Tackling Educational Inequality: A Data Analytics Approach

Inequality in educational outcomes has proved resistant to policy and service intervention across high-income countries. In part, this may be due to partial understanding of the determinants of educational inequality, including the inter-relationship between education experiences and other aspects of a young person's developmental experiences. Linking population-based administrative data for individuals and their households offers the potential to understand educational trajectories alongside parallel pathways of the young person and their family through criminal justice, health, social care, and other services. To this end, this studentship offers a superb opportunity to apply novel data analytical approaches to the unique Thames Valley Together dataset, which offers individual and household-level, linked multi-sector longitudinal administrative data. This emerging new data resource integrates over 120 live data feeds from education, police, youth offending service, health, social care, and other data sources, with further linkage to a range of geocoded data also possible. Exploiting these unique linkages offers unprecedented potential to study patterns in service access and intervention rates, revealing hitherto 'hidden' pathways to educational difficulty, and opening up novel opportunities for earlier support and intervention, both within and external to the education system.