Identifying and supporting high-risk students are critical to increasing retention and persistence. What makes some high-risk students able to succeed, while others attrit? This article argues that early success in college courses is a key factor in resilience among high-risk students. Enrolling students in skill-appropriate courses during their…

Students are most likely to leave science, technology, engineering, and mathematics (STEM) majors during their first year of college. We developed an analytic approach using random forests to identify at-risk students. This method is deployable midway through the first semester and accounts for academic preparation, early engagement in university…

Performance during the first year of college and in introductory courses has been widely identified as critical to college students' retention and success. Accordingly, interventions to assist beginning college students in gateway courses have gained increased attention in higher education. This study tested such an intervention using learning…

Along with the massification of higher education and increasing costs, the pressure on institutions to retain all students to degree completion has been mounting. Early identification of students who are at risk of leaving an institution may help institutions to target and retain these students. This study investigated whether freshmen behaviors,…

Early alert systems offer institutions systematic approaches to identifying and intervening with students exhibiting at-risk behaviors. Many of these systems rely on a common format for student referral to central receiving point. Systems at larger institutions often use web-based technology to allow for a scalable (available campus wide) approach…

Logistic regression models of students' 1-year retention and 6-year retention/graduation for the fall 2000 entering class of students at a research-extensive university in the Midwest were estimated by combining university, financial aid, and Cooperative Institutional Research Program data (n = 1,905; 45% female, 87% Caucasian, 75% in-state).…