Tackling Educational Inequality: A Data Analytics Approach

解决教育不平等：数据分析方法

• 批准号: 2584276
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2584276
• 关键词: Tackling; Educational; Inequality; Data; Analytics

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.

事实证明，高收入国家的教育成果不平等会阻碍政策和服务干预。在某种程度上，这可能是由于对教育不平等的决定因素的部分理解，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。将个人及其家庭的基于人口的行政数据联系起来，有可能了解青少年及其家庭通过刑事司法、卫生、社会关怀和其他服务的平行路径的教育轨迹。为此，该学生提供了一个极好的机会，可以将新颖的数据分析方法应用于独特的泰晤士河谷共同数据集，该数据集提供个人和家庭层面的、链接的多部门纵向管理数据。这一新兴数据资源集成了来自教育、警察、青少年犯罪服务、健康、社会关怀和其他数据源的 120 多个实时数据源，还可以进一步链接到一系列地理编码数据。利用这些独特的联系为研究服务获取和干预率的模式提供了前所未有的潜力，揭示了迄今为止“隐藏”的教育困难途径，并为教育系统内部和外部的早期支持和干预开辟了新的机会。