  Engaging in interactions with peers is important for student learning. Many
studies have quantified patterns of student interactions in in-person physics
courses using social network analysis, finding different network structures
between instructional contexts (lecture and lab) and styles (active and
traditional). Such studies also find inconsistent results as to whether and how
student-level variables (e.g., grades and demographics) relate to the formation
of interaction networks. In this cross-sectional research study, we investigate
these relationships further by examining lecture and lab interaction networks
in four different remote physics courses spanning various instructional styles
and student populations. We apply statistical methods from social network
analysis -- exponential random graph models -- to measure the relationship
between network formation and multiple variables: students' discussion and lab
section enrollment, final course grades, gender, and race/ethnicity. Similar to
previous studies of in-person courses, we find that remote lecture interaction
networks contain large clusters connecting many students, while remote lab
interaction networks contain smaller clusters of a few students. Our
statistical analysis suggests that these distinct network structures arise from
a combination of both instruction-level and student-level variables, including
the learning goals of each instructional context, whether assignments are
completed in groups or individually, and the distribution of gender and major
of students enrolled in a course. We further discuss how these and other
variables help to understand the formation of interaction networks in both
remote and in-person physics courses.
