Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…

This nonexperimental survey research investigated whether enrollment, location, expenditures, percentage of free and reduced lunch and percentage of minority students influenced Georgia's superintendent and board chairperson satisfaction. In addition, this study investigated whether respondents' satisfaction could predict student achievement.…