The presentation explore how network thinking and social network analysis can be useful to improve learners motivation and performance in collaborative learning settings.

1. why do learners cooperate?
SOCIAL NETWORK ANALYSIS FOR
COLLABORATIVE LEARNING
Fabio Nascimbeni, UNIR

2. This is a (computers) network….

3. This is a (social) network….

4. This is a neural network….

5. What about this?

6. 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)

7. 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.

8. 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)

9. SNA: Data source
• Personal questionnaires
• Administrative records
• Organizational charts
• Focus groups
• Learning analytics

10. 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)

11. www.visualcomplexity.com

12. 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

13. Direct reciprocity
I scratch your back and you scratch mine

14. Indirect reciprocity
I scratch your back and someone else will scratch mine

15. Spatial and kin influences
Birds of a feather fly (or don’t fly) together

16. Multilevel influence
When the group attitude is more important than its
members’ attitude

17. 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 andVan Zee and Engel, 2004)

18. 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

19. 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