COMPLETING THE SELF-REGULATION CYCLE: UTILIZING ONLINE TRACE DATA TO CHARACTERIZE STUDENT LEARNING BEHAVIORS IN INTRODUCTORY PHYSICAL GEOLOGY COURSES

There has been a significant amount of community-wide investigation that has focused upon how student learning is affected by classroom variables such as the level of student-centered instruction in the classroom as measured by observational instruments (e.g., RTOP). Additionally, much of what we now know about student learning in the geosciences has been provided via data from self-report surveys and exam scores. In relation to both efforts, however, there is an obvious and critical environment that exists between what happens in the classroom, what happens when a student fills out a survey, and what happens when they show up for an exam: the individual studying that occurs elsewhere. It has been well-documented that effective learning is self-regulated, but how does the self-regulation of learning occur in geoscience courses? Trace data derived from students’ interactions with online material (e.g., learning management systems) is one potential source of information that can be used to help complete this picture and triangulate signals from other datasets. We analyzed trace data collected from the learning management system (Moodle) supporting both face-to-face and hybrid formats of an introductory physical geology course. To isolate student learning behaviors, we analyzed the usage patterns of students in each course setting to highlight self-regulated learning behaviors that contributed to student success or failure in learning course concepts. As learning management systems have become near-ubiquitous elements of college courses, we will discuss our methodology and how instructors can learn more about their students through investigating the dormant dataset at their fingertips.