Education policies that work: A context-sensitive ‘big data’ approach

EDUPOL conducts a comprehensive analysis of neoliberal and democratic educational policies' interplay to identify effective policy combinations for improving quality and equality in education.

Subsidie
€ 1.930.120
2024

Projectdetails

Introduction

In the last decades, profound socio-economic changes have posed unprecedented challenges to the traditional centralised model of state-provided education. This has led many countries to adopt neoliberal educational policies (NLPs) promoting school autonomy, competition, and accountability as leverages for school effectiveness.

Resurgence of Democratic Policies

Yet, given increasing societal inequalities, we are also witnessing a resurgence of democratic inclusive educational policies (DIPs) with egalitarian aims. Although these different policies can coexist, they have so far been scrutinised separately.

Project Overview

EDUPOL develops the first comprehensive comparative analysis of the interplay between NLPs and DIPs. By building on the concepts of policy configurations and context-specific effects, the project proposes a paradigmatic shift from the focus on ‘what works’ to ‘what combinations of policies work for whom and in which circumstances’.

Key Aims

The project pursues four key aims:

  1. To investigate the trends and diffusion of NLPs and DIPs across the globe in the last four decades.
  2. To assess their effects on quality and equality in students’ (cognitive and socio-emotional) competencies and civic engagement.
  3. To map within-country school-specific policies.
  4. To uncover the heterogeneous effects of school-level policies across schools and territories.

Methodology

EDUPOL builds a novel macro-longitudinal dataset of educational policies and integrates insights from:

  • International repeated cross-sectional data
  • Ground-breaking Italian big data (millions of student records, textual data from school reports, administrative archives, satellite data)
  • Student population panel data
  • Qualitative interviews with key school actors

Conclusion

By combining qualitative comparative analysis, spatial panel regression, flexible machine-learning techniques, and causal-inference methods, the project will significantly advance our understanding of the conditions under which educational policies work.

Financiële details & Tijdlijn

Financiële details

Subsidiebedrag€ 1.930.120
Totale projectbegroting€ 1.930.120

Tijdlijn

Startdatum1-1-2024
Einddatum31-12-2028
Subsidiejaar2024

Partners & Locaties

Projectpartners

  • UNIVERSITA DEGLI STUDI DI MILANOpenvoerder
  • UNIVERSITA DEGLI STUDI DI TRENTO

Land(en)

Italy

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