, Volume 62, Issue 1, pp 71–76 | Cite as

Identifying the Help Givers in a Community of Learners: Using Peer Reporting and Social Network Analysis as Strategies for Participant Selection

  • Michael M. RookEmail author
Original Paper


The author presents a three-step process for selecting participants for any study of a social phenomenon that occurs between people in locations and at times that are difficult to observe. The process is described with illustrative examples from a previous study of help giving in a community of learners. This paper includes a rationale for combining peer-reporting questionnaires with social network analysis to find the authorities among a community of pre-service teachers. Triangulation is recommended as a technique to verify results and ensure accurate findings from the questionnaires. Implications and limitations are provided to help facilitate a successful modification and adaptation to future studies.


Participant selection Peer reporting Social network analysis Peer assistance Help giving Pre-service teacher education Instructional technology Technology support 



The author is grateful for Drs. Kyle Peck and John Cowan for their contributions to the ideas presented in this paper. Peck helped to conceptualize the initial idea to use Malcolm Gladwell’s theories, and Cowan suggested social network analysis as a possible method for participant selection.


  1. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for windows: Software for social network analysis. Harvard: Analytic Technologies.Google Scholar
  2. Borgatti, S. P., & Foster, P. C. (2003). The network paradigm in organizational research: A review and typology. Journal of Management, 29(6), 991–1013.CrossRefGoogle Scholar
  3. Bush, M. D., & Mott, J. D. (2009). The transformation of learning with technology: Learner-centricity, content and tool malleability, and network effects. Educational Technology, 49(2), 3–20.Google Scholar
  4. Ellwardt, L., Labianca, G., & Wittek, R. (2012). Who are the objects of positive and negative gossip at work? A social network perspective on workplace gossip. Social Networks, 34, 193–205.CrossRefGoogle Scholar
  5. Flanagan, J. C. (1954). The critical incident technique. Psychological Bulletin, 51(4), 327.CrossRefGoogle Scholar
  6. Gladwell, M. (2000). The tipping point: How little things can make a big difference. New York: Little, Brown and Company Hachette Book Group.Google Scholar
  7. Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632.CrossRefGoogle Scholar
  8. Knoke, D., & Yang. S. (2008). Social network analysis (2nd ed.). Thousand Oaks: Sage Publications, Inc..Google Scholar
  9. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills: Sage Publications, Inc..Google Scholar
  10. Rook, M. M. (2014). Technohubs in teacher education: The lived experience of assisting peers with instructional technology issues. (doctoral dissertation). Retrieved from ProQuest. (3583396).Google Scholar
  11. Sykes, T. A., Venkatesh, V., & Gosain, S. (2009). Model of acceptance with peer support: A social network perspective to understand employees’ system use. MIS Quarterly, 33(2), 371–393.CrossRefGoogle Scholar
  12. Veres, M., & Carlsen, R. (2003). Using a knowledge hub to reach the Educational technology tipping point. In C. Crawford et al. (Eds.), Proceedings of Society for Information Technology & teacher education international conference 2003 (pp. 1673–1677). Chesapeake: AACE.Google Scholar
  13. Zaiane, O., & Goebel, R. (2002). Meerkat Lite. Alberta: Centre for Machine Learning.Google Scholar

Copyright information

© Association for Educational Communications & Technology 2017

Authors and Affiliations

  1. 1.Krause Innovation StudioPennsylvania State UniversityUniversity ParkUSA

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