Zeitschrift für Erziehungswissenschaft

, Volume 17, Supplement 5, pp 117–134 | Cite as

Social network analysis of the influences of educational reforms on teachers’ practices and interactions



In this chapter we present social network analysis in the context of recent educational reforms concerning teachers’ instructional practices. Teachers are critical to the implementation of educational reforms, and teacher networks are important because teachers draw on local knowledge and conform to local norms as they implement new practices. We describe three social network approaches. First, we graphically represent network data to characterize the network structure through which information and knowledge about reforms might diffuse. Second, we use social influence models to express how teachers’ beliefs or behaviors are affected by others with whom they interact. Third, we use social selection models to express how teachers might select with whom to engage in interactions about reforms. We discuss the implications for scientific dialogue, and for informing educational policy studies and the practice of educational policy makers and school administrators.


Teacher networks Reform Implementation influence Statistical models 

Sozial Netzwerkanalyse der Einflüsse von Bildungsreformen auf die Lehrpraxis und Interaktionen von Lehrkräften


In diesem Kapitel präsentieren wir die Soziale Netzwerkanalyse im Kontext aktueller Bildungsreformen, die sich auf Instruktionspraktiken von Lehrpersonen beziehen. Lehrpersonen spielen für die Implementation von Bildungsformen eine zentrale Rolle. Soziale Netzwerke von Lehrpersonen sind insofern von hoher Bedeutung, als Lehrpersonen im Zuge der Implikation neuer Praktiken auf lokales Wissen und lokale Normen zurückgreifen. Wir beschreiben drei netzwerkanalytische Ansätze: Erstens präsentieren wir Netzwerkdaten graphisch, um die Struktur des Netzwerkes zu charakterisieren, durch die Information und Wissen über die Reform verbreitet werden. Zweitens verwenden wir soziale Einflussmodelle, um darzustellen, wie Überzeugungen und Verhalten von Lehrpersonen von denjenigen Lehrpersonen beeinflusst werden, mit denen sie interagieren. Drittens verwenden wir soziale Selektionsmodelle, um darzustellen, wie Lehrpersonen die Personen auswählen, mit denen sie die Reform betreffend interagieren. Wir diskutieren Implikationen für den wissenschaftlichen Dialog, die Bedeutung für bildungspolitische Studien sowie die praktische Bedeutung für bildungspolitische Akteure und Schulangestellte.


