  Learning management systems (LMSs) have become essential in higher education
and play an important role in helping educational institutions to promote
student success. Traditionally, LMSs have been used by postsecondary
institutions in administration, reporting, and delivery of educational content.
In this paper, we present an additional use of LMS by using its data logs to
perform data-analytics and identify academically at-risk students. The
data-driven insights would allow educational institutions and educators to
develop and implement pedagogical interventions targeting academically at-risk
students. We used anonymized data logs created by Brightspace LMS during fall
2019, spring 2020, and fall 2020 semesters at our college. Supervised machine
learning algorithms were used to predict the final course performance of
students, and several algorithms were found to perform well with accuracy above
90%. SHAP value method was used to assess the relative importance of features
used in the predictive models. Unsupervised learning was also used to group
students into different clusters based on the similarities in their
interaction/involvement with LMS. In both of supervised and unsupervised
learning, we identified two most-important features
(Number_Of_Assignment_Submissions and Content_Completed). More importantly, our
study lays a foundation and provides a framework for developing a real-time
data analytics metric that may be incorporated into a LMS.
