Presentation at the Graph-based Educational Data Mining workshop (G-EDM) during the 2014 Educational Data Mining conference (EDM 2014) at Institute of Education, University of London, London, UK on July 4th, 2014.

1. What is the source of social capital?
The association between social network position
and social presence in communities of inquiry
Vitomir Kovanovic1
Srecko Joksimovic1
vitomir kovanovic@sfu.ca sjoksimo@sfu.ca
Dragan Gasevic2
Marek Hatala1
dgasevic@acm.org mhatala@sfu.ca
1
School of Interactive Arts and Technology 2
School of Computing Science
Simon Fraser University Athabasca University
Burnaby, Canada Edmonton, Canada
July 4, 2014,
London, UK

2. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Objective
Main question
Why different students have different social
capital and social network positions?
V. Kovanovic et al. What is the source of social capital? 1 / 23

3. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Objective
Main question
Why different students have different social
capital and social network positions?
Can we use some of the online learning theories to provide
more insights into the underlying factors that contribute to
the observed differences?
V. Kovanovic et al. What is the source of social capital? 1 / 23

4. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Final goal
For instructors
Provide instructors with information on student’s learning progress
within a learning community so that appropriate instructional
interventions can be planned and implemented.
For students
Provide learners with the real time feedback of their own progress, and
progress of their peers so that they can self-regulate their learning
activities more successfully.
For researchers
Use data to better operationalize current Community of Inquiry model
of online learning.
V. Kovanovic et al. What is the source of social capital? 2 / 23

5. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Asynchronous online discussions -
“gold mine of information” Henri [9]
• Frequently used in both blended and
fully online learning [11],
• Their use produced large amount of
data about learning processes [4],
• Particularly important in
social-constructivist pedagogies [1].
• Frequently used for constructing
students’ social networks.
V. Kovanovic et al. What is the source of social capital? 3 / 23

6. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social network analysis
• Social capital: value resulting from occupying a particularly
advantageous position within a social network [2]
• Many studies indicated importance of students’ social capital on
many important aspects of learning and educational experience:
• Academic performance,
• Retention,
• Persistance,
• Program satisfaction,
• Sense of community,
• . . .
V. Kovanovic et al. What is the source of social capital? 4 / 23

7. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social network analysis
However,
• Typically isolated studies focusing on a single aspect of particular
interest,
• Typically not explaining what might be the cause of observed
differences in network positions,
• Lack of well-established learning theories which explicitly address
social network position.
V. Kovanovic et al. What is the source of social capital? 5 / 23

8. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social network analysis
However,
• Typically isolated studies focusing on a single aspect of particular
interest,
• Typically not explaining what might be the cause of observed
differences in network positions,
• Lack of well-established learning theories which explicitly address
social network position.
Can we leverage existing comprehensive models of online learning to
provide insight into the nature of the observed differences in social
networks?
V. Kovanovic et al. What is the source of social capital? 5 / 23

9. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Community of Inquiry (CoI) model
Conceptual model outlying the important constructs that define
worthwhile educational experience in online education setting.
• Social presence: relationships and
social climate in a community.
• Cognitive presence: phases of
cognitive engagement and knowledge
construction.
• Teaching presence: instructional
role during social learning.
CoI model is:
• Extensively researched and validated,
• Adopts content analysis for
assessment of presences.
V. Kovanovic et al. What is the source of social capital? 6 / 23

10. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social presence
Social presence
“Ability of participants in a community of inquiry to project themselves
socially and emotionally, as “real” people (i.e., their full personality),
through the medium of communication being used.” [7, p. 89]
V. Kovanovic et al. What is the source of social capital? 7 / 23

11. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social presence
Social presence
“Ability of participants in a community of inquiry to project themselves
socially and emotionally, as “real” people (i.e., their full personality),
through the medium of communication being used.” [7, p. 89]
Three different dimensions of communication:
1 Affectivity and expression of emotions: defined as “the ability
and confidence to express feelings related to the educational
experience.” [7, p. 99]
2 Interactivity and open communication: defined as ““reciprocal
and respectful exchanges of messages” [7, p. 100].
3 Cohesiveness: Activities that “build and sustain a sense of group
commitment” [7, p. 101]
V. Kovanovic et al. What is the source of social capital? 7 / 23

12. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Social presence coding scheme
• Content analysis scheme for analysis of discussion messages,
• Use of whole message as unit of analysis,
• Look for particular indicators of different sociocognitive processes,
Social presence categories and indicators as defined by Rourke et al. [12].
Category Code Indicator
Affective A1 Expression of emotions
A2 Use of humor
A3 Self-disclosure
Interactive or Open
Communication
I1 Continuing a thread
I2 Quoting from others’ messages
I3 Referring explicitly to others’ messages
I4 Asking questions
I5 Complementing, expressing appreciation
I6 Expressing agreement
Cohesive C1 Vocatives
C2 Addresses or refers to the group using inclusive pronouns
C3 Phatics, salutations
V. Kovanovic et al. What is the source of social capital? 8 / 23

13. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Proposed approach
General idea
Investigate the relationship between students’ social capital
and social climate in the course.
V. Kovanovic et al. What is the source of social capital? 9 / 23

14. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Proposed approach
General idea
Investigate the relationship between students’ social capital
and social climate in the course.
More specificially,
We looked at the relationship between social network
centrality measures and social presence, one of the three
main components of Community of Inquiry model of online
learning.
V. Kovanovic et al. What is the source of social capital? 9 / 23

15. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Proposed approach
Measure three dimensions of social presence for each student and see
how they relate to their network centrality measures.
V. Kovanovic et al. What is the source of social capital? 10 / 23

16. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
Social network analysis
Community of Inquiry (CoI) model
Proposed approach
Proposed approach
Measure three dimensions of social presence for each student and see
how they relate to their network centrality measures.
• Are three dimensions of social presence statistically significant
predictors of network centrality measures?
• What is the relative importance of different dimensions of social
presence?
V. Kovanovic et al. What is the source of social capital? 10 / 23

17. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Data set
• Six offerings of graduate level course in software engineering at
distance learning university.
• Total of 1747 messages by 81 students.
Course offering statistics.
Student count Message count Graph density
Winter 2008 15 212 0.52
Fall 2008 22 633 0.69
Summer 2009 10 243 0.84
Fall 2009 7 63 0.58
Winter 2010 14 359 0.84
Winter 2011 13 237 0.77
Average 13 291 0.71
Total 81 1747
V. Kovanovic et al. What is the source of social capital? 11 / 23

18. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Social network centrality measures
• Directed social network graph based on post-reply activity.
• Extracted popular centrality measures.
Descriptive statistics of social network metrics.
Mean SD Min Max
Betweenness 9.04 14.51 0.00 74.20
In-degree 19.84 8.62 4.00 42.00
Out-degree 19.86 9.37 3.00 44.00
In-closeness 0.09 0.04 0.04 0.17
Out-closeness 0.08 0.04 0.03 0.18
V. Kovanovic et al. What is the source of social capital? 12 / 23

19. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Social presence message coding
• Each message manually coded by two coders on the levels of
indicators (Percentage agreement = [84.1 − 98.9]%).
Social presence indicators.
Category Code Indicator Count Percentage Percent Agreement
Affective A1 Expression of emotions 288 16.5% 84.4
A2 Use of humor 44 2.5% 93.1
A3 Self-disclosure 322 18.4% 84.1
Interactive I1 Continuing a thread 1664 95.2% 98.9
I2 Quoting from others messages 65 3.7% 95.4
I3 Referring explicitly to other’s messages 91 5.2% 92.7
I4 Asking questions 800 45.8% 89.4
I5 Complementing, expressing appreciation 1391 79.6% 90.7
I6 Expressing agreement 243 13.9% 96.6
Cohesive C1 Vocatives 1433 82.0% 91.8
C2 Addresses or refers to the group using
inclusive pronouns
144 8.2% 88.8
C3 Phatics, salutations 1281 73.3% 96.1
V. Kovanovic et al. What is the source of social capital? 13 / 23

20. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Social presence message coding
• Some indicators way too frequent.
• Limiting discriminatory power of the whole category.
• We removed indicators occurring in more than 75% of the messages.
Social presence categories.
Category Count Percentage Percent Agreement
Affective 530 30.3% 80.8
Interactive (Excluded I1 and I5) 1030 59.0% 86.2
Cohesive (Excluded C1) 1326 75.9% 93.4
V. Kovanovic et al. What is the source of social capital? 14 / 23

21. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Statistical Analysis
Multiple regression analysis:
• DV: Social network centrality metrics.
• IVs: CoI Social presence codes.
• Backward stepwise model selection using AIC criterion [8].
• Holm-Bonferroni correction [10]:
• Guaranteed to keep family-wise error rate (FWER) α at the desired
level (i.e., α = 0.05).
• Significantly more powerful than classical Bonferroni correction [5].
V. Kovanovic et al. What is the source of social capital? 15 / 23

22. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Holm-Bonferroni correction procedure
For family of N tests and desired α significance:
• Sort all N observed p-values from smallest to largest.
• Cutoff for the smallest p-value: α/N.
• Cutoff for next p-value: α/(N − 1).
• . . .
• Cutoff for largest p-value: α.
V. Kovanovic et al. What is the source of social capital? 16 / 23

23. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Holm-Bonferroni correction procedure
For family of N tests and desired α significance:
• Sort all N observed p-values from smallest to largest.
• Cutoff for the smallest p-value: α/N.
• Cutoff for next p-value: α/(N − 1).
• . . .
• Cutoff for largest p-value: α.
Important rule
If any of the tests gets rejected, all the subsequent tests are also rejected
automatically.
V. Kovanovic et al. What is the source of social capital? 16 / 23

24. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures
Message coding
Statistical Analysis
Holm-Bonferroni correction procedure
For family of N tests and desired α significance:
• Sort all N observed p-values from smallest to largest.
• Cutoff for the smallest p-value: α/N.
• Cutoff for next p-value: α/(N − 1).
• . . .
• Cutoff for largest p-value: α.
Important rule
If any of the tests gets rejected, all the subsequent tests are also rejected
automatically.
Current study
In our study with 5 tests, cutoff p-values are
α = [0.01, 0.0125, 0.0167, 0.0250, 0.05]
V. Kovanovic et al. What is the source of social capital? 16 / 23

25. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Results
Regression results for selected centrality measures after stepwise model selection
using AIC criterion.
Betweenness In-degree Out-degree In-closeness Out-closeness
β SE p β SE p β SE p β SE p β SE p
Affective 0.27 0.12 0.024 0.18 0.054 0.001 0.23 0.059 <0.001
Interactive 0.38 0.12 0.002 0.65 0.064 <0.001 0.65 0.07 <0.001 0.27 0.11 0.015 0.37 0.15 0.017
Cohesive 0.2 0.061 0.001 0.14 0.066 0.041 -0.23 0.15 0.137
F(3, 77) 19.6 <0.001 159 <0.001 130 <0.001 6.24 0.015 3.03 0.054
Adjusted R2
0.32 0.86 0.83 0.061 0.048
V. Kovanovic et al. What is the source of social capital? 17 / 23

26. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings
• All but one regression models were significant, one marginally
significant.
• Interactive dimension of social presence is the most strongly
associated with all of the network centrality measures.
• Probable reason is the nature of social networks as a medium for
fostering collaborative and productive learning.
• According to Garrison [6], interactive social presence is dominant in
the beginning until students develop trust and sense of community,
but it decreases over time, while affective and cohesive increase over
time.
V. Kovanovic et al. What is the source of social capital? 18 / 23

27. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings
• All but one regression models were significant, one marginally
significant.
• Interactive dimension of social presence is the most strongly
associated with all of the network centrality measures.
• Probable reason is the nature of social networks as a medium for
fostering collaborative and productive learning.
• According to Garrison [6], interactive social presence is dominant in
the beginning until students develop trust and sense of community,
but it decreases over time, while affective and cohesive increase over
time.
• Practical implication: provide opportunities for focused, on task
interactions that foster open communication and collaboration.
V. Kovanovic et al. What is the source of social capital? 18 / 23

28. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Degree centrality
• All three categories of social presence were significantly predictive of
In-Degree and Out-Degree centrality measures.
• Affective and Cohesive are very interesting as they are not directly
affecting degree centrality.
• Interactive category was most strongly associated with degree
network centrality.
• This is expected for In-Degree as activities such as asking
questions, addressing by name or quoting someone’s message
increase chances of ’provoking’ a response.
• For Out-Degree it more interesting.
V. Kovanovic et al. What is the source of social capital? 19 / 23

29. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Betweenness centrality
• 32% of variability in betweenness centrality scores explained by our
regression model. Effect size: Cohen’s f2
= 0.47 which is considered
to be a large effect size [3].
• Interactive and affective dimensions of social presence were
significantly predictive of betweenness centrality, with interactive
dimension being more strongly associated.
• Probably due to the nature of social networks and the focus on
information exchange. Also trust and sense of community develops
later in the course when student already developed open
communication.
• As a followup, we want to look at the individual indicators, as they
might contain some answers to our findings.
V. Kovanovic et al. What is the source of social capital? 20 / 23

30. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Closeness centrality
• Only interactive category was significantly predictive of
In-closeness centrality. Model for Out-closeness was very
marginally significant (p = 0.054).
• Probable reason might be the fact that closeness embeds the
interactive relationships, for which affectivity and cohesiveness are
not much important.
V. Kovanovic et al. What is the source of social capital? 21 / 23

31. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Limitations and future work
Limitations:
• Data from one course
• despite having data from several offerings of the course, there might
be an effect of the particular pedagogical approach.
• Not all student interactions have positive effect and increase social
capital,
• Other important factors beside social presence.
Future work:
• Replicate on new data set, with larger and more diverse subjects,
• Investigate changes in the distributions of three social presence
dimensions over time,
• Maybe look at the levels of indicators instead of categories.
V. Kovanovic et al. What is the source of social capital? 22 / 23

32. Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Summary
• There is an interesting connection between social presence and
students’ social network positions.
• We can use three dimensions of social presence to predict different
network centrality metrics.
• Interactivity and open communication showed to be the most
significant component of social presence.
• Our findings indicate the need for providing student with
opportunities for the development of social capital through
collaboration with other students on focused tasks.
• Educational theories suggest that development of trust and sense of
community follows from on-task interactions. Our data shows some
preliminary support for this.
V. Kovanovic et al. What is the source of social capital? 23 / 23

33. Thank you

34. References I
Terry Anderson and Jon Dron. “Three generations of distance education pedagogy”. In:
The International Review of Research in Open and Distance Learning 12.3 (2010),
pp. 80–97.
Ronald S. Burt. “Structural Holes versus Network Closure as Social Capital”. In: Social
Capital: Theory and Research. 2001.
Jacob Cohen. “The Analysis of Variance”. In: Statistical power analysis for the behavioral
sciences. 1988, pp. 273–406.
Roisin Donnelly and John Gardner. “Content analysis of computer conferencing
transcripts”. In: Interactive Learning Environments 19.4 (2011), pp. 303–315.
Olive Jean Dunn. “Multiple Comparisons among Means”. In: Journal of the American
Statistical Association 56.293 (1961), pp. 52–64.
D. Randy Garrison. E-Learning in the 21st Century: A Framework for Research and
Practice. 2 edition. Routledge, 2011.
D. Randy Garrison, Terry Anderson, and Walter Archer. “Critical Inquiry in a Text-Based
Environment: Computer Conferencing in Higher Education”. In: The Internet and Higher
Education 2.2–3 (1999), pp. 87–105.

35. References II
Trevor J Hastie, Robert J Tibshirani, and Jerome H Friedman. The elements of statistical
learning: data mining, inference, and prediction. Springer, 2013.
France Henri. “Computer Conferencing and Content Analysis”. In: Collaborative Learning
Through Computer Conferencing. 1992, pp. 117–136.
Sture Holm. “A Simple Sequentially Rejective Multiple Test Procedure”. In: Scandinavian
Journal of Statistics 6.2 (1979), pp. 65–70.
Rocci Luppicini. “Review of computer mediated communication research for education”.
In: Instructional Science 35.2 (2007), pp. 141–185.
Liam Rourke et al. “Assessing Social Presence In Asynchronous Text-based Computer
Conferencing”. In: The Journal of Distance Education 14.2 (1999), pp. 50–71.