Lehrpersonen Netzwerke Reform Implementierung Einfluss Statistische Modellierung 


  1. Brown, D. G., Page, S. E., Riolo, R., Zellner, M., & Rand, W. (2005). Path dependence and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science, 19(2), 153–174.CrossRefGoogle Scholar
  2. Christakis, N., & Fowler, J. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357, 370–379.CrossRefGoogle Scholar
  3. Christakis, N., & Fowler, J. (2008). The collective dynamics of smoking in a large social network. The New England Journal of Medicine, 358, 249–258.CrossRefGoogle Scholar
  4. Coburn, C. E., & Russell, J. L. (2008). District policy and teachers’ social networks. Educational Evaluation and Policy Analysis, 30(3), 203–235.CrossRefGoogle Scholar
  5. Coburn, C. E., Choi, L., & Mata, W. (2010). I would go to her because her mind is math: Network formation in the context of mathematics reform. In A. J. Daly (Ed.), Social network theory and educational change (pp. 33–50). Cambridge: Harvard Educational Press.Google Scholar
  6. Coburn, C. E., Russell, J. L., Kaufman, J., & Stein, M. K. (2012). Supporting sustainability: Teachers’ advice networks and ambitious instructional reform. American Journal of Education, 119(1), 137–182.CrossRefGoogle Scholar
  7. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum.Google Scholar
  8. Cohen, D. K., Raudenbush, S. W., & Ball, D. L. (2003). Resources, instruction, and research. Educational Evaluation and Policy Analysis, 25(2), 119–142.CrossRefGoogle Scholar
  9. Cohen-Cole, E., & Fletcher, J. M. (2008a). Detecting implausible social network effects in acne, height, and headaches: Longitudinal analysis. British Medical Journal, 337, 2533–2537.CrossRefGoogle Scholar
  10. Cohen-Cole, E., & Fletcher, J. M. (2008b). Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic. Journal of Health Economics, 27(5), 1382–1387.CrossRefGoogle Scholar
  11. Cole, R. P., & Weinbaum, E. H. (2010). Changes in attitude: Peer influence in high school reform. In A. J. Daly (Ed.), Social network theory and educational change (pp. 77–95). Cambridge: Harvard Educational Press.Google Scholar
  12. Datnow, A. (2012). Teacher agency in educational reform: Lessons from social networks research. American Journal of Education, 119(1), 193–201.CrossRefGoogle Scholar
  13. Field, S., *Frank, K. A., Schiller, K., Riegle-Crumb, C., & Muller, C. (2006). Identifying Social Contexts in Affiliation Networks: Preserving the Duality of People and Events. Social Networks, 28, 97–123. (* co first authors).CrossRefGoogle Scholar
  14. Finnigan, K. S., & Daly, A. J. (2012). Mind the gap: Organizational learning and improvement in an underperforming urban system. American Journal of Education, 119(1), 41–71.CrossRefGoogle Scholar
  15. Frank, K. A. (1995). Identifying cohesive subgroups. Social Networks, 17, 27–56.CrossRefGoogle Scholar
  16. Frank, K. A. (1996). Mapping interactions within and between cohesive subgroups. Social Networks, 18, 93–119.CrossRefGoogle Scholar
  17. Frank, K. A. (1998). The social context of schooling: Quantitative methods. Review of Research in Education, 23, 171–216.Google Scholar
  18. Frank, K. A. (2009). Quasi-ties: Directing resources to members of a collective. American Behavioral Scientist, 52, 1613–1645.CrossRefGoogle Scholar
  19. Frank, K. A., & Fahrbach, K. (1999). Organizational culture as a complex system: Balance and information in models of influence and selection. Organization Science, 10(3), 253–277.CrossRefGoogle Scholar
  20. Frank, K. A., & Zhao, Y. (2005). Subgroups as a meso-level entity in the social organization of schools. In L. Hedges & B. Schneider (Eds.), Social organization of schools (pp. 279–318). New York: Sage Publications.Google Scholar
  21. Frank, K. A., Zhao, Y., & Borman, K. (2004). Social capital and the diffusion of innovations within organizations: Application to the implementation of computer technology in schools. Sociology of Education, 77, 148–171.CrossRefGoogle Scholar
  22. Frank, K. A., Muller, C., Schiller, K., Riegle-Crumb, C., Strassman-Muller, A., Crosnoe, R., & Pearson J. (2008a). The social dynamics of mathematics course taking in high school. American Journal of Sociology, 113(6), 1645–1696.CrossRefGoogle Scholar
