Understanding interaction network formation across instructional contexts in remote physics courses

TL;DR

This study analyzes how interaction networks form in remote physics courses, revealing that instructional context and student variables influence network structures, with implications for understanding peer engagement in online physics education.

Contribution

It applies exponential random graph models to remote physics courses, providing new insights into how instruction and student demographics shape interaction networks.

Findings

Remote lecture networks form large clusters of students.

Remote lab networks consist of smaller, more isolated clusters.

Network structures are influenced by instructional context and student demographics.

Abstract

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.