Who’s in Your School Learning Community Network?

1. Who’s in Your SchoolLearning CommunityNetwork? Barbara Schultz-Jones, PhD Department of Library and Information Sciences College of Information University of North Texas Denton, TX ESC Region XIVirtual Technology Conference November 10, 2009

2. Agenda • Show me your network • Background • Social network theory • Social network analysis • Texas schools • Constructing a social network • Applications of this approach Schultz-Jones / ESC XI

3. Social Networking Schultz-Jones / ESC XI

4. Background • The application of social network theory to the study of groups and group dynamics has its roots in the 1930s and the formulation of sociometry (Moreno, 1934). • Textile metaphors of fabric and web were used to describe interweaving relations of social action (1950 – 1970) • Diverse traditions culminated in the current use of social network analysis: anthropology, psychology, sociology and mathematics. Schultz-Jones / ESC XI

5. Social network theory • Seeks to explain the workings of networks • Small-world method (Milgram, 1967) • 6 degrees of separation (the Kevin Bacon Game) • Two prominent network properties provide a framework for viewing network behavior: • the strength of weak ties (Granovetter, 1973, 1983) • structural holes (Burt, 1992) Schultz-Jones / ESC XI

6. Social network example Schultz-Jones / ESC XI

7. Social network analysis • The methodology used to research network behavior • The network diagram, or sociogram, is a crucial means to demonstrate and illustrate the concepts, despite the limitations to its use by the difficulties of illustrating networks of high density. • In order to apply the concepts regarding the behavior of networks it is essential to identify the roles and positions of the members of the network. • The members of a network may be people, things or concepts depending on the focus of the analysis. Schultz-Jones / ESC XI

8. Who uses this approach? • Seven disciplines: • business and management • computer science • humanities • information science • medicine and health • sciences • social sciences Schultz-Jones / ESC XI

9. Network approaches • Citation analysis • Diffusion of information • Information flow • Degree of contact/interaction • Role and position analysis Schultz-Jones / ESC XI

10. How does this apply to the school learning environment? • Demonstrated levels of connectivity: • Between individuals • Within and between departments • Assessment tool for group interaction • Analysis tool for students Schultz-Jones / ESC XI

11. Frequency of Interaction SLMS 1 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

12. Level of Interaction SLMS 1 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

13. Frequency of InteractionSLMS 2 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

14. Level of InteractionSLMS 2 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

15. Frequency of InteractionSLMS 3 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

16. Level of InteractionSLMS 3 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

17. Frequency of InteractionSLMS 4 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

18. Level of InteractionSLMS 4 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

19. Frequency of InteractionSLMS 5 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

20. Level of InteractionSLMS 5 Blue – Language Arts Green – Math/Science Purple – History/Foreign Lang. Yellow – Administration Red - SLMS Schultz-Jones / ESC XI

21. Frequency of Interaction2 Schools - Science Schultz-Jones / ESC XI

22. Terminology • Network: an interconnected system • Node/actor/social entity: “discrete individual, corporate or collective social units” (Wasserman & Faust, 1999, p.17) • Level of analysis/discussion: • Egocentric: single node as the focus of attention • Whole: consideration of all nodes in the environment • Ties: the relationship connection between pairs of nodes/actors/entities: • Content: the resource shared, delivered or exchanged • Directed/Asymmetrical: content flows in one direction • Reciprocal/Symmetrical: content flows in both directions • Undirected: physically proximate but no exchange, or the exchange is not considered relevant to the research question • Strong: close association, based on the research context • Weak: distant association, based on the research context Schultz-Jones / ESC XI

23. How is data gathered? • Social network map – an instrument developed by Todd (cited in Curtis, 1979) • Surveys and interviews – personal or group network surveys that identify information exchange connections (Cross & Parker, 2003) • Agent-based technology to capture email and document flow across servers • Metrics of journals, authors, citations, co-citations, websites, online community positions Schultz-Jones / ESC XI

24. How is the data analyzed? • Construct a matrix identifying connections between nodes/actors/individuals Schultz-Jones / ESC XI

25. How is the data analyzed? • Employ software programs: • GraphPlot: a spreadsheet and a drawing tool for sociometric data • KrackPlot: a network graphics computer program. • Social Network Analysis Functional Utility (SNAFU): MacOS network analysis and algorithm development software • Social Network Visualizer for Linux (SocNetV): a GNU program for Linux OS to visualize graphically and play with social networks • UCINET: a general program designed to facilitate the analysis of social network data (Borgatti & Freeman, 2002) • http://www.analytictech.com/networks/ • Pajek: a network drawing package; large density networks Schultz-Jones / ESC XI

26. Practical Demonstration • Sign-up sheet of attendees • Distribute list and ask each attendee to identify if they have met any other attendees • Compile results in a matrix • Input matrix to UCINET software program • Produce sociogram of attending network • Discuss results Schultz-Jones / ESC XI

27. Classroom applications • Math • Calculate distances between contacts • Science • Map the connections between countries and animal species • English • Map the connections between authors (Shakespeare, for example), and derivative works (the movie Shakespeare in Love, for example). Schultz-Jones / ESC XI

28. Future applications • Within a subject area • Within a school • Within a district • Within a state • Within a region • Anywhere the degree or frequency of connectivity is important Schultz-Jones / ESC XI

29. Thank You! If you have any interest in exploring future applications of social network analysis Please contact me: Barbara.Schultz-Jones@unt.edu Schultz-Jones / ESC XI

30. References Borgatti, S.P., Everett, M.G. & Freeman, L.C. (2002). Ucinet for Windows: Software for social network analysis. Harvard, MA: Analytic Technologies. Burt, R.S. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press. Cross, R. & Parker, A. (2003). The hidden power of social networks: Understanding how work really gets done in organizations. Boston, MA: Harvard Business School Press. Granovetter, M.S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360-1380. Schultz-Jones / ESC XI

31. References (cont.) Granovetter, M.S. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1, 201-233. Moreno, J.L. (1934). Who shall survive? New York: Beacon Press. Schultz-Jones, B. (2009). Collaboration in the school social network: School library media specialists connect. Knowledge Quest, 37(4), 20-25. Wasserman, S. & Faust, K. (1999). Social network analysis: Methods and applications. New York: Cambridge University Press. Schultz-Jones / ESC XI