2. 2003, OrlandoAviv, Network Analysis1 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

3. 2003, OrlandoAviv, Network Analysis2 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

4. 2003, OrlandoAviv, Network Analysis3 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

5. 2003, OrlandoAviv, Network Analysis4 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.asphttp://www.aln.org/publications/jaln/v7n3/v7n3 _aviv.asp Analysis of Markov Stochastic Models: –Forthcoming

6. 2003, OrlandoAviv, Network Analysis5 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

7. 2003, OrlandoAviv, Network Analysis6 Design Parameters Of the two ALNs Structured ALN un-structured ALN RegistrationYesNo Cooperation commitmentYesNo Goal - directed schedulingYesNot relevant Predefined Work ProcedureYesNo Resource InterdependenceYesNo Work InterdependenceYesNo Reward mechanismYesNo Reward InterdependenceNoNot relevant Pre-assigned rolesNo Reflection proceduresNo Individual AccountabilityYesNot relevant

8. 2003, OrlandoAviv, Network Analysis7 Level Content Analysis via Gunawardena Model Structured ALN un- Structured ALN IExplain Concepts3870 IIArgue dissonances34 IIISynthesis & Judge28 IVTest to theory143 VReflection5 Structured ALN Reached High Level (4) of Knowledge Construction Un Structured ALN reached level 1

9. 2003, OrlandoAviv, Network Analysis8 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)

10. 2003, OrlandoAviv, Network Analysis9 Output of Network Analysis: macro-structures Cohesion analysis –cliques of participants Position (power) analysis –distributions of triggering & responsiveness powers Role cluster analysis –role groups

11. 2003, OrlandoAviv, Network Analysis10 Cohesion Analysis tutor Structured ALN Un structured ALN Structured ALN: many cohesive macro-structures with many bridging participants

12. 2003, OrlandoAviv, Network Analysis11 Power Analysis: responders maps Structured ALNUn-Structured ALN Structured ALN: Responsiveness power is distributed between many participants

13. 2003, OrlandoAviv, Network Analysis12 Role Cluster Analysis Structured ALN Un Structured ALN [responder] [lurkers] tutor students [responders] [triggers] tutor [lurkers] Structured ALN: multiple roles distributed between large groups of participants

14. 2003, OrlandoAviv, Network Analysis13 Evolution of Cliques (structured ALN) 1 2 3 4 TIME Network Structures develop in early stages

15. 2003, OrlandoAviv, Network Analysis14 Evolution of Power (structured ALN) 1 2 3 4 TIME 12 3 4 Network Structures develop in early stages

16. 2003, OrlandoAviv, Network Analysis15 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 )

17. 2003, OrlandoAviv, Network Analysis16 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

18. 2003, OrlandoAviv, Network Analysis17 Markov Model Variables neighborhoodDependent Responses Effect (Individual / global) Explanatory z N (r) i responsiveness (i→j) fixed i i responsiveness R i (r) =  j r ij i triggering (j→i) fixed i i triggerring T i (r) =  j r ji All pairs {i, j} (i→j) OR (j→i) Pairing tendency P(r)  i  j r ij all mutual (i→j) AND (j→i) mutuality M(r)  i  j r ij r ji all 2 out-stars (i→j) AND (i→k) Multi- responsiveness OS 2 (r)  i  j  k r ij r ik all 2 in-stars (i→j) AND (k→j) Multi-triggering IS 2 (r)  i  j  k r ij r kj all 2 mix-stars (i→j) AND (j→k) response & triggering MS 2 (r)   i  j  k r ij r jk All transitive triads (i→j) AND (j→k) AND (i→k) transitivity TRT(r)  i  j  k r ij r jk All cyclic triads (i→j) AND (j→k) AND (k→i) cyclicity CYT(r)  i  j  k r ij r jk

19. 2003, OrlandoAviv, Network Analysis18 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

20. 2003, OrlandoAviv, Network Analysis19 Results: What Effects the Response Relation? Structured ALNUn-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

21. 2003, OrlandoAviv, Network Analysis20 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

22. 2003, OrlandoAviv, Network Analysis21 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

23. 2003, OrlandoAviv, Network Analysis22 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)

24. 2003, OrlandoAviv, Network Analysis23 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 …

25. 2003, OrlandoAviv, Network Analysis24 Thank You