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Ties that matterEffects of the network context on the association between social centrality and academic performance
17 December 2015
PhD SeminarSrećko Joksimović, Dragan Gašević
[email protected] @s_joksimovic
www.de.ed.ac.uk/people/srecko-joksimovic
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Social network analysis
Slide 2 out of 18
Structural environment as opportunity or constraint
Structure (e.g., social, economic, political) as lasting patterns of relations among actors
Actions are viewed as interdependent
Ties as channels for flow of resources
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Centrality measures
Slide 3 out of 18
Eigenvalue centrality
Betweenness centrality
Degree centrality
Closeness centrality
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Strength of ties
Slide 4 out of 18http://www.informationweek.com/why-your-weak-relationships-pack-strength/d/d-id/1107476?
Connections through
strong ties
Connections through
weak ties
“The argument asserts that
our acquaintances (weak ties) are less likely
to be socially involved with one another
than are our close friends (strong ties)” (Granovetter, 1983, p.1).
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Structural holes
Slide 5 out of 18http://rzhengac.github.io/Comp4641Main_tutorial.html
Structural hole
Node A’s position implies structural advantage relative to node D.
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SNA in educational research
Structural centrality measures as predictors of: Cognitive learning outcomes
Final grade
Higher sense of belonging to a group
Course satisfaction
Comprehension of learning materials
etc.
Slide 6 out of 18
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Motive
Slide 7 out of 18
Russo and Koesten (2005)
prestige (in-degree) Cognitive learningoutcomecentrality (out-degree)
degree centralityCourse grade
Cho et al. (2007)
closeness centrality
betweenness centrality
Jiang et al. (2014)
degree centrality
GPAcloseness centrality
betweenness centralityeccentrality
Gašević et al. (2013)
degree centralityCourse grade
closeness centrality
betweenness centrality
degree centralityCourse grade
closeness centrality
betweenness centrality
Positive, statistically significant association
Note:
No statistically significant association
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Theoretical approach
Slide 8 out of 18https://cvcedhlab.hypotheses.org/author/mduering
Centrality does (not) necessarily imply less constraints and more benefit (Krachardt, 1999)
Importance of contextual factors
Triads as the fundamental unit of analysis
Simmel’s theory of social interactions
No inherent motivation to form a clique
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Study objective
Slide 9 out of 18
Network structural properties Learning outcome
Social dynamical processes?
Research questions:
1. Differences in the underlying processes that determine network formation?
2. Propensity for forming Simmelian ties?
3. The impact at the association between social centrality and academic performance?
Tie dynamics:• Homophily/heterophily• Reciprocity• Triadic closure• etc.
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Method(Data)
Platform: Coursera
Courses: Code Yourself! (English), ¡A Programar! (Spanish)
Certificate: 50% for the coursework; 75% - distinction
Slide 10 out of 18
59,531
26,568
1,430
25,255
13,808
1,8180
10000
20000
30000
40000
50000
60000
70000
Enrolled Engaged Engaged withforum
Course participants
Codeyourself Aprogramar
0
500
1000
1500
2000
Codeyourself Aprogramar
Obtained certificate
Normal Disctinction
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Method(Analysis)
Slide 11 out of 18
Discussion forum
extract
Weighted,
directed graphSNA
Descriptive
network analysis
Statistical
network analysis
Centrality measures Exponential random graph models
Homophily Achievement Domestic/Guest Gender
Reciprocity Popularity Expansiveness Simmelian cliquesCourse outcome
Obtained certificate Normal
Distinction
None
Multinomial logistic
regression Association?
