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Virtual Neighborhoods Architecture of Online Communities

Virtual Neighborhoods Architecture of Online Communities . Reuven Aviv Zippy Erlich Gilad Ravid gilad@ravid.org http://www.ravid.org/gilad. Agenda. Introduction Design, Mechanisms, Architecture Method Results. Design of network. mechanisms. Social Interdependence theory.

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Virtual Neighborhoods Architecture of Online Communities

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  1. Virtual Neighborhoods Architecture of Online Communities Reuven Aviv Zippy Erlich Gilad Ravid gilad@ravid.org http://www.ravid.org/gilad

  2. Agenda • Introduction • Design, Mechanisms, Architecture • Method • Results

  3. Design of network mechanisms Social Interdependence theory Matching the predictions of network emergence theories Architecture Content Analysis Network statistical analysis of Markov models Collaborative Knowledge construction

  4. Global SNA Macro Cohesiveness Equivalence (role groups) Power of actors Range of influence Brokerages Local SNA Micro Statistical Dyads and triads SNA viewpoints Aviv, R., Erlich, Z., Ravid, G., & Geva, A. (2003). Network Analysis of Knowledge Construction in Asynchronous Learning Networks. Journal of Asynchronous Learning Networks

  5. The Social Capital Mechanism • Hunt for Knowledge (social capital) • Using efficient interactions • E.g. bridging others • Community works with broadcast medium: • Most efficient connection: No interactions • Passive members (Lurkers)

  6. Cognitive balance Emergence of transitive triads cohesiveness cliques Creation of knowledge support

  7. The architecture of a network can be describes in terms of three components • One or more relations are the fundamental glue between the actors • A partition of the actors and the relations into 2 level hierarchy of groups of actors • A set of mechanisms shaping the relations to the creation of the neighborhood

  8. link In star Mutual dyad Mix star Out star Virtual Neighborhoods Transitive triad Cyclic triad

  9. Method of Analysis • Reveal Architectural Components • Identify Relevant Theories • Identify Mechanisms

  10. Method • Analyze the recorded responsiveness data of two online forums of learners with different design

  11. Example: Two Communities • 16 weeks each; 19, 18 participants • Parts of Open U “Business Ethics” Course • Team community • Designed for Knowledge Construction • Tested positively by Content Analysis • Forum Community • Designed for support by Q & A

  12. Social Capital & Transaction Costs • Burt 1992, 2002 • Bridge over Holes with minimal cost • Few single links • link<0 • Supported for both networks

  13. Collective action • Coleman, 1973, 1986; Marwell & Oliver 1993; Fulk et al. 1996 • Inducements to contribute under peer pressure • Respond to several others • If large density & centralization & size then out star > 0 • Supported for team network. • Not supported for forum network because condition in not fulfilled

  14. Exchange • Willer & Skvoretz, 1997; Hommans, 1958 • Exchange resources directly, depending on partner & network status • Tendency to reciprocate to resource promising partners • mutuality > 0 • Not supported for team network because there are no a-priory resource promising actors • supported for Forum network because Tutor is a-priory resource promising actors

  15. Generalized exchange • Bearman, 1997 • Exchange resources via mediators, depending on partner & network states • Tendency to respond cyclically to resource promising partner • cyclicity > 0 • Not supported in both networks. Probably because no need for information exchange via mediators

  16. Contagion Exposure • Burt 1980; Contractor et al., 1990 • Leading to social influence & limitation in attitudes, knowledge & behavior • Respond to same as other equivalent actors • Out star > 0; in star > 0; mixed star >0; transitivity >0 • Not supported in both networks. Probably because contagion process could not develop in the short lifetime of networks

  17. Cognitive Consistency • Heider 1958; Festinger, 1957; Cartwright et al., 1956 • Drive for balance in cognitions • Respond via several paths • transitivity > 0 • Supported in team networks. Not supported in the forum network. In both networks this is due to their designs

  18. Uncertainty reduction • Berger 1987 • Reduce uncertainty by gaining • Attract responses from several others • In star > 0 • Not supported in both networks. In the forum network the tutor clarified all uncertainties

  19. Exogenous factors • Residual personal tendencies o respond or trigger only to actors with pre assigned roles • For students (1)resp=0; (2)trigg=0; For tutor (3)resp>0; (4)trigg>0 • 1,2 supported for both networks; 3 un supported for team network, supported for forum network; 4 un supported for both networks

  20. Thank You Questions? Comments? Remarks ?

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