Post on 24-Dec-2015
2003, Orlando Aviv, Network Analysis 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
2003, Orlando 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
2003, Orlando 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
2003, Orlando 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
2003, Orlando 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
2003, Orlando Aviv, Network Analysis 6
Design Parameters
Of the two ALNs
Structured ALN
un-structured ALN
Registration Yes No
Cooperation commitment Yes No
Goal - directed scheduling Yes Not relevant
Predefined Work Procedure Yes No
Resource Interdependence Yes No
Work Interdependence Yes No
Reward mechanism Yes No
Reward Interdependence No Not relevant
Pre-assigned roles No No
Reflection procedures No No
Individual Accountability Yes Not relevant
2003, Orlando Aviv, Network Analysis 7
Level
Content Analysis via Gunawardena Model
Structured ALN
un- Structured ALN
I Explain Concepts 38 70
II Argue dissonances 34
III Synthesis & Judge 28
IV Test to theory 143
V Reflection 5
Structured ALN Reached High Level (4) of
Knowledge Construction
Un Structured ALN reached level 1
2003, Orlando 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)
2003, Orlando Aviv, Network Analysis 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
2003, Orlando Aviv, Network Analysis 10
Cohesion Analysis
tutor tutor
Structured ALN Un structured ALN
Structured ALN: many cohesive macro-
structures with many bridging participants
2003, Orlando Aviv, Network Analysis 11
Power Analysis: responders mapsStructured ALN Un-Structured ALN
Structured ALN: Responsiveness power is
distributed between many participants
2003, Orlando Aviv, Network Analysis 12
Role Cluster AnalysisStructured ALN Un Structured ALN
[responder]
[lurkers]
tutor
students
[responders]
[triggers]
tutor
[lurkers]
Structured ALN: multiple roles distributed
between large groups of participants
2003, Orlando Aviv, Network Analysis 13
Evolution of Cliques (structured ALN)
1 2
3 4
TIME
Network Structures develop in early stages
2003, Orlando Aviv, Network Analysis 14
Evolution of Power (structured ALN)
1 2 3 4
TIME
1 2 3 4
Network Structures develop in early stages
2003, Orlando 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{N N•zN(r)}/k()
N zN(r): effect of neighborhood N
– sum over neighborhoods (Hamersley Clifford )
2003, Orlando Aviv, Network Analysis 16
Markov Model: micro-neighborhoods
• Markov: dependent respones ↔ common actor
– Examples: mutual, triad, star-shape responses
• Explanatory variable: zN(r) = (i → j)N 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
2003, Orlando Aviv, Network Analysis 17
Markov Model Variablesneighborhood Dependent
ResponsesEffect
(Individual / global) Explanatory zN(r)
i responsiveness
(i→j) fixed i i responsiveness Ri(r) = jrij
i triggering (j→i) fixed i i triggerring Ti(r) = jrji
All pairs {i, j} (i→j) OR (j→i) Pairing tendency P(r)ijrij
all mutual (i→j) AND (j→i) mutuality M(r) ijrijrji
all 2 out-stars (i→j) AND (i→k) Multi-responsiveness
OS2(r) ijkrijrik
all 2 in-stars (i→j) AND (k→j) Multi-triggering IS2(r) ijkrijrkj
all 2 mix-stars (i→j) AND (j→k)
response & triggering
MS2(r) ijkrijrjk
All transitive triads
(i→j) AND (j→k) AND (i→k)
transitivity TRT(r) ijkrijrj
k
All cyclic triads (i→j) AND (j→k) AND (k→i)
cyclicity CYT(r) ijkrijrj
k
2003, Orlando 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
2003, Orlando Aviv, Network Analysis 19
Results: What Effects the Response Relation?
Structured ALN Un-structured ALN
2. transitivity3. out-stars (multi-responses)
1. Global (negative) tendency for pairing
2. tutor responsiveness
3. mutuality
1 12
2
33
2003, Orlando 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
• 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
2003, Orlando 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
2003, Orlando 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:
– Tutor responsiveness (Pre-assigned role)
– mutuality (social exchange theory)
2003, Orlando 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 …
2003, Orlando Aviv, Network Analysis 24
Thank You