Jan. 28, 2026
Contact: Janese Heavin, heavinj@missouri.edu
Photo by Morgan Solomon
Students taking online courses leave behind a trail of data, from page views and clicks to navigation patterns. Now, a University of Missouri researcher has analyzed that digital footprint, comparing how students behave online with what motivates them to learn. The result is a spectrum of learning behaviors that can help instructors better understand their students and design more engaging online courses.
Kui Xie, dean and Joanne H. Hook Dean’s Chair in Educational Renewal in the College of Education and Human Development, recently published his findings in the Journal of Computing in Higher Education.
Using an online course he taught prior to coming to Mizzou, Xie analyzed nearly 11,000 online actions from 276 students as they worked through course materials. The data included how students navigated between reading materials and assignments, how long they spent on individual pages and how often they toggled between different sections of the course.
Xie also surveyed those students to find out what motivated them to succeed in the class. The surveys measured factors such as students’ confidence in their abilities, their interest in the subject matter and how useful they believed the coursework was for their career goals. By pairing those responses with detailed activity data, researchers were able to link why students were motivated to how they actually engaged online.
“What’s significant about this study is that we looked at students’ online activity in two ways: how often they engage with course materials and how they move through them,” Xie said. “That gives us a much clearer picture of how students actually learn in online classes.”
What emerged from the study wasn’t a single definition of student engagement but rather a range of distinct learning behaviors. Students who appeared equally active in the course were, in reality, learning in substantially different ways. Some students focused almost entirely on completing assignments, while others balanced time between readings and assignments. Another group regularly moved among readings, additional resources and assignments, indicating a more integrated approach to learning. Still others engaged with the material over longer periods of time, suggesting a more reflective style.
The patterns allowed Xie to identify multiple engagement profiles ranging from highly assignment-focused behaviors to more exploratory approaches. He stressed that no single pattern was deemed “right” or “wrong,” but rather the profiles revealed how motivation and confidence shape the way students approach online learning. These insights point to a more holistic way of understanding digital learning.
“When we focus only on performance, we’re ignoring the whole picture,” Xie said. “This paper suggests we design for engagement and motivation, which will drive better learning experiences. We should design courses that blend resources, readings and assignments and incorporate them together for more integrated behaviors. That way we’re designing with intention for engagement and motivation.”
For instructors interested in applying these insights, Xie said many learning management systems already capture the types of data used in the study, including page views, navigation patterns and time spent on course materials. By paying attention to how students move among course materials, educators can begin to identify different engagement patterns in their own courses. Pairing that information with simple surveys about student motivation can offer a more complete picture of how students are learning and where additional support may be needed.
