error?
Using the IGC files from all students on the camp as a dataset, design a comparative investigation. Specify the variable you would compare, the measurement method, the expected result based on physics, and what the data would need to show to constitute evidence for or against your hypothesis. Ask gen AI to critique your design. Where is its critique valid?
| Level | Years 9–10 | Years 11–12 | Year 13 |
|---|---|---|---|
| 1 | Student identifies the main phases of their flight in the IGC altitude profile (winch launch, cable release, thermal, glide, landing) and names the energy conversion occurring at each phase. | Student locates key features in their IGC data, assigns physics labels to each phase, and quantifies at least one feature: maximum altitude gained, flight duration, or approximate glide angle during the descent. | Student produces a fully annotated plot of their IGC altitude and climb rate data, labelling every phase with the physics process, quantifying all identified features, and noting where data resolution limits precision. |
| 2 | Student explains in physics terms why the glider gained altitude during the thermal phase and lost altitude during the glide, connecting this to energy conservation: PE and KE, and continuous losses to drag. | Student constructs a qualitative energy account of their complete flight: inputs (winch, thermal), useful conversions (altitude gain), and losses to drag at each phase. Explains why the glider must eventually land regardless of pilot skill. | Student produces a quantitative energy audit from their IGC data: PE gained during the winch launch, work done against drag estimated from glide ratio, losses identified by phase, and an evaluation of whether the measured glide ratio is consistent with the aircraft's published performance. |
| 3 | Student asks gen AI to explain a feature of their IGC data and compares the AI's explanation to what the data actually shows. Notes where the AI's account fits and where it requires judgement the AI cannot supply. | Student uses gen AI to help calculate a physics quantity from their IGC data, then checks the AI's methodology against their own calculation. Documents where the AI's approach is valid, where it introduces unsupported assumptions, and where the actual data produces a different result from the AI's prediction. | Student produces an annotated evaluation of a gen AI energy model of their flight: claims the IGC data supports, claims the data refutes, and claims the data cannot test at available resolution. Draws conclusions about the conditions under which gen AI is a useful tool for physics data analysis. |
| 4 | Student explains what being in the glider added that the IGC file, a video, or a gen AI explanation could not provide: the physical experience of the winch acceleration, the silence at cable release, the bank angle in a thermal, and the body's response to lift and sink. | Student articulates the difference between reading energy conservation in a textbook and being the system in which it was occurring: the variometer as a real-time physics readout, the IGC file as a record of a physical experience that had generated specific predictions before the data was read. | Student reflects on the epistemological status of their personal IGC dataset relative to textbook data and AI-generated models: independently collected, time-stamped, location-specific, and tied to a physical experience that generated testable predictions. Evaluates what this means for how the data can function as scientific evidence. |
| 5 | Student formulates one testable question arising from their IGC data that could be investigated on a return visit: a specific change in conditions or flight strategy, a predicted outcome, and the measurement from a new IGC file that would constitute evidence. | Student designs a follow-up investigation using the full set of student IGC files from the camp as a comparative dataset. Specifies the variable, the measurement method, the expected outcome based on physics, and what the data would need to show to support or refute the hypothesis. | Student produces a written investigation report structured for NCEA Physics assessment, using their IGC file as the primary dataset. Includes research question, method, data analysis, conclusion referenced to physics principles, and evaluation of the dataset's reliability and validity for the investigation. |