Abstract
This chapter reviews the basic structures of social networks and how they have been used to study interrelationships in schools, most prominently those among teachers and students. Part of this discussion includes how network structures are visualized, with multiple examples. These graphic representations demonstrate how information flows in social organizations and is influenced by interactions with colleagues and personalized selections. One of the most important contributions of network analysis is the ability to visualize influence and how inferences of influence can be determined. Influence modeling shows how actors change behaviors in response to others. Selection models show how actors choose with whom they wish to interact and allocate their resources. Finally, this work shows how network forces can facilitate learning by creating opportunities and regulating specific practices. This is particularly beneficial for modeling interactions of teachers within schools and understanding how interactions among teachers and administrators create norms and conditions that can promote or impede reforms within schools. Teacher networks can be especially useful in the formation of learning communities and can enhance effective teaching. But networks also exist outside of school, and the final section of the chapter discusses the emergence of virtual social networks and how professionals are interacting and using them.
With contribution from Kim Jansen.
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Notes
- 1.
For a complete review of social networks in educational research, see Frank (1998); on teacher networks and the implementation of innovations, see Carolan (2013), Daly (2010), Yoon and Baker-Doyle (2018) and Frank et al. (2014); on teacher networks and collaboration, see Moolenaar (2012); and on network formation see McPherson et al. (2001). For a motivation of network analysis from utility theory and a guide to the application of social network analysis see Frank et al. (2010).
- 2.
- 3.
Available at https://msu.edu/~kenfrank/resources.htm#KliqueFinder.
- 4.
- 5.
Because the metrics varied slightly between administrations of the instrument, each measure of use was standardized and then the difference was taken from the standardized measures. Each ring represents an increase of .2 standardized units.
- 6.
The skilled-based instructional practices include that teachers read stories or other imaginative texts; practice dictation (teacher reads and students write down words) about something the students are interested in; use context and pictures to read words; blend sounds to make words or segment the sounds in words; clap or sound out syllables of words; drill and practice sight words (e.g., as part of a competition); use phonics-based or letter-sound relationships to read words in sentences; use sentence meaning and structure to read words; and practice letter-sound associations (see Frank et al. 2013b, pp. 318–319 for details).
- 7.
In this sense, the exposure term extends basic conceptualizations of centrality (e.g., Freeman 1978) because the exposure term is a function of the characteristics of the members of a network, whereas centrality is a function only of the structure of the network.
- 8.
See https://www.msu.edu/~kenfrank/resources.htm: influence models for SPSS, SAS, and STATA modules and PowerPoint demonstrations that calculate a network effect and include it in a regression model.
- 9.
Estimation of model (2) can be challenging because of dependencies among the network ties. Techniques that control for dependencies through random effects (Baerveldt et al. 2004; Hoff, 2005; Lazega and Van Duijn, 1997) as well as latent spaces (Hoff et al. 2002; Sweet et al. 2013) have encouraging potential, although we note the focus of Exponential Random Graph Models on a relatively small number of geometrically weighted terms may address some previous concerns about degeneracies in estimation (Hunter et al. 2008). See Frank and Xu (2018), for more discussion.
- 10.
- 11.
Given the recent emergence of the phenomenon, many of the studies we report on here are in early stages, such as conference presentations, but not yet published in peer review journals.
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Acknowledgement
We acknowledge the work of Zixi Chen, I-chien Chen, Angelo Garcia, Sihua Hu, Qinyun Lin, and Yuqing Liu for helping us identify and summarize literature reviewed.
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Frank, K., Lo, Yj., Torphy, K., Kim, J. (2018). Social Networks and Educational Opportunity. In: Schneider, B. (eds) Handbook of the Sociology of Education in the 21st Century. Handbooks of Sociology and Social Research. Springer, Cham. https://doi.org/10.1007/978-3-319-76694-2_13
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