What is the source of social capital?
The association between social network position
and social presence in communities o...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Objective
Main question
Why diffe...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Objective
Main question
Why diffe...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Final goal
For instructors
Provi...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Asynchronous online discussions
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Data set
SNA centrality measures...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Results
Regression results for s...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings
• All but one regre...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings
• All but one regre...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Degree centrality
...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Betweenness centra...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Main findings: Closeness centrali...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Limitations and future work
Limi...
Introduction
Background
Methods
Results
Discussion and Conclusions
Conclusions
References
Summary
• There is an interestin...
Thank you
References I
Terry Anderson and Jon Dron. “Three generations of distance education pedagogy”. In:
The International Review...
References II
Trevor J Hastie, Robert J Tibshirani, and Jerome H Friedman. The elements of statistical
learning: data mini...
Upcoming SlideShare
Loading in …5
×

What is the source of social capital? The association between social network position and social presence in communities of inquiry

724
-1

Published on

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.

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
724
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

What is the source of social capital? The association between social network position and social presence in communities of inquiry

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 33. Thank you
  34. 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. 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.
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×