R&D Outcomes

Computer-based assessments collect process data to give insights into testees' response behaviors, strategies, and motivations. This project, supported by the IEA Research and Development Fund, assesses areas that help researchers enhance their understanding of student motivation and how they interact with tasks in large-scale assessments.

This paper uses teacher data from 28 educational systems participating in the ICILS 2023 field trial to investigate the differences between question formats.

Using data from ICILS 2013 and 2018, this research leverages technology to address educational and occupational research challenges, promoting greater inclusivity and precision in international studies.

This project reports on the feasibility of automatic scoring systems for text responses from the 2016 ePIRLS assessment.

Developing questionnaires for international large-scale assessments can be challenging. This white paper addresses questionnaire development for educational assessment projects and the language used in its items.

An outcome from the first R&D call, this report addresses the question how to evaluate the validity of results of international large-scale assessment programs (ILSAs) that incorporate technology-enhanced items, paying special attention for the comparability of results between countries.

This report informs and provides recommendations on technical standards and reporting in international large-scale assessments.

Using data from ICILS 2018, this report focuses on the nine countries and regions assigned to the computation thinking model to better understand missing responses.

The first outcome from the IEA R&D fund call two. This report discusses the construction of scaling models for large-scale assessments in education, applying methods to PIRLS 2016 data.

The first output from IEA's R&D call one, this report assesses how to validate human rater scores for graphical responses using AI.