S1.2 Technology
- Syllabus
- First assessment 2025
- Topic
- S1.2
- Level
- HL
Choose Data Collection Tools
Choose technology for the data needed
Name the variable, required range, resolution and sampling interval before choosing a tool. The technology must produce data that can answer the investigation question; a convenient output is not automatically suitable.
| Collection technology | What it provides | Essential check |
|---|---|---|
| Sensor/data logger | Repeated numerical measurements, often at fixed intervals | Calibration, range, resolution and sampling rate |
| Database | Existing observations selected from stored records | Field definitions, units, provenance and selection criteria |
| Model or simulation | Generated results under chosen rules and parameters | Assumptions and parameter range; output is model data, not direct observation |
| Video/image analysis | Position, time, angle or intensity extracted frame/pixel by frame/pixel | Scale, frame rate, viewpoint, tracking reference and pixel resolution |
Example — image sensing
A CCD converts light arriving at pixels into electrical signals and then numerical pixel values. Smaller pixels can improve spatial resolution, while quantum efficiency limits the fraction of incident photons detected. Record these limits before interpreting intensity or position.
Keep observation and model separate
Sensors, databases and images record or encode observations; simulations generate consequences of assumptions. Either can be useful, but they are not interchangeable evidence.
S1.2.1 Exam Analysis
Assessment in practice
2–3 marksHow it is assessed
Questions describe how CCD pixels create a digital image or how signals are multiplexed along a channel.
Command terms
Describe / Outline
What earns marks
Describe a sequence: charge/signal at pixels is read and encoded, or data are divided into time slots, transmitted sequentially and recombined.
Watch for
Saying the image is stored without explaining pixel readout or describing multiplexing as simultaneous transmission.
Process Data with Models
Use a spreadsheet deliberately
Keep raw data separate from calculated columns. Use formulas to transform variables, propagate units and uncertainties, and make the calculation reproducible rather than replacing the reasoning with a cell output.
Choose a graph that tests the model
Plot the variables suggested by the relationship. A straight line, its gradient and intercept can reveal the constant, proportionality or systematic offset.
Worked processing route — test a nonlinear model
If a model predicts y=kx2, keep the raw x and y columns, create a spreadsheet column for x2, then plot y against x2. A straight-line model with intercept consistent with the physical expectation supports the proposed form; the gradient estimates k with the plotted units.
State model assumptions
A simulation explores consequences of chosen assumptions; it does not independently validate them. Compare simulated and measured outputs and identify which parameters were held fixed.
Keep the process reproducible
Label axes and units, preserve formulas rather than pasted answers, and state model settings. A graph or trendline does not explain itself: connect its gradient, intercept and deviations to the physical relationship being tested.
Retrieve Data Technology
Collect and encode
Choose a suitable sensor or image/video method, sample the signal and record its resolution and limits.
Process transparently
Keep raw data, calculated columns and model assumptions visible; use graphs and simulations to test relationships, not to hide uncertainty.