Developing a LMS Data-Driven Approach to Support At-Risk Students

As institutions of higher education seek to improve student outcomes, data analytics has become an essential tool for identifying at-risk students and providing timely support. Learning Management System (LMS) data offer valuable insights into student engagement and academic progress. In this presentation, we will share our experience using Canvas data to develop a tool for academic advisers to identify at-risk students and proactively support their success. Our approach involved creating a risk profile for each student based on their prior academic success, campus system access, and daily Canvas course grades and engagement. To streamline the process of allowing academic advisors to identify at-risk students, a dashboard was created detailing the risk indicators and the academic support structures available for each enrollment. In this presentation, we will share best practices and lessons learned from our experience piloting this approach at a four-year public university. We will discuss the challenges we faced, as well as possible strategies for overcoming these challenges. Attendees will leave with a deeper understanding of how Canvas data can be leveraged to identify at-risk students and proactively support their success. We will demonstrate the value of using a dashboard to streamline the process of identifying at-risk students and providing personalized support.