Understanding interaction network formation across instructional contexts in remote physics courses

Engaging in interactions with peers is important for student learning. Many studies have quantified patterns of in-person physics courses using social network analysis, finding different structures between instructional contexts (lecture and laboratory) styles (active traditional). Such also find inconsistent results as to whether how student-level variables (e.g., grades demographics) relate the formation interaction networks. In this cross-sectional research study, we investigate these relationships further by examining lecture lab networks four remote spanning various populations. We apply statistical methods from analysis—exponential random graph models—to measure relationship multiple variables: students’ discussion section enrollment, final course grades, gender, race or ethnicity. Similar previous courses, that contain large clusters connecting many students, while smaller a few students. Our analysis suggests distinct arise combination both instruction-level variables, including learning goals each context, assignments are completed groups individually, distribution gender major students enrolled course. discuss other help understand courses.1 MoreReceived 10 June 2022Accepted 20 October 2022DOI:https://doi.org/10.1103/PhysRevPhysEducRes.18.020141Published American Physical Society under terms Creative Commons Attribution 4.0 International license. Further work must maintain attribution author(s) published article’s title, journal citation, DOI.Published SocietyPhysics Subject Headings (PhySH)Research AreasLearning environmentPhysics Education Research