Skip to content

LEARN Deliverable D2.5: Report on Evidence-Based Education

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)
Evaluation and Performance Assessment Educational Assessment and Improvement

Abstract

This report constitutes Deliverable D2.5 of Work Package 2 under the Horizon Europe-funded project Longitudinal Educational Achievements: Reducing Inequalities (LEARN; Grant Agreement No. 101132531). Focusing on Task 2.5, the report systematically examines the adoption, adaptation, and structural resistance to Evidence-Based Education (EBE) across European educational systems. Using mature institutional models from the Anglosphere (specifically the Education Endowment Foundation in England and the Institute of Education Sciences in the United States) as comparative benchmarks, the study investigates how varying governance structures, pedagogical traditions, and research cultures shape the translation of empirical evidence into classroom practice. Methodologically, the report synthesises four distinct analytical strands: an extensive theoretical scoping review defining the core principles and epistemological boundaries of EBE; semi-structured scoping interviews with leading international EBE academics and policymakers; a multi-national survey administered across eight LEARN consortium countries; and qualitative, thematic deep-dive case studies into the Netherlands, Switzerland, and Finland. Both qualitative interview phases were conducted using a structured, Large Language Model (LLM)-assisted thematic analysis framework coupled with rigorous human oversight and refinement. The findings demonstrate a highly fragmented European EBE landscape characterised by marked regional divergence. While state-led ecosystems such as the Netherlands exhibit high conceptual alignment, other jurisdictions display partial alignment—frequently utilising empirical data for system-level monitoring and accountability rather than pedagogical improvement—or remain underdeveloped. Cross-nationally, the report identifies widespread European preference for "evidence-informed" over "evidence-based" terminology to protect professional autonomy, an ongoing commitment to methodological pluralism that combines causal inference designs with implementation process evaluations, and severe structural bottlenecks. These systemic barriers include a lack of protected time for teachers, deficits in research literacy, fragmented translation platforms, and friction arising from decentralised or autonomous governance structures. The report concludes with strategic considerations for designing sustainable, context-sensitive knowledge mobilisation and brokerage infrastructures across Europe.

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Trajectory Balance: Improved Credit Assignment in GFlowNets

It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...

Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al. · 302 citations · ⚡60

Related blog posts

GPT-Lab Sep 3, 2026

Adaptive AI Agents in Construction Workflows

Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.