Leveraging LMS Data to Enhance Academic Advising and Early Alert Systems

Session Abstract: Student success in gateway math courses is a strong predictor of persistence and degree completion. At our institution, we piloted an initiative that extracts data from the Canvas learning management system (LMS) to be used for both descriptive reporting and predictive models. Using variables such as assignment submissions, quiz performance, and patterns of LMS engagement, the model generates risk indicators that are shared with advisors through a streamlined early-alert process. This allows advisors and student support staff to identify students that may not be on track to pass gateway courses and refer students to support services such as tutoring or academic coaching. Attendees will leave with a practical roadmap for exploring LMS-based data and connecting predictive models to advising practices, as well as lessons learned from piloting this approach in high-impact gateway courses

Keywords: Predictive Analytics, Predictive Models, Student Success, Student Retention, LMS Data