With the increasing adoption of Learning Management Systems (LMS) in colleges and universities, research in exploring the interaction data captured by these systems is promising in developing a better learning environment and improving teaching practice. Most of these research efforts focused on course-level variables to predict student performance in specific courses. However, these research findings for individual courses are limited to develop beneficial pedagogical interventions at the student level because students often have multiple courses simultaneously. This paper argues that student-centric models will provide systematic insights into students' learning behavior to develop effective teaching practice. This study analyzed 1651 undergraduate student's data collected in Fall 2019 from computer science and information systems departments at a US university that actively uses Blackboard as an LMS. The experimental results demonstrated the prediction performance of student-centric models and explained the influence of various predictors related to login volumes, login regularity, login chronotypes, and demographics on predictive models. Our findings show that student prior performance and normalized student login volume across courses significantly impact student performance models. We also observe that regularity in student logins has a significant influence on low performing students and students from minority races. Based on these findings, the implications were discussed to develop potential teaching practices for these students. [For the full proceedings, see ED615472.]