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📄 Research Article

Archetypes or ability? Clustering for modelling student mathematical competence

● high · 2026-07-30personalized-learning, stem-education, learning-analytics, student-experience, adaptive-learning, k-12, higher-ed, knowledge-tracing, ai-literacy, formative-assessment

On 119,034 students across 13 UK national exams, Bernoulli Mixture Models found few distinct skill clusters — overall ability dominates. A simple explainable model achieved 78% accuracy, competitive with complex approaches. Small personalization gains are possible by accounting for individual question-level strengths, but students don't develop strongly divergent ability profiles across topics.

Relevance to AI in Education: This paper contributes to the understanding of Automated Assessment, Personalized Learning, and Student Experience. The findings have implications for Adaptive Learning systems, Formative Assessment design, and the broader Edtech Platform landscape. Future work should explore how these results generalize across STEM Education and Higher Ed contexts.

This research connects to the growing body of work on AI Literacy and Teacher Role, highlighting both the promise and limitations of AI tools in educational settings.

Connected Concepts

  • Automated Assessment
  • Personalized Learning
  • Student Experience
  • Adaptive Learning
  • Formative Assessment
  • Edtech Platform
  • STEM Education
  • Higher Ed
  • AI Literacy
  • Teacher Role

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    • Citation

    Benjamin Mawdsley, Tom Quilter, Richard Turner, Sarah Jackson, Paul Edwards (2026). Archetypes or ability? Clustering for modelling student mathematical competence. arXiv:2607.26063. arXiv preprint.