- ASA 2022 Data-Driven Decision Making
- Edited by Enrico di Bella, Luigi Fabbris, Corrado Lagazio
The joint estimation of accuracy and speed: An application to the INVALSI data
- Luca Bungaro
- Marta Desimoni
- Mariagiulia Matteucci
- Stefania Mignani
- © 2023 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-0106-3.39
In Italy, the National Institute for the Evaluation of the Education and Training System (INVALSI) every year administers standardized tests via computer-based testing (CBT) to students attending grades 8, 10, and 13. The CBT mode allows to collect data not only on the students’ response accuracy (RA) based on item responses, but also on their response times (RT). By using these data, it is now possible to estimate the speed ability of examinees, besides the usual ability (e.g. Italian language, mathematics or English ability). In this study, we use the 2018 mathematics data for grade 10 to estimate the ability and speed of students following the fully Bayesian approach of Fox et al. (2021), who implemented in the R package LNIRT the models of van der Linden (2007) and Klein Entik et al. (2009). In a second step, we use the estimated mathematics ability and speed in a bivariate multilevel model, where the first-level units are represented by students and the second-level units are represented by classes. Covariates such as gender, school type, immigrant status, economic, social, and cultural status, prior achievement, grade retention, student anxiety, class compositional variables, and geographical area are included in the model. The main results show that the ability and speed are inversely proportional, e.g. as ability increases, speed decreases. Also, differences in the students performance by gender and school type are significant for both ability and speed.
- Keywords:
- educational assessment,
- large standardized test,
- mathematics achievement,
- IRT models for response times,
- multilevel models,
University of Bologna, Italy
INVALSI, Italy - ORCID: 0000-0002-3407-0002
University of Bologna, Italy - ORCID: 0000-0003-3404-6325
University of Bologna, Italy - ORCID: 0000-0003-4746-1130
- Charlton, C., Rasbash, J., Browne, W.J., Healy, M., Cameron, B. (2020). MLwiN Version 3.05. Centre for Multilevel Modelling, University of Bristol.
- Fox, J. P., Klotzke, K., & Simsek, A. S. (2021). LNIRT: An R Package for Joint Modeling of Response Accuracy and Times. arXiv preprint arXiv:2106.10144.
- Klein Entink, R. H., Fox, J.-P., van der Linden, W. J. (2009). A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers. Psychometrika, 74(1), pp. 21-48.
- Rasbash, J., Steele, F., Browne, W.J., Goldstein, H. (2017). A User's Guide to MLwiN, v3.00. Centre for Multilevel Modelling, University of Bristol.
- van der Linden, W. J. (2007). A Hierarchical Framework for Modeling Speed and Accuracy on Test Items. Psychometrika, 72(3), 287.
Chapter Information
Chapter Title
The joint estimation of accuracy and speed: An application to the INVALSI data
Authors
Luca Bungaro, Marta Desimoni, Mariagiulia Matteucci, Stefania Mignani
Language
English
DOI
10.36253/979-12-215-0106-3.39
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
ASA 2022 Data-Driven Decision Making
Book Subtitle
Book of short papers
Editors
Enrico di Bella, Luigi Fabbris, Corrado Lagazio
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press, Genova University Press
DOI
10.36253/979-12-215-0106-3
eISBN (pdf)
979-12-215-0106-3
eISBN (xml)
979-12-215-0107-0
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846