Dr. Reuven Aviv Dr. Zippy Erlich Gilad Ravid Open University of Israel

1. Network Analysis of Effective Knowledge Construction InAsynchronous Learning Networks9’th ALN/SLOAN-C ConferenceNovember 14-16, 2003, Orlando Dr. Reuven Aviv Dr. Zippy Erlich Gilad Ravid Open University of Israel Aviv, Network Analysis

2. Content • Introduction: What this research is all about • Network Analysis of two ALNs • Macro-structures: Cohesion structures, Power Distribution and Role groups • Micro-structures: Markov Stochastic Models • Theories underlying the micro-structures • Conclusions, Limitations Aviv, Network Analysis

3. Research Questions and Techniques • What are the network macro-structures in a knowledge constructing ALN • Done by Social Network Analysis • What are the network micro-structures • By Analysis of Markov Stochastic Models • What are the theories underlying these micro-structures • Literature Search Aviv, Network Analysis

4. Details • Content Analysis and Social Network Analysis: • Journal of Asynchronous Learning Networks, (JALN) Vol. 7, Sept. 2003 • http://www.aln.org/publications/jaln/v7n3/v7n3_aviv.asp • Analysis of Markov Stochastic Models: • Forthcoming Aviv, Network Analysis

5. Test-bed: Two ALNs • 16 weeks each • 18, 17 participants • Parts of Open U “Business Ethics” Course • Structured ALN: Online Seminar • Design & Test for Knowledge Construction • un-Structured ALN: Q & A Aviv, Network Analysis

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7. Structured ALN Reached High Level (4) of Knowledge Construction • Un Structured ALN reached level 1 Aviv, Network Analysis

8. Response Network Analysis: Input intensity of response relation (i  j): number of responses from i to j (triggers of i by j)in recorded transcript of the ALN (4 months) Aviv, Network Analysis

9. Output of Network Analysis: macro-structures • Cohesion analysis • cliques of participants • Position (power) analysis • distributions oftriggering & responsiveness powers • Role cluster analysis • role groups Aviv, Network Analysis

10. Cohesion Analysis tutor tutor Un structured ALN Structured ALN • Structured ALN: many cohesive macro-structures with many bridging participants Aviv, Network Analysis

11. Power Analysis: responders maps Structured ALN Un-Structured ALN • Structured ALN: Responsiveness power is distributed between many participants Aviv, Network Analysis

12. Role Cluster Analysis [triggers] [responder] [responders] [lurkers] tutor tutor [lurkers] students Structured ALN Un Structured ALN • Structured ALN: multiple roles distributed between large groups of participants Aviv, Network Analysis

13. Evolution of Cliques (structured ALN) 1 2 3 4 TIME Network Structures develop in early stages Aviv, Network Analysis

14. Evolution of Power (structured ALN) 1 3 2 4 1 3 2 4 TIME Network Structures develop in early stages Aviv, Network Analysis

15. Stochastic Model for Response Relation • Responses result from stochastic processes, Ri,j • {r}: possible set of responses states, ri, j = 0, 1 • neighborhood: actors such that every pair of probabilities of responses are dependent • P(i→j; k→ l) ≠ P(i→ j)P(k→l) • P(r) = exp{SN N•zN(r)}/k() • N zN(r): effect of neighborhood N • sum over neighborhoods (Hamersley Clifford ) Aviv, Network Analysis

16. Markov Model: micro-neighborhoods • Markov: dependent respones ↔ common actor • Examples: mutual, triad, star-shape responses • Explanatory variable: zN(r) = P(i → j)eN rij • product is over all (i → j) in neighborhood N • Non Zero only if neighborhood completely responsive • N parameter • strength of effect of neighborhood N Aviv, Network Analysis

17. Markov Model Variables Aviv, Network Analysis

18. Logistic Regression • Cases: > g(g-1) actor-pairs (more then 300) • dependent Variable: Observed Response (1/0) • 43 (45) independent Explanatory Variables: • global variables: P, M, TRT, CYC, IS, OS, MS • pairing, mutuality, transitivity, cyclicity, in-stars, out-stars, mix-stars • 36 (38) individual variables: Ri, Ti • responsiveness and triggering of actors • Result: Relative importance of explanatories  micro-structures (effects)  theories Aviv, Network Analysis

19. Results: What Effects the Response Relation? Structured ALN Un-structured ALN 3 3 1 1 2 2 1. Global (negative) tendency for pairing 2. transitivity 3. out-stars (multi-responses) 2. tutor responsiveness 3. mutuality Aviv, Network Analysis

20. Theoretical Foundations • Both ALNs: Negative tendency for pairing • Theory of Social Capital (network holes) • Minimize effort to gain maximal knowledge • Structured ALN • transitivityandmulti-responses • Balance Theory: spread info in several paths • Theory of Collective Action: we sink or swim • Unstructured ALN • Tutor responsiveness: Pre-assigned role • mutuality: Social Exchange Theory Aviv, Network Analysis

21. Conclusions: Macro Structures • Macro-structures are developed in early stages • Macro-structures of Knowledge Constructing ALNs • mesh of interlinked cliques • Distributed Response & triggering power • roles groups • Triggers, responders, lurkers Aviv, Network Analysis

22. Conclusions: Micro-structures and Underlying effects • Major effect: • negative tendency for pairing • Minimize effort for maximum capital • Effects in Structured ALN: • transitivity (balance theory) • multiple responses (collective action theory) • Effects in un-structured ALN: • Tutorresponsiveness(Pre-assigned role) • mutuality(social exchange theory) Aviv, Network Analysis

23. Limitations • Only two ALNs • Only one relation (response) • Definitions of Network Structures are not standardized • Check stability of results with respect to redefinition of structures • Time dependence was not analyzed analytically • Markov model is limited to few effects • More … Aviv, Network Analysis

24. Thank You Aviv, Network Analysis