IB Chemistry IA: Ideas and How to Score High

In fifteen years of marking these, the strongest scripts almost always come from a genuine 'huh, that's odd' moment in class — not Google. Reliable starting points:

1. A practical you already did, pushed one variable further (concentration → ionic strength; temperature → activation energy via an Arrhenius plot)
2. A household chemistry question you can quantify (vitamin C degradation in juice over time; antacid effectiveness by back-titration)
3. A cross-subject idea (electrochemistry of a homemade cell; kinetics of an enzyme-catalysed reaction)

Quick tip: an idea you can't actually execute with your school's equipment scores worse than a modest one you nail cleanly.

Moderation doesn't remark your IA from scratch — moderators check whether your teacher's marks are consistent with the descriptors, then adjust the whole school's marks up or down if there's a systematic gap. That's why the exact wording your teacher uses in feedback matters more than you'd think.

See the criteria breakdown above for the marks split. One thing students miss: Personal Engagement is only 2 marks but it's assessed through how you write the whole report, not a separate section — a first-person justification of why you chose the topic in your introduction usually covers it.

The other 80% splits across three exam papers: Paper 1 (multiple choice plus data-based questions), Paper 2 (short answer and extended response), and Paper 3 (practical-based questions across the options and the whole syllabus). A weak IA is recoverable through strong exam papers — but it's the one component students have most control over well before exam season.

Worked example — before and after:

• Weak: "How does temperature affect reaction rate?"
• Strong: "What is the effect of temperature (15–55°C, 5°C intervals) on the rate constant for the reaction between sodium thiosulfate and hydrochloric acid, determined using the disappearing-cross method?"

Steps to get there:

1. Pick a reaction or system you can run repeatedly with school equipment
2. Choose one variable to change deliberately (independent)
3. Choose one measurable outcome (dependent)
4. Add a specific range and interval for the independent variable
5. Name the method you'll use to measure the dependent variable

Five topic families that consistently score well when executed carefully:

1. Kinetics — rate law determination via initial rates or half-life
2. Equilibrium — Le Chatelier shifts measured quantitatively (e.g., colorimetrically)
3. Titrimetric analysis — determining an unknown concentration or purity
4. Electrochemistry — Nernst-equation-based cell voltage investigations
5. Thermochemistry — enthalpy of reaction via calorimetry, with Hess's law comparison

Common mistake: picking a topic because it sounds impressive rather than because you can generate at least five repeat trials per condition.

If your school's safety policy or equipment genuinely can't support the reaction you want, a well-justified secondary-data investigation (analysing published spectroscopic or thermodynamic datasets, for instance) is allowed — but check with your supervisor first, because the Exploration criterion is written with primary data collection in mind and a database approach needs extra justification to score well.

Topics that need extra care and a documented risk assessment rather than outright avoidance: concentrated acid/base titrations, heavy-metal salt solutions, exothermic reactions producing significant heat or gas, and anything using a Bunsen burner near flammable organics. Naming the specific hazard and control measure (not just "wear goggles") is what separates a top-band Exploration mark from a middling one.

Worked example — uncertainty propagation: A burette with ±0.05 cm³ uncertainty delivers 12.50 cm³ in a titration. Percentage uncertainty = (0.05 ÷ 12.50) × 100 = 0.4%. A 7-band script carries this through the mole calculation to a final percentage uncertainty on molar concentration, then discusses whether the experimental value falls within that margin of the literature value.

Common mistake: running each condition once and calling the leftover time "analysis." Fix it before you start:

1. Plan for at least three repeat trials per data point, five if the reaction is fast or noisy
2. Decide your uncertainty method (range, standard deviation, or instrument uncertainty) before collecting data
3. Book double the lab time you think you need — repeats always take longer than planned

Quick tip: put raw data tables and repeated calculations in an appendix rather than the main body if your school allows it — it keeps the core report readable and stops Communication marks slipping because a marker can't find your actual conclusion among the tables.

A realistic rough timeline:

1. Weeks 1–2: finalise research question, run a small pilot trial
2. Weeks 3–4: full data collection (repeats included)
3. Weeks 5–6: analysis, graphs, uncertainty calculations
4. Weeks 7–8: write Evaluation and Communication draft, get supervisor feedback
5. Final week: proofread against the criteria descriptors, not just for spelling

You don't need to know the chemistry to help. Three things worth asking:

1. "Have you shown your supervisor the research question yet?"
2. "Do you have a backup plan if the first trial doesn't work?"
3. "Have you checked the criteria descriptors, or just the word count?"

If the underlying content — equilibrium, kinetics, titration calculations — is shaky, working through the relevant Revision Notes and Topical Worksheets on revisionprep.com alongside the IA can shore up the theory an examiner expects to see referenced in the Evaluation section.