The document analyzes the network structures of two asynchronous learning networks (ALNs) using social network analysis and Markov stochastic models. For the structured ALN, it found cohesive macro-structures with distributed roles and powers. Micro-structure analysis found effects of transitivity and multiple responses, aligned with balance theory and collective action theory. For the unstructured ALN, tutor responsiveness and mutuality effects were found, aligned with pre-assigned role and social exchange theories. Both ALNs showed a negative tendency for response pairing, aligned with social capital network theory.

1. Network Analysis of Effective Knowledge Construction In Asynchronous Learning Networks 9’th ALN/SLOAN-C Conference November 14-16, 2003, Orlando Dr. Reuven Aviv Dr. Zippy Erlich Gilad Ravid Open University of Israel

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

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

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

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

6. Not relevant Yes Individual Accountability No No Reflection procedures No No Pre-assigned roles Not relevant No Reward Interdependence No Yes Reward mechanism No Yes Work Interdependence No Yes Resource Interdependence No Yes Predefined Work Procedure Not relevant Yes Goal - directed scheduling No Yes Cooperation commitment No Yes Registration un-structured ALN Structured ALN Design Parameters Of the two ALNs

7. Structured ALN Reached High Level (4) of Knowledge Construction Un Structured ALN reached level 1 5 Reflection V 143 Test to theory IV 28 Synthesis & Judge III 34 Argue dissonances II 70 38 Explain Concepts I un- Structured ALN Structured ALN Content Analysis via Gunawardena Model Level

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)

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

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

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

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

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

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

15. Stochastic Model for Response Relation Responses result from stochastic processes, R i,j {r}: possible set of responses states, r i, 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{  N  N •z N (r)}/k(  )  N z N (r): effect of neighborhood N sum over neighborhoods ( Hamersley Clifford )

16. Markov Model: micro-neighborhoods Markov: dependent respones ↔ common actor Examples: mutual , triad , star-shape responses Explanatory variable: z N (r) =  (i -> j)  N r ij product is over all (i -> j) in neighborhood N Non Zero only if neighborhood completely responsive  N parameter strength of effect of neighborhood N

17. Markov Model Variables T i (r) =  j r ji i trigg erring (j->i) fixed i i triggering R i ( r ) =  j r ij i resp onsiveness (i->j) fixed i i responsiveness CYT(r )  i  j  k r ij r jk cyclicity (i->j) AND (j->k) AND (k->i) All cyclic triads TRT(r )  i  j  k r ij r jk transitivity (i->j) AND (j->k) AND (i->k) All transitive triads MS 2 ( r )  i  j  k r ij r jk response & triggering (i->j) AND (j->k) all 2 mix-stars IS 2 ( r )  i  j  k r ij r kj Multi-triggering (i->j) AND (k->j) all 2 in-stars OS 2 ( r )  i  j  k r ij r ik Multi-responsiveness (i->j) AND (i->k) all 2 out-stars M(r )  i  j r ij r ji mutuality (i->j) AND (j->i) all mutual P(r )  i  j r ij Pairing tendency (i->j) OR (j->i) All pairs {i, j} neighborhood Explanatory z N (r ) Effect (Individual / global) Dependent Responses

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: R i , T i responsiveness and triggering of actors Result: Relative importance of explanatories  micro-structures (effects)  theories

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

20. Theoretical Foundations Both ALNs: Negative tendency for pairing Theory of Social Capital (network holes) Minimize effort to gain maximal knowledge Structured ALN transitivity and multi-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

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

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: Tutor responsiveness (Pre-assigned role) mutuality (social exchange theory)

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 …

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