EduCoDe: Advancing the Understanding of Cognitive Demands in Educational Assessment Tasks

"Complex analysis of educational measurement data to understand cognitive demands of assessment tasks" (EduCoDe) is a grant funded by the Czech Science Foundation, awarded to Patrícia Martinková (Principal Investigator) and Eva Potužníková (co-Principal Investigator) for the period 2025—2027. The grant involves collaboration between the Computational psychometrics group at the Institute of Computer Science of the Czech Academy of Sciences (ICS CAS) and the Institute for Research and Development in Education (IDRE) at Faculty of Education, Charles University.

This multidisciplinary project aims to develop and implement methods that combine textual data extracted from item wording, numerical data on item difficulty, qualitative data on the test takers' perceptions, and content experts' judgments of task difficulty to investigate to what extent an automated analysis of assessment data can identify sources of task difficulty related to classroom teaching and learning. The project builds on theoretical results stemming from project "Theoretical foundations of computational psychometrics" (2021 - 2023) funded by the Czech Science Foundation, and on successful collaboration of ICS CAS, IDRE, and Centre for the Evaluation of Educational Achievement (CERMAT) within the EduTest project (2021 - 2023).

Team members

Previous work we are building on

  • Martinková, P. & Hladká, A. (2023). Computational Aspects of Psychometric Methods: With R. Chapman and Hall/CRC. doi:10.1201/9781003054313

  • Hladká, A., Martinková, P., & Brabec, M. (2024). New iterative algorithms for estimation of item functioning. Journal of Educational and Behavioral Statistics (Accepted). doi:10.48550/arXiv.2302.12648
  • Hladká, A. & Martinková, P. & Magis, D. (2024). Combining item purification and multiple comparison adjustment methods in detection of differential item functioning. Multivariate Behavioral Research, 59(1), 46-61. doi:10.1080/00273171.2023.2205393
  • Štěpánek, L., & Dlouhá, J., & Martinková, P. (2023). Item Difficulty Prediction Using Item Text Features: Comparison of Predictive Performance across Machine-Learning Algorithms. Mathematics, 11(9) doi:10.3390/math11194104
  • Pérez, I., Vomlel, J. (2023). On Identifiability of BN2A Networks. In Bouraoui, Z.; Vesic, S. (Eds.): Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2023. Cham: Springer, 136-148. Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, 14294. ISBN 978-3-031-45607-7.
  • Martinková, P., Hladká, A., & Potužníková, E. (2020). Is academic tracking related to gains in learning competence? Using propensity score matching and differential item change functioning analysis for better understanding of tracking implications. Learning and Instruction, 66, 101286, doi:10.1016/j.learninstruc.2019.101286
  • Hladká, A., & Martinková, P. (2020). difNLR: Generalized logistic regression models for DIF and DDF detection. The R Journal, 12(1), 300—323, doi:10.32614/RJ-2020-014
  • Martinková, P., & Drabinová, A. (2018) ShinyItemAnalysis for teaching psychometrics and to enforce routine analysis of educational tests. The R Journal, 10(2), 503—515, doi:10.32614/RJ-2018-074

Outputs stemming from the project