Research

My research sits at the intersection of methodology and educational science. I develop and apply advanced quantitative methods to study educational inequalities and the factors that shape learning outcomes.

Current Projects

Statistical Methods for Research Synthesis

I develop statistical methods for research synthesis, with a focus on complex educational data and evidence integration. My research includes individual participant data meta-analysis, methods for synthesizing data from large-scale assessments, approaches for handling effect-size dependencies, and the application of machine learning to evidence synthesis.

Mapping of Longitudinal Data of Inequalities in Education (MapIE)

As part of the EU-funded MapIE project (https://mapie-project.eu/home), we examine the mechanisms through which individual pupil-level characteristics contribute to educational inequalities across Europe. Our research focuses on understanding how these factors influence educational outcomes and on assessing the capacity of policies and practices to compensate for such disparities. By analyzing educational systems at the school, regional, and national levels, we aim to generate evidence that can inform more equitable and effective educational policies.

AIRES — AI-Assisted Research Synthesis in Educational Sciences

I am developing AIRES (AI-Assisted Research Synthesis), a research program focused on integrating artificial intelligence into evidence synthesis workflows in the educational sciences. The project aims to make systematic reviews and other forms of research synthesis more efficient, transparent, and reproducible by combining rigorous review methodology with advances in machine learning and large language models.

Educational Inequalities

I examine the factors that shape students’ educational outcomes and the development of competencies that are essential for learning and participation in modern societies. Using large-scale national and international assessments, as well as linked administrative register data, I investigate how individual characteristics, family background, school environments, and broader contextual conditions contribute to educational inequalities and differences in student achievement.

Methodological Expertise

  • Meta-analysis and systematic reviews
  • Individual Participant Data (IPD) meta-analysis
  • Multilevel modeling
  • Structural equation modeling and meta-SEM
  • Machine learning for evidence synthesis
  • International Large-Scale Assessments (PISA, ICILS, TIMSS, PIRLS)
  • R (primary statistical environment)