This is a sample report
Illustrative feedback on a made-up Mathematics exploration — this is what Wenzify gives you for your own IA.
Modelling the spread of a rumour through a school using logistic growth
A genuinely engaged exploration built on the student's own data, with correct core mathematics. The marks are held back by notation that isn't always defined, a graph missing units and a measure of fit, and a reflection that identifies a real limitation without going on to refine the model.
~2400 words · Typically 12–20 pages; length is judged through conciseness, not a word count.Estimated
12–16
out of 20
DecentDraft history
How this commentary developed, draft by draft — a record you can show your teacher.
Focus on these first
The highest-impact changes, in order.
Finish the graph and the notation
Label axes with units, show the fitted parameters and a measure of fit, and define every symbol on first use.
Mathematical communication (Criterion B)
Show the parameter-fitting
Demonstrate how r, K and A were found (e.g. linearisation or least squares) rather than stating the values.
Use of mathematics (Criterion E)
Turn the limitation into a refinement
You spotted that a constant rate is unrealistic — now propose and justify a better model.
Reflection (Criterion D)
Comments on your text
Click a highlighted passage to see its comment.
Criterion breakdown
Criterion A
Presentation
3 / 4
How to improve
- Trim the repetition in the modelling section to improve conciseness
To reach 4/4
Make the exploration more concise and ensure every section contributes directly to the aim.
Criterion B
Mathematical communication
2 / 4
How to improve
- Define every symbol the first time it is used (K, A, r)
- Label the graph axes with units and show the fitted parameters
To reach 3/4
Define all notation on first use and present graphs with labelled axes, units and the fitted parameters, so the mathematics is fully communicated.
Graphs, tables and figures
Image 1 · Scatter of data with a fitted logistic curve
Figure 1 — Logistic model vs collected data
Modelling section
- Label both axes with units (number of students, days)
- Add the fitted parameter values and a measure of fit (R² or residuals)
- Comment in the text on where the model fits well and where it doesn't
Criterion C
Personal engagement
3 / 3
How to improve
- Keep this personal voice going into the reflection
Criterion D
Reflection
2 / 3
How to improve
- Propose how to adapt the model (e.g. a time-varying rate)
- Reflect on what the quality of the fit tells you about the assumptions
To reach 3/3
Extend the reflection: having found the constant-rate assumption unrealistic, propose and justify a refinement to the model.
Criterion E
Use of mathematics
4 / 6
How to improve
- Show the parameter-fitting method, not just the fitted values
- Verify the solution against a boundary condition
To reach 5/6
Demonstrate the parameter-fitting (e.g. linearisation or least squares) and verify the model, rather than stating the fitted values.
What's already working
- Genuine personal engagement with the student's own data
- Correct solution of the logistic differential equation
- A relevant model matched to a real situation
- A clear aim and logical structure