HiNZ 2015 Conference

1. Analysing health professionals'
learning interactions in online
social networks: A social network
analysis approach
HiNZ 2015
20 October
Xin Li

2. Contents
•Background
•Motivation
•Relevant work
•Approach
•Results
•Conclusion
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3. Background
An increasing number of Online Social Networks
(OSN) are targeted for health professionals to:
•Learn and share medical knowledge
•Discuss practice management challenges
•and clinical issues…
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4. Examples…
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5. 5
Examples…

6. Motivation
•Many OSN for health professionals but they appear to
fail (Sandars et al., 2012) (Ikioda et al., 2013)
•Insufficient understanding on the efficacy of OSN in
supporting health professionals’ learning (Institute of
Medicine, 2010)
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7. Motivation
How does the interaction occurring in health professionals’
OSN support their learning?
1. Patterns of interaction (this study)
• Level of participation
• Structure of interaction
2. Quality of interaction (future study)
3. Outcome of interaction (future study)
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8. Relevant Work
•Dawson, et al. (2010) proposed a tool called SNAPP to
analyse students’ interactions in LMS discussion forum
•De Laat and Schreurs (2012, 2013) analysed teachers'
learning interaction occurring in their OSN.
•Study in health is limited, yet, Stewart and Abidi (2013)
studied a paediatric pain discussion forum
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9. Dataset
•Online forum for Australian doctors
•Established in 2009, by an online health CPD provider
•Currently 11282 members, mainly GPs from Australia
•Over 8000 posts in 40 medical topic areas
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10. Network activities
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11. Network activities
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12. Approach
Social Network Analysis
- Year 2009 to 2014
- 621 users, 723 threads
The measurement
• Network structural measures -> Structure of interaction
– Density, Centralisation, Diameter, Average path length
• Centrality measures -> Level of participation
– Degree, Betweenness, Closeness centrality
• 1-mode and 2-mode network visualisation
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13. Results – Network Structural Measures
User (N=621) Thread (N=723)
Density 0.04 0.40
Centralisation 0.59 0.46
Diameter 5.00 4.00
Average path length 2.17 1.66
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14. 14
Results – Centrality for User Network

15. 15
Results – Centrality for Thread Network

16. 16
Results – 2-Mode Visualisation

17. 17
Results – 1-Mode Visualisation

18. Conclusion
• Low level of participation
• Highly centralised network
• Longitudinal analysis to study interaction changes over time
• Chance of small group learning occurring – requires further
investigation to identify potential learning groups
• Content analysis of online discussion to understand how the
knowledge is constructed and influenced by the interaction
• Outcome assessment of online interaction
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19. References
De Laat, M., & Schreurs, B. (2013). Visualizing Informal Professional Development Networks: Building a Case for Learning Analytics in the
Workplace. American Behavioral Scientist, 57(10 ), 1421-1438.
Ferguson, R., & Shum, S. B. (2012). Social Learning Analytics: Five Approaches. Paper presented at the Proceedings of the 2nd
International Conference on Learning Analytics and Knowledge.
Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher
education. The internet and higher education, 2(2), 87-105.
Ikioda, F., Kendall, S., Brooks, F., De Liddo, A., & Buckingham Shum, S. (2013). Factors That Influence Healthcare Professionals’ Online
Interaction in a Virtual Community of Practice. Social Networking, 02(04), 174-184.
Institute of Medicine. (2010). Redesigning Continuing Education in the Health Professions. Washington, DC: National Academies Press.
McGowan, B. S., Wasko, M., Vartabedian, B. S., Miller, R. S., Freiherr, D. D., & Abdolrasulnia, M. (2012). Understanding the Factors That
Influence the Adoption and Meaningful Use of Social Media by Physicians to Share Medical Information. Journal of Medical Internet
Research, 14(5).
Moore, D. E., Green, J. S., & Gallis, H. A. (2009). Achieving desired results and improved outcomes: integrating planning and assessment
throughout learning activities. Journal of Continuing Education in the Health Professions, 29(1), 1-15.
Sandars, J., Kokotailo, P., & Singh, G. (2012). The importance of social and collaborative learning for online continuing medical education
(OCME): directions for future development and research. Med Teach, 34(8), 649-652.
Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a
communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721-1731.
Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics.
Social Networks, 32(1), 44-60.
Stewart, S. A., & Abidi, S. S. R. (2013). Using Social Network Analysis to Study the Knowledge Sharing Patterns of Health Professionals
Using Web 2.0 Tools. Biomedical Engineering Systems and Technologies, 273, 335-352.
Wenger, E., Trayner, B., & de Laat, M. (2011). Promoting and assessing value creation in communities and networks: A conceptual
framework. http://www.knowledge-architecture.com/downloads/Wenger_Trayner_DeLaat_Value_creation.pdf
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