Learning from learning loss: Bayesian updating in academic universal screening during learning disruptions

Abstract

We used Bayesian ordinal regression methods to examine reading and math screening predictive strength and accuracy before and after learning disruptions related to the Covid-19 pandemic. Using a Bayesian updating procedure in which model estimates from previous years were used as Bayesian priors in following years, we found that reading and math screening was similarly predictive before and after Covid-19 prolonged unplanned school closures (PUSCs) and subsequent learning disruptions (odds ratios range across years: 15–25). We additionally found that predictive strength and accuracy varied across grade levels, but this grade variation was insensitive to learning disruptions. These findings demonstrate the practical applicability of Bayesian updating to universal screening prediction, particularly in the context of PUSCs or other learning disruptions that may impact student academic needs. Limitations and future directions for Bayesian methods in screening are discussed.