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STEM learning communities promote friendships but risk academic segmentation
Universities are increasingly using learning communities (LCs) to promote the academic and social integration of entering students, especially within STEM majors. Examining the causal effect of LCs on student networks is necessary to understand the nature and scope of their impact. This study combin...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9300596/ https://www.ncbi.nlm.nih.gov/pubmed/35858939 http://dx.doi.org/10.1038/s41598-022-15575-y |
Sumario: | Universities are increasingly using learning communities (LCs) to promote the academic and social integration of entering students, especially within STEM majors. Examining the causal effect of LCs on student networks is necessary to understand the nature and scope of their impact. This study combines a regression discontinuity design with social network analysis to estimate the effect of a simple LC design on the size, strength, structure, and composition of friendship networks among students within the same biological sciences freshman cohort. Results of the quasi-experimental analysis indicate that LC participants acquired one additional friend in the major and increased their share of friends in the LC by 54 percentage-points. Exponential random-graph models that test mediation and alternative friendship mechanisms provide support for the theoretical argument that the LC promoted friendship development by structuring opportunities for interaction through block-registration into courses. Thus, this study shows that even simple LCs can shape the development of friendships through relatively low-cost administrative means. The increased access to resources and support facilitated by the LC is likely beneficial for participating students. However, there is a potential downside when eligibility for participation is determined using academic metrics that separate the student population into distinct classroom environments. |
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