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Using networks to relate student interactions to their recognition of strong peers

Cornell Affiliated Author(s)

Author

Meagan Sundstrom
Lee Simpfendoerfer
Annie Tan
Natasha Holmes

Abstract

Gaining recognition from peers has been shown to improve student persistence and career intentions in physics. It is important, therefore, to understand how students develop perceptions of their peers. Prior research suggests that interactions are one possible mechanism for peer recognition: interacting with others allows students to demonstrate their physics skills and knowledge and acquire recognition as a physicist. To probe this explanation directly, we use methods from social network analysis to compare students' self-reported interactions to their recognition of strong peers. We find that there is a significant correlation between edges in the interaction and recognition networks, however interactions only account for about half of students' recognition of strong peers. Results suggest that students determine their strong peers by both (i) choosing the strongest of the peers with whom they interact and (ii) indirectly observing other peers with whom they do not interact. We will also discuss how these two processes help explain the formation of biases (e.g., based on gender) in peer recognition.

Date Published

Conference Name

APS April Meeting 2023

URL

https://ui.adsabs.harvard.edu/abs/2023APS..APRM16003S/abstract

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