There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…

The purpose of this research to investigate the heterogeneity of per-pupil expenditure (PPE) slope estimates in predicting student achievement. The research question guiding this project is: how does the measured relationship between per-pupil expenditure vary across studies that use different models? In concert with SREE's 2012 conference mission…

This paper aims to provide metro, state, and national policy makers with a better sense of the specific problems facing metropolitan labor markets. First, the analysis examines trends in the demand for educated labor and how a gap between education supply and demand is related to unemployment. Next, it attempts to distinguish between cyclical and…

Since 1973, the National Assessment of Educational Progress (NAEP) has gathered information about student achievement in mathematics. Results of these periodic assessments, produced in print and web-based formats, provide valuable information to a wide variety of audiences. They inform citizens about the nature of students' comprehension of the…

This study examined need, predisposing, market, and regional factors that predicted the likelihood of individuals with developmental disabilities living in state developmental centers (DCs) compared with living at home, in community care, or in intermediate care (ICFs) and other facilities. Secondary data analysis using logistic regression models…