Understanding interaction network formation across instructional contexts in remote physics courses

Sundstrom, Meagan; Schang, Andy; Heim, Ashley B.; Holmes, N. G.

doi:10.1103/physrevphyseducres.18.020141

Cited by 6 publications

(10 citation statements)

References 69 publications

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“…For each network, we measured four different network statistics to describe the overall structure, similar to those used in prior work [20,29]:…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the survey was given once in the middle of the 15 week semester, after students had completed at least one assessment in both the lab and lecture courses. The survey asked students to list peers in each of their physics courses (lab and lecture) with whom they recently had a meaningful interaction using a survey prompt adapted from prior work [20,21,26,29,39]. For each peer that they listed, students were also asked to explain what aspects of the course material they discussed with that peer.…”

Section: Data Collectionmentioning

confidence: 99%

“…Many prior studies have performed quantitative analyses of student interactions in physics courses, largely drawing on methods of social network analysis to both visualize and quantify peer interactions [4,5,[7][8][9][10][19][20][21][22][23][24][25][26][27][28][29]. The subset of these studies that examine peer interactions as related to student performance have found that students who have more and/or stronger (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…more meaningful, more frequent, or more prolonged) interaction connections to their peers tend to earn higher grades [5-10, 17, 29, 30]. This correlation has been observed between students' course-wide interactions and their overall course grades [5][6][7][8][9][10]29] as well as between students' lab-specific interactions and their lab grades [17,30].…”

Section: Introductionmentioning

confidence: 99%

“…We also disentangle the relationships between interaction topics and student performance in the instructional contexts of lab and lecture separately, motivated by our prior work [29]. Given the different learning objectives, course structures, and grading schemes of these two contexts, we hypothesize that students may interact with peers about different topics in each context and that the relationship between interacting about certain topics and performing well in the course may vary between contexts.…”

Section: Introductionmentioning

confidence: 99%

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What topics of peer interactions correlate with student performance in physics courses?

Simpfendoerfer,

Sundstrom,

Dew

et al. 2024

Eur. J. Phys.

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Research suggests that interacting with more peers about physics course material is correlated with higher student performance. Some studies, however, have demonstrated that different topics of peer interactions may correlate with their performance in different ways, or possibly not at all. In this study, we probe both the peers with whom students interact about their physics course and the particular aspects of the course material about which they interacted in six different introductory physics courses: four lecture courses and two lab courses. Drawing on methods in social network analysis, we replicate prior work demonstrating that, on average, students who interact with more peers in their physics courses have higher final course grades. Expanding on this result, we find that students discuss a wide range of aspects of course material with their peers: concepts, small-group work, assessments, lecture, and homework. We observe that in the lecture courses, interacting with peers about concepts is most strongly correlated with final course grade, with smaller correlations also arising for small-group work and homework. In the lab courses, on the other hand, small-group work is the only interaction topic that significantly correlates with final course grade. We use these findings to discuss how course structures (e.g., grading schemes and weekly course schedules) may shape student interactions and add nuance to prior work by identifying how specific types of student interactions are associated (or not) with performance.

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“…For each network, we measured four different network statistics to describe the overall structure, similar to those used in prior work [20,29]:…”

Section: Discussionmentioning

confidence: 99%