Analytics to Action: Predictive Model Outcomes and a Communication Strategy for Student Persistence. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Miller, Nathan Brad; Bell, Bryan
• Is Part Of: Journal of continuing higher education. Volume 64:Issue 1(2016)
• Page Start: 16
• Page End: 29
• Publication Date: 2016-01-02
• Subjects: nontraditional student retention -- predictive model -- communication strategy -- analytics -- persistence -- accelerated programs; Continuing education -- Periodicals; Education, Higher -- Periodicals; University extension -- Periodicals; Graduate students -- Periodicals; 374
• DOI: 10.1080/07377363.2016.1125218
• Languages: English
• ISSNs: 0737-7363

Abstract

Abstract: Increased federal attention to student completion metrics and uncertain financial forecasts have heightened the tenor of student retention conversations. Improved institutional retention rates will lead to higher completion rates and relieve some funding concerns. To accomplish these improvements, institutions have invested in analytics to better understand student persistence and completion factors. However, few studies outline how analytics are employed to positively influence student success and persistence. This article outlines the creation of a predictive model for student persistence, and details the use of the model outcomes to create a communication strategy intended to increase student persistence. The methodology used to develop the persistence model and the results of the communication strategy are included.