As part of our mission, IEA aims to advance research and innovation by actively promoting research projects and activities.
In 2021, the IEA R&D (Research and Development) Fund was created to improve the science and methodology of IEA studies, ensuring that they remain at the forefront of international large-scale assessments in education.
Each year, the R&D Fund addresses a variety of key topics through open and thematic calls. We look forward to seeing how the future cycles of R&D funding further the work of IEA and research in the ILSA field.
IEA is delighted to have awarded 32 total projects with funding across five calls for R&D proposals. The proposals, focus on a diverse range of topics relevant to the work of IEA. Information on the awardees from Call 4 is here. See below for the full list of projects and the available outcomes.
| Project | Outcome |
|---|---|
Methodological advances to enhance the extraction of policy-relevant information in TIMSS and TIMSS-L Henry Braun, Damian Betebenner, Kiara Sanchez
TIMSS and TIMSS-L, collect enormous amounts of data. This project will substantially enhance the amount of ILSA policy-relevant information by implementing statistical methods that heretofore haven’t been applied in international contexts. For example, it will demonstrate how TIMSS data can be used to obtain estimates of comparative growth from grade 4 to grade 8 within and between countries. | Outcome Forthcoming |
IEABank: Documentation standards and item bank for past and future IEA studies Andrés Strello, Andrés Christiansen, Maximiliano Romero, Pablo Torres Irribarra, Rolf Strietholt
IEABank creates a centralized item bank template for IEA studies. It will set common documentation standards, improve collaboration across teams, support consistency across studies and cycles, and make IEA data easier to use for trend analyses – both internally and for external researchers. | Outcome Forthcoming |
Beyond the Score: A multi-level and cross-grade analysis of process data in TIMSS 2023 for equity, instruction, and item design Philipp Doebler, Purya Baghaei Moghadam, Hamdollah Ravand, Susanne Frick, Farshad Effatpanah Hesari
Beyond the achievement scores in TIMSS 2023, what does the rich log-data reveal about test-taking behavior? Log-data comprises timings, click events, navigation paths, and action sequences, an unprecedented objective record of the micro-level behaviors students exhibit while solving problems. Specifically, the project studies persistence, rapid guessing, and strategic reviewing. | Outcome Forthcoming |
ILSAreport: Accessible tables using IEA data with R Andrés Christiansen, Andrés Strello, Katie Zuber
The ILSAreport project aims to create an R package that will streamline the production of tables for IEA international reports, ensuring their compliance with accessibility guidelines. | Outcome Forthcoming |
Bridging Time and Context: Longitudinal comparability of background scales in international assessments (COMPASS) Diego Carrasco, Andrés Sandoval-Hernandez, Nurullah Eryilmaz
COMPASS examines longitudinal and cross-national comparability of ICCS background scales using ordinal CFA and alignment methods. The initiative will generate comparable scale scores when feasible, develop methodological tools for invariance diagnosis, and aid trend analyses in international large-scale assessments. | Outcome Forthcoming |
Modeling problem-solving trajectories in ICILS 2023 using multi-state survival analysis: A study with computational thinking (CT) assessment data Jennifer Viñas Forcade, Mariana González Burgstaller, Valentina Cancela, Déborah Zak Godoy, Gimena Castelao, Leonardo Moreno
This project examines how students solve computational thinking tasks in ICILS 2023 by modeling problem-solving states and transitions. Using multi-state survival models, it examines transitions between incorrect, partial, and correct states, response times and strategies, comparing students and countries to provide evidence for teaching and to improve computer-based assessments design. | Outcome Forthcoming |
Real-time dashboard for monitoring data collection procedures and outcomes in international large-scale assessments Sebastian Meyer, Diego Cortes, Marius Kaltenbach, Ali Raza
This project develops a reusable real-time dashboard to monitor data collection activities in large-scale assessments. By providing timely indicators, visual summaries, and early warnings, it enables proactive interventions to address emerging risks, supports efficient study management, and strengthens transparency and collaboration between IEA and participating countries during ongoing fieldwork. | Outcome Forthcoming |
Exploring the Application of AI/Machine-Learning Based Options for IEA Translation and Translation Verification Procedures Lauren Musu, Miguel Zea, David Ebbs, Anja Waschk, Aisha Elsayed
The project explores integrating AI-assisted translation into IEA workflows by developing an IEA-specific translation and quality estimation model. Using verified past translations, its performance will be compared with other translation outputs through automated metrics and blind human reviews to assess feasibility, quality, and scalability. | Outcome Forthcoming |
