Seminar in Psychometrics
Probabilistic Forecasting with International Large-Scale Assessments: Applications to the UN Sustainable Development Goals
Date and time: December 20, 2022 (3:00 PM CET)
Place: ICS CAS plenary room (room 318), Pod Vodárenskou věží 2, Prague 8, also on Zoom 954 7823 4977 (Passcode: 712564).
Note: Jointly held as seminar Hora Informaticae
Abstract. In 2015, the Member States of the United Nations (UN) adopted the Sustainable Development Goals. With regard to education, the UN identified equitable, high-quality education, including the achievement of literacy and numeracy by all youth and a substantial proportion of adults, both men and women, as one of its global SDGs to be attained by 2030. To analyze education policies such as these, it is critically important to monitor trends in educational outcomes over time. Indeed, as educational systems around the world face new challenges due to the COVID-19 pandemic, monitoring trends in educational outcomes could help identify the long-run impact of this unprecedented health crisis on global education. To this end, international large-scale assessment programs such as PISA are uniquely situated to provide population-level trend data on literacy and numeracy outcomes. The purpose of this talk is to describe a new project in collaboration with the University of Heidelberg and funded by the US Institute of Education Sciences. This project proposes a methodology applicable to international large-scale assessments, and PISA in particular, to monitor and forecast changes in gender equity and to relate changes over time in gender equity to policy-relevant predictors and exogenous events such as the coronavirus pandemic. We utilize a Bayesian workflow to account for uncertainty in all steps in the modeling process, including uncertainty in the parameters of the model as well as model uncertainty in the choice of policy-relevant predictors. A proof-of-concept using data from the United States NAEP program provides a demonstration of the ideas.
References.
Kaplan, D., & Huang, M. (2021). Bayesian probabilistic forecasting with large-scale educational trend data: A case study using NAEP. Large-scale Assessments in
Education, 9(1), 1-31. https://doi.org/10.1186/s40536-021-00108-2
Kaplan, D., & Jude, N. (2021). Trend analysis with international large-scale assessments: Past practice, current issues, and future directions. In International
Handbook of Comparative Large-Scale Studies in Education: Perspectives, Methods and Findings (pp. 1-14). Cham: Springer International Publishing.
https://doi.org/10.1007/978-3-030-88178-8_57
David Kaplan
University of Wisconsin – Madison
https://edpsych.education.wisc.edu/staff/kaplan-david/
David Kaplan is the Patricia Busk Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin – Madison. Dr. Kaplan holds affiliate appointments in the University of Wisconsin’s Department of Population Health Sciences and the Center for Demography and Ecology. Dr. Kaplan’s program of research focuses on the development of Bayesian statistical methods for education research. His work on these topics is directed toward applications to large-scale cross-sectional and longitudinal survey designs. Dr. Kaplan is an elected member of the National Academy of Education and served as the chair of its Research Advisory Committee. Kaplan is a recipient of the Samuel J. Messick Distinguished Scientific Contributions Award from the American Psychological Association (Division 5); a past-President of the Society for Multivariate Experimental Psychology and President-elect of the Psychometric Society; a fellow of the American Psychological Association (Division 5); a recipient of the Alexander Von Humboldt Research Award; and a fellow of the Leibniz Institute for Educational Trajectories; and was a Jeanne Griffith Fellow at the National Center for Education Statistics. Dr. Kaplan received his Ph.D. in education from UCLA in 1987.