It is well recognized and accepted that student success and completion is multivariate and complex. My colleagues Tim Culver and Lew Sanborne have blogged about the importance of a data informed annual retention plan, using multiple data points.
I’d like to dig into just one of those multiple variables we talk so much about, student motivation. Non-cognitive student motivation data enhances campus early-alert programs. Early-alert programs and programs designed to serve at-risk students are regularly cited by campus practitioners as one of the top 10 most effective retention strategies. Like the overall retention plan, these programs are more successful when they are data driven.
The best early-alert programs identify incoming at-risk students before they even enroll and prioritize interventions for those who could most benefit. After enrollment, you want to identify students experiencing academic, social, and/or personal problems that might be addressed by institutional intervention.
The way campuses use the Noel-Levitz Retention Management System Plus illustrates how motivational assessment can provide a significant advantage with student success programs. This system is a suite of non-cognitive motivational assessments and predictive modeling that gauges students’ likelihood of persisting. Campuses use these data to identify at-risk students and student receptivity to institutional support. Using the data, their advising programs, academic support services, mentoring programs, and career services programs target students who indicate both a need and a desire for assistance.