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Social network data from teacher leader development.

ABSTRACT: Social network analysis can draw upon surveys and discussions to generate quantitative and qualitative data. We describe network data generated via a social network survey and discussion activity with high school science teachers in a teacher leadership development program. Data include social network maps related to seeking expertise in teaching content and/or pedagogy, disaggregated by contacts at the school, district, state, nation, and international spheres of influence. Data also include transcripts of the activity and teacher discussions of networks in their own educational settings. This data article is related to the research article, "The use of visual network scales in teacher leader development" Polizzi et al., 2019, where data interpretation can be found.

SUBMITTER: Polizzi SJ

PROVIDER: S-EPMC6617162 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Social network data from teacher leader development.

Polizzi Samuel J SJ Ofem Brandon B Coyle William W Lundquist Keith K Rushton Gregory T GT

Data in brief 20190624

Social network analysis can draw upon surveys and discussions to generate quantitative and qualitative data. We describe network data generated via a social network survey and discussion activity with high school science teachers in a teacher leadership development program. Data include social network maps related to seeking expertise in teaching content and/or pedagogy, disaggregated by contacts at the school, district, state, nation, and international spheres of influence. Data also include tr ...[more]

PMID: 31334315

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Project description:Understanding the relationship between individuals' social networks and health could help devise public health interventions for reducing incidence of unhealthy behaviors or increasing prevalence of healthy ones. In this context, we explore the co-evolution of individuals' social network positions and physical activities. We are able to do so because the NetHealth study at the University of Notre Dame has generated both high-resolution longitudinal social network (e.g., SMS) data and high-resolution longitudinal health-related behavioral (e.g., Fitbit physical activity) data. We examine trait differences between (i) users whose social network positions (i.e., centralities) change over time versus those whose centralities remain stable, (ii) users whose Fitbit physical activities change over time versus those whose physical activities remain stable, and (iii) users whose centralities and their physical activities co-evolve, i.e., correlate with each other over time. We find that centralities of a majority of all nodes change with time. These users do not show any trait difference compared to time-stable users. However, if out of all users whose centralities change with time we focus on those whose physical activities also change with time, then the resulting users are more likely to be introverted than time-stable users. Moreover, users whose centralities and physical activities both change with time and whose evolving centralities are significantly correlated (i.e., co-evolve) with evolving physical activities are more likely to be introverted as well as anxious compared to those users who are time-stable and do not have a co-evolution relationship. Our network analysis framework reveals several links between individuals' social network structure, health-related behaviors, and the other (e.g., personality) traits. In the future, our study could lead to development of a predictive model of social network structure from behavioral/trait information and vice versa.

Dataset Information

Social network data from teacher leader development.

Publications

Social network data from teacher leader development.

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