Ties that matterEffects of the network context on the association between social centrality and academic performance

17 December 2015

PhD SeminarSrećko Joksimović, Dragan Gašević

s.joksimovic@ed.ac.uk @s_joksimovic

www.de.ed.ac.uk/people/srecko-joksimovic

Social network analysis

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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

Centrality measures

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Eigenvalue centrality

Betweenness centrality

Degree centrality

Closeness centrality

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).

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.

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.

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Motive

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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

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

Study objective

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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.

Method(Data)

Platform: Coursera

Courses: Code Yourself! (English), ¡A Programar! (Spanish)

Certificate: 50% for the coursework; 75% - distinction

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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

Method(Analysis)

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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

Results(Network characteristics)

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-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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***

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Note: * p<.05; ** p<.01; *** p<.001

Results(centrality vs. performance)

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-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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*

**

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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.

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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?

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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.

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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.

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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

s.joksimovic@ed.ac.uk @s_joksimovic

Q&A

www.de.ed.ac.uk/people/srecko-joksimovic