2003, OrlandoAviv, Network Analysis1 Network Analysis of Effective Knowledge Construction In Asynchronous Learning Networks 9’th ALN/SLOAN-C Conference.

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


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


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


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

http://www.aln.org/publications/jaln/v7n3/v7n3_aviv.asp

http://www.aln.org/publications/jaln/v7n3/v7n3_aviv.asp


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


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


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


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)


Output of Network Analysis: macro-structures

• Cohesion analysis

– cliques of participants

• Position (power) analysis

– distributions of triggering & responsiveness

powers

• Role cluster analysis

– role groups


Cohesion Analysis

tutor tutor

Structured ALN Un structured ALN

Structured ALN: many cohesive macro-

structures with many bridging participants


Power Analysis: responders mapsStructured ALN Un-Structured ALN

Structured ALN: Responsiveness power is

distributed between many participants


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


Evolution of Cliques (structured ALN)

1 2

3 4

TIME

Network Structures develop in early stages


Evolution of Power (structured ALN)

1 2 3 4

TIME

1 2 3 4

Network Structures develop in early stages


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 )


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


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


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


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


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


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


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)


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 …


Thank You

2003, OrlandoAviv, Network Analysis1 Network Analysis of Effective Knowledge Construction In Asynchronous Learning Networks 9’th ALN/SLOAN-C Conference.

Documents

social network analysis

network microstructures

network macrostructures

forthcoming slide

cohesion structures

network analysis5 testbed

alns macrostructures

role groups microstructures