Quillattice services · UK education
Mathematical data analysis
Explore assessment, learner or programme data, identify patterns and explain what questions the numbers can support.
Go to the project briefThe offer
A clear brief, shaped for its audience.
Analysis starts with a question and the data available to explore it. The aim is to describe visible patterns clearly and be precise about what the numbers can, and cannot, support.
Illustrative service example · synthetic data
A small question, answered with care.
This worked illustration uses a different dataset from the examples in the portfolio.
Browse the separate portfolio examplesQuestion
Which fraction skill had the fewest correct responses?
Imagine 12 learners answer one short item for each skill in a fraction exit check. The counts below are invented for this example.
Correct responses by item
Each item is scored out of 12- Identify equivalent fractions10 / 12
- Find 1/4 of 209 / 12
- Add fractions with like denominators8 / 12
- Compare 2/3 and 3/56 / 12
- Explain with a diagram7 / 12
The scale starts at zero and stays the same for every item.
Plain-English interpretation
One item stands out for review.
Comparing 2/3 and 3/5 has the fewest correct responses (6 of 12); identifying equivalent fractions has the most (10 of 12). In this invented snapshot, the comparison question is a prompt to review the item and teaching sequence.
A relevant limitation
This does not explain why.
There are only 12 hypothetical responses and one item per skill. The pattern cannot explain individual answers, represent a real class or show learning over time.
Synthetic education dataset · illustrative service-page example · not client work
How the work can take shape
What the work can include
Explore assessment, learner or programme data against an agreed question.
Identify patterns and differences that are visible in the selected data.
Explain what the numbers can support without overstating the evidence.
Present the analysis and its limits in language people can use.
AI-assisted · Human-reviewed
AI supports the process. People shape the work.
AI supports exploration and drafting; mathematical judgement, classroom context and human review shape the deliverable.
For analysis, mathematical judgement keeps interpretation close to what the data supports and relevant to its education context.
Learning access and inclusion
Keep the learning goal in view. Check what helps access it.
A resource may need to account for a particular learner, barrier or setting. Our inclusion approach starts with individual evidence and learner voice rather than assumptions about a group.
Read Every Student MattersA good place to begin
Tell us about the work you have in mind.
The project brief helps set out the audience, context and questions behind the request.