why do learners cooperate? SOCIAL NETWORK ANALYSIS FOR

COLLABORATIVE LEARNING

Fabio Nascimbeni, UNIR

This is a (computers) network….

This is a (social) network….

This is a neural network….

What about this?

81% of students have experience of

discussing course-related problems on

FB

59% say it is a reason to use FB

(Jong et al, 2014)

Instilling more “network thinking” within education

The rise of the network society (Castells and many others) urges us to “network-think”, education is no exception.

“Network thinking is poised to invade all domains of human activity and most field of human inquiry.” (Barabási, 2002)

The level of network thinking within education varies considerably depending on the sector we look at (Learnovation Report, 2010).

Increasing the level of network thinking within education practices is fundamental if we want to understand the motivation factors which lay behind the different cooperation attitudes of learners, and ultimately if we want to take the maximum benefit from any collaborative learning experience.

SNA: Social Network Analysis

A social network represents the finite sets of actors and the relations defined between them

• Actors

• Ties

• Groupings

• What kind of questions can we ask of social network data?

(Wasserman & Faust, 1994)

SNA: Data source

• Personal questionnaires

• Administrative records

• Organizational charts

• Focus groups

• Learning analytics

SNA: Analyzing a Social Network

• Descriptive statistics: How many learners, how many ties?

• Degree centrality: How many ties does each learner have; what kinds of learners have lots of ties, few ties. What kind of ties?

• Betweenness centrality: The connective properties of learners, hubs and authorities.

• Closeness centrality: Path length between learners. Better to be closer to some people?

• Network centrality: Average path length to traverse a network. Shorter paths better?

Quoting (Wasserman & Faust, 1994)

www.visualcomplexity.com

Looking for the “mechanisms” though which collaboration works

Adopting a collaborative approach has a “cost”

In the long term, humans tend to chose “win stays, lose shifts” approaches

Any network would be doomed to fail

Some cooperation mechanisms exist (luckily!)Direct reciprocity

Indirect reciprocity

Spatial and Kin influence

Multilevel influence

(adapted from Novak 2011)

Direct reciprocity

I scratch your back and you scratch mine

Indirect reciprocity

I scratch your back and someone else will scratch mine

Spatial and kin influences

Birds of a feather fly (or don’t fly) together

Multilevel influence

When the group attitude is more important than its members’ attitude

Supporting collaborative learning: hints from network sciences (1/2)

Four conditions to look at:

1.Confidence (“dare to share”)

2.Commitment

3.Space for divergence

4.Decentralisation

(adapted from Surowiecki, 2005 and Van Zee and Engel, 2004)

Supporting collaborative learning: hints from network sciences (2/2)

The importance of “collaboration dynamisers” (AKA “network weavers”)

What strategy works best? What risks?

a)Focus on the collaboration leaders (natural hubs)

b)Focus on the followers

c)A balanced strategy

Conclusions

Learners should not only sit in the driving seat, but should “drive together”.

For this to happen meaningfully and smoothly, we need to look at network sciences and to apply network analysis methods (such as SNA).

1.Measure new things

2.Reveal (motivational) patterns

3.Improve support activities

4.Increase the level of network-thinking among educational researchers/practitioners