Linking grade 4 and grade 8: structural differences and comparability of TIMSS student context indices via MGCFA and network analysis Yulia Kuzmina
Student context indices such as interest or confidence in mathematics are widely used in TIMSS analyses, yet their comparability across grades remains understudied. This project investigates similarities and differences between Grade 4 and Grade 8 contextual indices to support valid cross-grade comparisons of students’ motivation and attitudes toward school. | Outcome Forthcoming |
| Project | Outcome |
|---|---|
An AI-Driven Approach to TIMSS Item Verification and Alignment Ummugul Bezirhan and Matthias von Davier
| Outcome Forthcoming |
SMART: Survey Mode and Response Trends in TIMSS and PIRLS Context Questionnaires Yuan-Ling Liaw, Alec Kennedy, Rune Müller Kristensen, Rolf Strietholt, and Christian Christrup Kjeldsen
| |
Striving for More Inclusiveness: Understanding and Addressing Student Exclusions in TIMSS and PIRLS Sabine Meinck, Umut Atasever, Matthias von Davier, Rolf Strietholt, and Bethany Fishbein
| Outcome Forthcoming |
Enhancing Questionnaire Relevance in Low- and Middle-Income Countries: A Comparative Analysis of Regional Assessment Frameworks and IEA Instruments in Primary Education Oliver Neuschmidt, Clara Beyer, Sarah Howie, and Katherine Reynolds
| Outcome Forthcoming |
Information Retrieval Using Retrieval-Augmented Generation on PIRLS Documents Widianto Persadha and Heiko Sibberns
| Outcome Forthcoming |
Assessment Design for Accommodating Participants with Tailored Support Rolf Strietholt, Nurullah Eryilmaz, Julian Fraillon, Olga Kunina-Habenich, and Ana María Mejía-Rodríguez
| Outcome Forthcoming |
A Two-Step Imputation Approach Combining Item-Response-Theory and Deep-Learning Methods in Large Scale Survey Assessments Peter van Rijn, Usama Ali, and Priyadarshini Dwivedi
|
| Project | Outcome |
|---|---|
An Examination on the Performance of Sampling Variance Estimators in ILSAs Umut Atasever, Sabine Meinck, and Diego Cortés
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Field Trial Sample Size Andrés Christiansen
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Using Large Language Models for Automatic Item Generation: Development and Validation for TIMSS Fourth Grade Marek Muszyński, Hubert Plisiecki, Tomasz Żółtak, and Artur Pokropek
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Approximate Areas of Interest for Enhanced Understanding of Student Motivation and Task Interaction in IEA Assessments Artur Pokropek, Tomasz Żółtak, and Marek Muszyński
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Enhancing Cross-Cultural Comparisons in ILSAs: A Comprehensive Investigation and Guideline Development Andrés Sandoval-Hernández, Diego Carrasco, and Nurullah Eryilmaz
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Dimensionality and Mode Effects in PIRLS 2016 and 2021: Evaluating the Comparability of Different Assessment Modes Rolf Strietholt, Stefan Johansson, Elpis Grammatikopoulou, Purya Baghaei,
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Gauging the Instructional Sensitivity of Ordinal Assessment Items Anne Traynor, Özge Altıntaş, Yu-Hui Chang, and Setlhomo Koloi-Keaikitse
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| Project | Outcome |
|---|---|
CABE: Studying School-level Nonresponse within a Partial Identification Framework Diego Cortes, Jeff Dominitz, Maximiliano Romero, and Sabine Meinck
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Improving Scaling in Large-scale Assessment: A Variable Selection Approach to Latent Regression Yunxiao Chen, Motonori Oka, and Matthias von Davier
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Improving Data in Electronic Surveys Mojca Rožman, Andrés Christiansen, Rolf Strietholt, Jeppe Bundsgaard, Julian Fraillon, and Ronny Scherer
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To Mix or not to Mix Positively and Negatively Worded Items Isa Steinmann
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Improving Parental Occupation Procedures with AI Daniel Duckworth and Julian Fraillon
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Operational Automatic Scoring of Text Responses in 2016 ePIRLS: Performance and Linguistic Variance Hyo Jeong Shin, Nico Andersen, Andrea Horbach, Euigyum Kim, Jisoo Baik, and Fabian Zehner
|
| Project | Outcome |
|---|---|
Using Neural Network Classification of the Automated Scoring of Images Responses in TIMSS 2023 Lale Khorramdel, Lillian Tyack, and Matthias von Davier
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Understanding Students' Computational Thinking Strategies Using Process Data in ICILS 2018 Qiwei He and Eugenio Gonzalez
| |
Validity and Measurement Properties in Technology-Enhanced Items: A Balancing Act Saskia Wool, Paul Drijvers, Remco Feskens, Dylan Molenaar, and Emmelien van der Scheer
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The R&D fund is currently closed for proposals, please watch this space for future open calls.
