["Journal of Computer Assisted Learning, Volume 42, Issue 3, June 2026. ", "\nABSTRACT\n\nBackground\nThis research employs the concept of digital maturity (DM) to characterise levels of digital use in education and to represent them on a dashboard. This dashboard should enable assessment of teachers' digital practices and tracking of their technology adoption over time, particularly when new continuing professional development opportunities are offered to support their integration of digital tools into their professional practice. The challenge of this research is to make these observations at scale and in a regular manner. This study explores the use of Teaching Analytics to measure and report on teachers' DM.\n\n\nObjectives\nThe paper proposes methods for processing and analysing teachers' virtual learning environment (VLE) activity logs to characterise DM. Specifically, the focus is on identifying analytical methods for modelling and assessing maturity, as well as visualising population states to help stakeholders better understand and use these data.\n\n\nMethods\nThe techniques employed include structuring data according to the DigCompEdu competence framework, supervised and unsupervised classification to assess maturity levels across dimensions (diversity, intensity, and type of usage), and three visualisations (Sunburst, Histogram, and Bubble chart) integrated into a dashboard. These three visualisations were evaluated (usability and satisfaction, perceived credibility, perceived usefulness, and actionability) during interviews with stakeholders. A discussion also addresses interactive methods for presenting these results through a dashboard. The methods are illustrated using increasingly large datasets representing 12,949 teachers (Paris, 2022–2023), 144,900 teachers (Paris, Lille, Amiens, 2022–2024), and 1,320,000 teachers (national area, 2022–2026).\n\n\nResults and Conclusions\nThis study makes three contributions. First, it proposes an operational framework for designing a multidimensional dashboard that uses VLE logs to assess teachers' DM at scale. It uses supervised and unsupervised classification and considers the intensity of use (frequency) of the VLE platform by teachers, as well as the diversity and types of use. Second, it compares supervised and unsupervised classification approaches to examine their scalability and analytical accuracy. Third, it explores how different institutional actors perceive dashboards, focusing on their interpretability, credibility, and practical usefulness.\n\n"]