Improving Early Warning Systems with Categorized Course Resource Usage


  • Richard Joseph Waddington University of Kentucky
  • SungJin Nam
  • Steven Lonn
  • Stephanie D. Teasley



Early Warning Systems, Academic Advisors, Learning Management Systems, Course Resources, Student Grades


Early Warning Systems (EWSs) aggregate multiple sources of data to provide timely information to stakeholders about students in need of academic support. There is an increasing need to incorporate relevant data about student behaviors into the algorithms underlying EWSs to improve predictors of students’ success or failure. Many EWSs currently incorporate counts of course resource use, although these measures provide no information about which resources students are using. We use seven years of data from seven core STEM courses at a large university to investigate the associations between students’ use of categorized course resources (e.g., lecture or exam preparation resources) and their final course grade. Using logistic regression, we find that students who use exam preparation resources to a greater degree than their peers are more likely to receive a final grade of B or higher. In contrast, students who use more lecture-related resources than their peers are less likely to receive a final grade of B or higher. We discuss the implications of our results for developers deciding how to incorporate categories of course resource usage data into EWSs, for academic advisors using this information with students, and for instructors deciding which resources to include on their LMS site.

Author Biography

Richard Joseph Waddington, University of Kentucky

Assistant Professor of Educational Policy Studies and Evaluation




How to Cite

Waddington, R. J., Nam, S., Lonn, S., & Teasley, S. D. (2016). Improving Early Warning Systems with Categorized Course Resource Usage. Journal of Learning Analytics, 3(3), 263-290.