  23. Frank, K. A., Sykes, G., Anagnostopoulos, D., Cannata, M., Chard, L., Krause, A., & McCrory, R. (2008b). Extended influence: National board certified teachers as help providers. Education, Evaluation, and Policy Analysis, 30(1), 3–30.CrossRefGoogle Scholar
  24. Frank, K. A., Kim, C., & Belman, D. (2010). Utility theory, social networks, and teacher decision making. In A. J. Daly (Ed.), Social network theory and educational change (pp. 223–242). Cambridge: Harvard University Press.Google Scholar
  25. Frank, K. A., Zhao, Y., Penuel, W. R., Ellefson, N. C., & Porter, S. (2011). Focus, fiddle and friends: Sources of knowledge to perform the complex task of teaching. Sociology of Education, 84(2), 137–156.CrossRefGoogle Scholar
  26. Frank, K.A., Penuel, W.R., Sun, M., Kim, C., & Singleton, C. (2013a). The organization as a filter of institutional diffusion. Teacher’s College Record. 115(1), 306–339.Google Scholar
  27. Frank, K. A., Penuel, W. R., & Krause, A. (2013b). What is a “good” social network for a system? Knowledge flow and organizational change. Paper presented at the annual meeting of Association for Public Policy Analysis and Management, Washington, DC, USA.Google Scholar
  28. Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239.CrossRefGoogle Scholar
  29. Friedkin, N. E., & Marsden, P. (1994). Network studies of social influence. In S. Wasserman & J. Galaskiewicz (Eds.), Advances in social network analysis (pp. 1–25). Thousand Oaks: Sage.Google Scholar
  30. Garrison Wilhelm, A., Chen, I., Frank, K.A., & Smith, R. (2014). Understanding Mathematics Teachers’ Advice-Seeking Networks. Paper presented at the Annual Meeting of the American Educational Research Association, Philadelphia, PA.Google Scholar
  31. Lazega, E., & van Duijn, M. (1997). Position in formal structure, personal characteristics and choices of advisors in a law firm: A logistic regression model for dyadic network data. Social Networks, 19, 375–397.CrossRefGoogle Scholar
  32. Leenders, R. (1995). Structure and influence: Statistical models for the dynamics of actor attributes, network structure and their interdependence. Amsterdam: Thesis Publishers.Google Scholar
  33. Lim, K., Deadman, P. J., Moran, E., Brondizio, E., & Mc-Cracken, S. (2002). Agent-based simulations of household decision-making and land use change near Altamira, Brazil. In H. R. Gimblett (Ed.), Integrating geographic information systems and agent-based techniques for simulating social and ecological processes (pp. 277–308). New York: Oxford University Press.Google Scholar
  34. Lyons, R. (2011). The spread of evidence-poor medicine via flawed social-network analysis. Statistics, Politics, and Policy, 2(1). Retrieved at http://arxiv.org/abs/1007.2876. Accessed 18 Feb. 2013.
  35. Maroulis, S., Guimera, R., Petry, H., Stringer, M J., Gomez, L., Amaral, L.A.N., & Wilensky, U. (2010). Complex systems view on educational policy research. Science, 330(6000), 38–39.CrossRefGoogle Scholar
  36. Marsden, P. V. (2005). Recent developments in network measurement. In P. J. Carrington, J. Scott, & S. Wasserman (Eds), Model and methods in social network analysis (pp. 8–30). New York: Cambridge University Press.CrossRefGoogle Scholar
  37. Moody, J., McFarland, D. A., & Bender-DeMoll, S. (2005). Dynamic network visualization: Methods for meaning with longitudinal network movies. American Journal of Sociology, 110, 1206–1241.CrossRefGoogle Scholar
  38. Moolenaar, N. M. (2012). A social network perspective on teacher collaboration in schools: Theory, methodology, and applications. American Journal of Education, 119(1), 7–39.CrossRefGoogle Scholar
  39. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37.CrossRefGoogle Scholar
  40. Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2), 314–337.CrossRefGoogle Scholar
  41. Penuel, W. R., Riel, M., Krause, A., & Frank, K. A. (2009). Analyzing teachers’ professional interactions in a school as social capital: A social network approach. Teachers College Record, 111(1), 124–163.Google Scholar
  42. Penuel, W. R., Riel, M., Joshi, A., & Frank, K. A. (2010). The alignment of the informal and formal supports for school reform: Implications for improving teaching in schools. Educational Administration Quarterly, 46(1), 57–95.CrossRefGoogle Scholar