The R&D program welcomes proposals from researchers external to IEA, staff, and experts from study centers, and IEA staff. The award is based on merit only.
All proposals submitted for consideration must:
- Be based on rigorous, intellectually ambitious, and technically sound research that is relevant to the most pressing questions and opportunities in IEA’s work on ILSAs;
- Relate to methods and approaches that IEA uses in one or more of its studies, or related to those methods and approaches that would, in a forward-looking way, have the potential to significantly improve IEA’s work in the future;
- Propose specific approaches, innovations, and methods with a tangible outcome or recommendation that adds value to IEA studies and can be used within the work of IEA immediately or in the future;
- Ensure the proposed approaches can be realistically implemented and are tied to the actual needs identified within the proposal;
- Not be duplicative of work already ongoing at IEA.
For any questions regarding R&D, please contact rd@iea.nl.
All R&D proposals and final outcomes are subject to a single-blind peer review process. We are extremely thankful to our network of peer reviewers and would like to acknowledge their commitment to ensuring high-quality selection and research results from the R&D projects.
For call 1 through 5, our review network includes (in alphabetical order):
Francesco Avissati
Peter Barwis
Ummugul Bezirhan
Johan Braeken
Maximilian Brinkmann
Janine Buchholz
Jeppe Bundsgaard
Nathan Burroughs
Charalambos Charalambous
Yunxiao Chen
Diego Cortes
Julian Fraillon
Eugenio Gonzalez
Jiangang Hao
Martin Hooper
Sarah Howie
Hong Jiao
Nina Jude
Alec Kennedy
Saskia Kespaik
Lale Khorramdel
Tom Krenzke
Ulf Kröhne
Aurelie LaCriox
Dihao Leng
Yuan-Ling (Linda) Liaw
Antoine Marivin
João Marôco
Guido Martin
Michael O. Martin
Sabine Meinck
Bart Meulemann
Daniel Andres Miranda Fuenzalida
Christian Monseur
Patrick Montjouridis
Irini Moustaki
Marek Muszyński
Gabriel Nagy
Oliver Neuschmidt
Elodie Persem
Mick Couper
Steve Dept
Reinaldo dos Santos
Kristi Donaldson
Brita Dorer
Daniel Duckworth
Jean Dumais
David Ebbs
Michèle Ernst Stähli
Remco Feskens
Bethany Fishbein
Pierre Foy
Ricardo Primi
Stephen Provasnik
Mark Reckase
Katherine Reynolds
Mojca Rozman
David Rutkowski
Leslie Rutkowski
Franck Salles
Andres Sandoval-Hernandez
Wolfram Schulz
Heiko Sibberns
Peter Siegel
Megan Sim
Stephen Sireci
Rolf Streitholt
Andres Strello
Nicole Swart
Esther Ulitzsch
Matthias van Davier
Peter van Rijn
Daniele Vidoni
Steve Wise
Kentaro Yamamoto
Fabian Zehner
Ting Zhang
Katie Zuber