IA2 — Student Experiment
Weighting: 20%
What is a student experiment?
You modify an experiment (refine, extend or redirect) performed in class to address your own related hypothesis or research question. You then report your findings in a scientific format.
Conditions
- Time: 10 hours class time (not necessarily sequential)
- Mode: Individual (some collaborative elements permitted — see below)
- Response: Written report up to 2000 words, or multimodal presentation up to 11 minutes
What can be done collaboratively?
- Identifying an experiment to modify
- Developing a research question
- Conducting a risk assessment
- Conducting the experiment and collecting data
All other stages must be completed individually — processing data, analysing evidence, evaluating the methodology, and writing the report.
Report structure
Your report should contain:
- Research question — specific, measurable, testable
- Rationale — why this investigation is worth pursuing
- Methodology — reference to the initial experiment, identification and justification of modifications
- Raw and processed data — tables, graphs with correct units, significant figures, and uncertainties
- Analysis — identify trends, patterns and relationships in the processed data
- Conclusion — based on interpretation of the evidence
- Evaluation — discuss reliability, validity, uncertainties and limitations; suggest improvements and extensions
- Reference list
Assessment criteria
| Criterion | Focus |
|---|---|
| Research & Planning | Research question, rationale, methodology modifications |
| Analysing | Trends, patterns, relationships, uncertainties |
| Evaluating | Reliability, validity, limitations, improvements |
| Communicating | Scientific language, conventions, referencing |
Tips for success
Key advice
- Your modification must be justified — explain why you changed the experiment
- Use vertical error bars on graphs where appropriate
- Calculate percentage uncertainty in processed data
- Include minimum and maximum trendlines to determine gradient uncertainty
- Identify specific sources of imprecision and inaccuracy (not generic ones like "human error")
- Suggested improvements should directly address the limitations you identified