Student success is critical to improve retention and graduation rates. Fanshawe College piloted machine learning to integrate data from the Student Information System (SIS) and Learning Management System (LMS) and provide early identification of academically-at-risk students enrolled in the first term of a program. Findings showed early and consistent student engagement with weekly LMS course content significantly enhanced successful completion of that course. We overview the methods used to provide prediction, identifies key LMS metrics, and discusses possible academic and student service enhancements that can optimize course-level success for students enrolled in the first term of a new program.
Ling Zou (Fanshawe College), Robert Downie (Fanshawe College), Heather Cummings (Camosun College)
Monday, October 24th 10:30 – 11:10 am
Crystal Ballroom 3