Pinpoint Learning mock-exam dataset of 119,034 UK students across 13 national-level mock exams¶
Product or Programme · All products and programmes
Evidence · 2 claims (2 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
A question-level GCSE mathematics mock-exam results dataset extracted from the Pinpoint Learning platform and collected from teacher-uploaded assessments.
Components¶
Claims¶
- Explainable models including logistic regression, kNN and a Bernoulli Mixture Model all reach around 78% accuracy in predicting question-level pass/fail, with well-calibrated probabilities [+W]
- Cluster profiles of student mathematics performance are dominated by overall ability, with most clusters being linear rescalings of average exam performance [+W]
Related Products and Programmes¶
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Key Sources¶
- Benjamin Mawdsley, Tom Quilter, Richard Turner, Sarah Jackson, and Paul Edwards. (2026). Archetypes or ability? Clustering for modelling student mathematical competence. https://arxiv.org/abs/2607.26063
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
pinpoint-learning-mock-exam-dataset-of-119034-uk-students-across-13-national-level-mock-ex |
title |
Pinpoint Learning mock-exam dataset of 119,034 UK students across 13 national-level mock exams |
description |
A question-level GCSE mathematics mock-exam results dataset extracted from the Pinpoint Learning platform and collected from teacher-uploaded assessments. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
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