Interpretation
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Results(Network characteristics)
Slide 12 out of 18
-8 -6 -4 -2 0 2 4 6
Expansiveness
Popularity
Simmelian
Reciprocity
Sel. Mixing (Gender)
Sel. Mixing (Domestic)
Achievement (Normal)
Achievement (None)
Achievement (Distinct)
Edges
Aprogramar Codeyourself
Analysis of the estimates for the two ERG models
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Note: * p<.05; ** p<.01; *** p<.001
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Results(centrality vs. performance)
Slide 13 out of 18
-0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08
Betweenness (normal)
Betweenness (distinct)
Closeness (normal)
Closeness (distinct)
W. Degree (normal)
W. Degree (distinct)
Aprgoramar Codeyourself
Results of the multinomial regression analysis
Note: * p<.05; ** p<.01; *** p<.001
In order to provide meaningful visualizations, estimates for betweenness centrality were
multiplied by 100 (only for the presentation purposes)
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Conclusions
Observed networks differ with respect to the determinants of network formation.
These discrepancies DO affect the association between social centrality and academic performance.
Social centrality within the network characterized with “super-strong” ties, DOES NOT necessarily imply benefits.
Slide 14 out of 18
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Implications & Further Research
Implications:
“Traditional” (descriptive) SNA + statistical network analysis.
Account for contextual determinants.
Further Research:
Examine temporal dynamics?
SNA + content analysis?
Language vs. social dynamics?
Slide 15 out of 18
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References
S. Joksimović, A. Manataki, D. Gašević, S. Dawson, V. Kovanović, and I. F. de Kereki: “Translating network position into performance: Importance of centrality in different network configurations”, In Proceedings of the Sixth International Conference on Learning Analytics and Knowledge (LAK 2016), (submitted);
B. V. Carolan, Social Network Analysis Education: Theory, Methods & Applications. Social Network Analysis Education: Theory, Methods & Applications. SAGE Publications, Inc. SAGE Publications, Inc., 2014.
S. Goodreau, J. Kitts, and M. Morris, “Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks*,” Demography, vol. 46, no. 1, pp. 103–125, 2009.
L. C. Freeman, “Centrality in social networks conceptual clarification,” Soc. Netw., vol. 1, no. 3, pp. 215–239, 1979.
S. Wasserman, Social network analysis: Methods and applications, vol. 8. Cambridge university press, 1994.
R. S. Burt, STRUCTURAL HOLES. Harvard University Press, 1995.
M. S. Granovetter, “The strength of weak ties,” Am. J. Sociol., pp. 1360–1380, 1973.
D. Krackhardt, “The Ties that Torture: Simmelian Tie Analysis in Organizations,” Res. Sociol. Organ., vol. 16, pp. 183–210, 1999.
D. Krackhardt, “Super Strong and Sticky,” Power Influ. Organ., p. 21, 1998.
Slide 16 out of 18
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References
Granovetter, Mark. "The strength of weak ties: A network theory revisited.“ Sociological theory 1.1 pp. 201-233, 1983.
T. C. Russo and J. Koesten, “Prestige, centrality, and learning: A social network analysis of an online class,” Commun. Educ., vol. 54, no. 3, pp. 254–261, 2005.
H. Cho, G. Gay, B. Davidson, and A. Ingraffea, “Social networks, communication styles, and learning performance in a CSCL community,” Comput. Educ., vol. 49, no. 2, pp. 309–329, Sep. 2007.
D. Gašević, A. Zouaq, and R. Janzen, “‘Choose Your Classmates, Your GPA Is at Stake!’: The Association of Cross-Class Social Ties and Academic Performance,” Am. Behav. Sci., 2013
S. Jiang, S. M. Fitzhugh, and M. Warschauer, “Social Positioning and Performance in MOOCs,” in Proceedings of the Workshops held at Educational Data Mining 2014, co-located with 7th International Conference on Educational Data Mining (EDM 2014), London, United Kingdom, 2014, vol. 1183, p. 14.
Slide 17 out of 18
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Ties that matterEffects of the network context on the association between social centrality and academic performance
17 December 2015
PhD Seminar
Srećko Joksimović, Dragan Gašević
[email protected] @s_joksimovic
Q&A
www.de.ed.ac.uk/people/srecko-joksimovic