Analysing health professionals' learning interactions in online

social networks: A social network analysis approach

HiNZ 201520 October

Xin Li

Contents

• Background• Motivation• Relevant work• Approach• Results• Conclusion

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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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Examples…

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Examples…

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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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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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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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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Network activities

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Network activities

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ApproachSocial 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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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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Results – Centrality for User Network

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Results – Centrality for Thread Network

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Results – 2-Mode Visualisation

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Results – 1-Mode Visualisation

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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