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  1. 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. 2. Content <ul><li>Introduction: What this research is all about </li></ul><ul><li>Network Analysis of two ALNs </li></ul><ul><ul><li>Macro-structures: Cohesion structures, Power Distribution and Role groups </li></ul></ul><ul><li>Micro-structures: Markov Stochastic Models </li></ul><ul><li>Theories underlying the micro-structures </li></ul><ul><li>Conclusions, Limitations </li></ul>
  3. 3. Research Questions and Techniques <ul><li>What are the network macro-structures in a knowledge constructing ALN </li></ul><ul><ul><li>Done by Social Network Analysis </li></ul></ul><ul><li>What are the network micro-structures </li></ul><ul><ul><li>By Analysis of Markov Stochastic Models </li></ul></ul><ul><li>What are the theories underlying these micro-structures </li></ul><ul><ul><li>Literature Search </li></ul></ul>
  4. 4. Details <ul><li>Content Analysis and Social Network Analysis: </li></ul><ul><li>Journal of Asynchronous Learning Networks, (JALN) Vol. 7, Sept. 2003 </li></ul><ul><ul><li>http://www.aln.org/publications/jaln/v7n3/v7n3_aviv.asp </li></ul></ul><ul><li>Analysis of Markov Stochastic Models: </li></ul><ul><ul><li>Forthcoming </li></ul></ul>
  5. 5. Test-bed: Two ALNs <ul><li>16 weeks each </li></ul><ul><li>18, 17 participants </li></ul><ul><li>Parts of Open U “Business Ethics” Course </li></ul><ul><li>Structured ALN: Online Seminar </li></ul><ul><ul><li>Design & Test for Knowledge Construction </li></ul></ul><ul><li>un-Structured ALN: Q & A </li></ul>
  6. 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. 7. <ul><li>Structured ALN Reached High Level (4) of </li></ul><ul><li>Knowledge Construction </li></ul><ul><li>Un Structured ALN reached level 1 </li></ul>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. 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. 9. Output of Network Analysis: macro-structures <ul><li>Cohesion analysis </li></ul><ul><ul><li>cliques of participants </li></ul></ul><ul><li>Position (power) analysis </li></ul><ul><ul><li>distributions of triggering & responsiveness powers </li></ul></ul><ul><li>Role cluster analysis </li></ul><ul><ul><li>role groups </li></ul></ul>
  10. 10. Cohesion Analysis Structured ALN Un structured ALN <ul><li>Structured ALN: many cohesive macro-structures with many bridging participants </li></ul>tutor tutor
  11. 11. Power Analysis: responders maps Structured ALN Un-Structured ALN <ul><li>Structured ALN: Responsiveness power is </li></ul><ul><li>distributed between many participants </li></ul>
  12. 12. Role Cluster Analysis Structured ALN Un Structured ALN <ul><li>Structured ALN: multiple roles distributed </li></ul><ul><li>between large groups of participants </li></ul>[responder] [lurkers] tutor students [responders] [triggers] tutor [lurkers]
  13. 13. Evolution of Cliques (structured ALN) TIME Network Structures develop in early stages 1 2 3 4
  14. 14. Evolution of Power (structured ALN) TIME Network Structures develop in early stages 1 2 3 4 1 2 3 4
  15. 15. Stochastic Model for Response Relation <ul><li>Responses result from stochastic processes, R i,j </li></ul><ul><ul><li>{r}: possible set of responses states, r i, j = 0, 1 </li></ul></ul><ul><li>neighborhood: actors such that every pair of probabilities of responses are dependent </li></ul><ul><ul><li>P(i->j; k-> l) ≠ P(i-> j)P(k->l) </li></ul></ul><ul><li>P(r) = exp{  N  N •z N (r)}/k(  ) </li></ul><ul><ul><li> N z N (r): effect of neighborhood N </li></ul></ul><ul><ul><li>sum over neighborhoods ( Hamersley Clifford ) </li></ul></ul>
  16. 16. Markov Model: micro-neighborhoods <ul><li>Markov: dependent respones ↔ common actor </li></ul><ul><ul><li>Examples: mutual , triad , star-shape responses </li></ul></ul><ul><li>Explanatory variable: z N (r) =  (i -> j)  N r ij </li></ul><ul><ul><li>product is over all (i -> j) in neighborhood N </li></ul></ul><ul><ul><li>Non Zero only if neighborhood completely responsive </li></ul></ul><ul><li> N parameter </li></ul><ul><ul><ul><li>strength of effect of neighborhood N </li></ul></ul></ul>
  17. 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. 18. Logistic Regression <ul><li>Cases: > g ( g -1) actor-pairs (more then 300) </li></ul><ul><li>dependent Variable: Observed Response (1/0) </li></ul><ul><li>43 (45) independent Explanatory Variables: </li></ul><ul><ul><li>global variables: P, M, TRT, CYC, IS, OS, MS </li></ul></ul><ul><ul><ul><li>pairing, mutuality, transitivity, cyclicity, in-stars, out-stars, mix-stars </li></ul></ul></ul><ul><ul><li>36 (38) individual variables: R i , T i </li></ul></ul><ul><ul><ul><li>responsiveness and triggering of actors </li></ul></ul></ul><ul><li>Result: Relative importance of explanatories </li></ul><ul><ul><li> micro-structures (effects)  theories </li></ul></ul>
  19. 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. 20. Theoretical Foundations <ul><li>Both ALNs: Negative tendency for pairing </li></ul><ul><ul><li>Theory of Social Capital (network holes) </li></ul></ul><ul><ul><li>Minimize effort to gain maximal knowledge </li></ul></ul><ul><li>Structured ALN </li></ul><ul><li>transitivity and multi-responses </li></ul><ul><ul><li>Balance Theory: spread info in several paths </li></ul></ul><ul><ul><li>Theory of Collective Action: we sink or swim </li></ul></ul><ul><li>Unstructured ALN </li></ul><ul><ul><li>Tutor responsiveness : Pre-assigned role </li></ul></ul><ul><ul><li>mutuality : Social Exchange Theory </li></ul></ul>
  21. 21. Conclusions: Macro Structures <ul><li>Macro-structures are developed in early stages </li></ul><ul><li>Macro-structures of Knowledge Constructing ALNs </li></ul><ul><ul><li>mesh of interlinked cliques </li></ul></ul><ul><ul><li>Distributed Response & triggering power </li></ul></ul><ul><ul><li>roles groups </li></ul></ul><ul><ul><ul><li>Triggers, responders , lurkers </li></ul></ul></ul>
  22. 22. Conclusions: Micro-structures and Underlying effects <ul><li>Major effect: </li></ul><ul><ul><li>negative tendency for pairing </li></ul></ul><ul><ul><li>Minimize effort for maximum capital </li></ul></ul><ul><li>Effects in Structured ALN: </li></ul><ul><ul><li>transitivity (balance theory) </li></ul></ul><ul><ul><li>multiple responses (collective action theory) </li></ul></ul><ul><li>Effects in un-structured ALN: </li></ul><ul><ul><li>Tutor responsiveness (Pre-assigned role) </li></ul></ul><ul><ul><li>mutuality (social exchange theory) </li></ul></ul>
  23. 23. Limitations <ul><li>Only two ALNs </li></ul><ul><li>Only one relation (response) </li></ul><ul><li>Definitions of Network Structures are not standardized </li></ul><ul><ul><li>Check stability of results with respect to redefinition of structures </li></ul></ul><ul><li>Time dependence was not analyzed analytically </li></ul><ul><li>Markov model is limited to few effects </li></ul><ul><li>More … </li></ul>
  24. 24. Thank You

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