|Title:||Bayesian Probabilistic Forecasting with International Large-Scale Assessments|
|Principal Investigator:||Kaplan, David||Awardee:||University of Wisconsin, Madison|
|Program:||Statistical and Research Methodology in Education [Program Details]|
|Award Period:||3 years (06/01/2022 – 05/31/2025)||Award Amount:||$896,582|
|Type:||Methodological Innovation||Award Number:||R305D220012|
Co-Principal Investigator: Jude, Nina
The purpose of this project is to develop models and methods to demonstrate that international large-scale assessments, in particular PISA, can be used to forecast international trends in literacy and numeracy outcomes. More specifically, the researchers will synthesize latent growth curve modeling, Bayesian model averaging (BMA), and probabilistic scoring rules into a comprehensive modeling framework for forecasting international trends in education outcomes over time. The researchers will concentrate on gender gaps in literacy and numeracy, with a particular focus on forecasting the impact of COVID-19 on these trends. The main product of the grant is a Shiny app (ShinyBPF) that will enable applied researchers to use the combined modeling framework for different forecasts using large-scale assessment data. The research team will provide extensive tutorials on the use of the software. They also plan to conduct workshops on the use of the Shiny app, present technical and applied findings at conferences, and publish technical and applied findings in peer-reviewed journals.