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…

An ethnically diverse sample of 6th-grade students completed peer nomination procedures that were used to create subgroups of students with reputations as victims, aggressors, aggressive victims, and socially adjusted (neither aggressive nor victimized). Self-report data on psychological adjustment, attributions for peer harassment, and perceived…