Supporting Systematically Marginalized Students in Higher Education Using Analytic-Driven Advisor Action

• In Event: AERA Roundtable Session 15
  In Roundtable Session: Effective Student Success Interventions

Abstract

Big data and analytics claim to offer a solution for institutions to identify and support struggling students (Baer, 2019; Macfadyen, 2017). While big data promises big solutions, barriers to implementation abound and higher education leaders are grappling with how to effectively utilize such data. We conducted logistic regressions using six semesters of academic early warning system (EWS) data from a large public four-year university to assess an institution's strategy for using EWS signals to prompt proactive advisor outreach to struggling students who might not persist to the following term. From a student persistence perspective, findings suggest the institution is on the right track in leveraging EWS signals, however the corresponding advisor outreach requires additional research and refinement of strategy.

Authors

• Tray Geiger, Arizona State University

• Lisa Rodrigue McIntyre, Arizona State University

• Abigail Colburn, Arizona State University