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 report uses NAEP background data to track time and learning since the mid-1990s in three areas: student absenteeism; classroom instructional time in mathematics, reading, music and the visual arts; and homework time expected by teachers. Key report findings are: (1) Students with higher rates of "monthly absenteeism" score…