Tackling Educational Inequality: A Data Analytics Approach

事实证明，在高收入国家，教育成果的不平等对政策和服务干预产生了抵制作用。这在一定程度上可能是由于对教育不平等的决定因素，包括教育经历与年轻人发展经历的其他方面之间的相互关系的理解不全面。将基于人口的个人及其家庭的行政数据联系起来，就有可能了解教育轨迹以及年轻人及其家庭通过刑事司法、卫生、社会护理和其他服务的平行途径。为此，该奖学金提供了一个极好的机会，将新颖的数据分析方法应用于独特的泰晤士河谷数据集，该数据集提供个人和家庭层面的，相互关联的多部门纵向管理数据。这一新兴的数据资源整合了来自教育、警察、青少年犯罪服务、卫生、社会关怀和其他数据源的120多个实时数据源，并可能与一系列地理编码数据进一步联系。利用这些独特的联系提供了前所未有的潜力，研究服务的获取和干预率的模式，揭示迄今为止“隐藏”的途径教育困难，并开辟新的机会，早期的支持和干预，内部和外部的教育系统。

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.