Estimating the Korean CBM Slope for Individual Students: Utilizing Bayesian Prior Distributions

This study explores the effectiveness of Bayesian prior distributions in estimating the slope of individual students’ reading progress using Curriculum-Based Measurement (CBM). While traditional Ordinary Least Squares (OLS) regression is commonly employed in CBM research, it often yields unstable estimates, particularly when data points are limited or influenced by outliers. This paper investigates whether Bayesian estimation can provide more reliable slope predictions compared to OLS by incorporating prior information from relevant studies. Using Korean first-grade students’ reading CBM data, we compare the accuracy of the two methods through Root Mean Square Error (RMSE) analysis. The findings reveal that Bayesian estimation consistently outperforms OLS, especially for students with reading difficulties, indicating that stronger prior distributions yield more stable results. The implications for special education practitioners include improved decision-making regarding intervention strategies and reduced data collection efforts. This study underscores the potential of Bayesian methods to enhance the precision of individual progress monitoring in educational settings.