  43. Penuel, W. R., Sun, M., Frank, K. A., & Gallagher, H. A. (2012). Using social network analysis to study how collegial interactions can augment teacher learning from external professional development. American Journal of Education, 119(1), 103–136.CrossRefGoogle Scholar
  44. Purkey, S. C., & Smith, M. S. (1983). Effective schools: A review. Elementary School Journal, 83(4), 427–452.CrossRefGoogle Scholar
  45. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks: Sage Publications.Google Scholar
  46. Rogers, E. M. (2010). Diffusion of innovations. New York: Simon and Schuster.Google Scholar
  47. Snijders, T. A. B., Pattison, P. E., Robins, G. L., & Handcock, M. S. (2006). New specifications for exponential random graph models. Sociological Methodology, 36(1), 99–153.CrossRefGoogle Scholar
  48. Spillane, J. P., & Kim, C. M. (2012). An exploratory analysis of formal school leaders’ positioning in instructional advice and information networks in elementary schools. American Journal of Education, 119(1), 73–102.CrossRefGoogle Scholar
  49. Spillane, J. P., Halverson, R. R., & Diamond, J. B. (2001). Investigating school leadership practice: A distributed perspective. Educational Researcher, 30, 23–27.CrossRefGoogle Scholar
  50. Spillane, J., Kim, C. M., & Frank, K. A. (2012). Instructional advice and information providing and receiving behavior in elementary schools: Exploring tie formation as a building block in social capital development. American Educational Research Journal, 49(6), 1112–1145.CrossRefGoogle Scholar
  51. Steglich, C., Snijders, T. A. B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40, 329–393CrossRefGoogle Scholar
  52. Sun, M. (2011). The use of multilevel item response theory modeling to estimate professional interactions among teachers (Unpublished doctoral dissertation). Michigan State University, East Lansing.Google Scholar
  53. Sun, M., Frank, K. A., Penuel, W., & Kim, C. M. (2013a). How external institutions penetrate schools through formal and informal leaders. Educational Administration Quarterly, 49(4), 610–644.CrossRefGoogle Scholar
  54. Sun, M., Penuel, W., Frank, K. A., Gallagher, A., & Youngs, P. (2013b). Shaping professional development to promote the diffusion of instructional expertise among teachers. Education, Evaluation and Policy Analysis, 35(3), 344–369.CrossRefGoogle Scholar
  55. Supovitz, J. A., Sirinides, P., & May, H. (2010). How principals and peers influence teaching and learning. Educational Administration Quarterly, 46, 31–56.CrossRefGoogle Scholar
  56. Tajfel, H, & J. C. Turner. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The Social Psychology of Intergroup Relations (pp. 33–47). Pacific Grove: Brooks-Cole.Google Scholar
  57. Tyack, D., & Cuban, L. (1995). Tinkering toward utopia: A century of school reform. Cambridge: Harvard University Press.Google Scholar
  58. Van Duijn, M. A. J. (1995). Estimation of a random effects model for directed graphs. In T. A. B. Snijders (Ed.), Symposium statistische software, nr. 7. toeval zit overal: Programmatuur voor random-coefficient modellen [Chance is omnipresent: Software for random coefficient models], (pp. 113–131). Groningen, iec ProGAMMA.Google Scholar
  59. Wilensky, U. (1999). NetLogo [Computer software]. Retrieved from http://ccl.northwestern.edu/netlogo. Accessed 12 Nov. 2013.
  60. Wilensky, U. (2001). Modeling nature’s emergent patterns with multi-agent languages. Paper presented at the EuroLogo, Linz, Austria. Nov. 12, 2013.Google Scholar
  61. Yasumoto, J. Y., Uekawa, K., & Bidwell, C. (2001). The collegial focus and student achievement: Consequences of high school faculty social organization for students on achievement in mathematics and science. Sociology of Education, 74, 181–209.CrossRefGoogle Scholar
  62. Youngs, P., Frank, K. A., Thum, Y. M., & Low, M. (2012). The motivation of teachers to produce human capital and conform to their social contexts. In T. Smith, L. Desimone, & A. C. Porter (Eds.), Organization and effectiveness of high-intensity induction programs for new teachers (pp. 248–272). Malden: Blackwell Publishing.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden 2014

Authors and Affiliations

  1. 1.Department of Counseling, Educational Psychology and Special EducationMichigan State UniversityEast LansingUSA
  2. 2.University of MichiganAnn ArborUSA
  3. 3.College of EducationUniversity of WashingtonSeattleUSA

Personalised recommendations