Presentation at the Connecting Levels and Methods of Analysis in Networked Learning Communities Workshop during the Learning Analytics Conference in Vancouver, 2012

1. Visualizing Informal
Networked Learning Activities
Bieke Schreurs, Chris Teplovs & Maarten de Laat

2. Our research context:
Informal learning in practice
• Tacit knowledge
• Hidden, spontaneous, aimed at solving work
related problems
• Important driver for professional development
• Hard to manage and reward its value
The problem of “under the radar” informal learning
poses an interesting challenge for the field of
Learning Analytics, namely finding ways to capture
and analyze traces of (social) informal learning in
every day life and work networks.

3. Our approach:
Practice-based Research
‘Practice-based research is conducted in the real-world
context, with real problems, and in collaboration with
practitioners, and therefore it is much more likely to lead to
effective application and real change’
(Ros & Vermeulen, 2010, Hargreaves, 1996; Van den Akker et al, 2006).
Our research mostly takes place in face-to-face and in work
practices

4. Learning analytics in the
workplace
Network Awareness Tool: Creating a social
learning browser
A web2.0 Tool that, informed by social network analysis and social
learning theory, aims to detect and raise awareness about
informal networked learning activities within organizations
A user generated tool to gather real time networked data on
learning topics that can be updated by the participants when
needed
Contribute to the understanding of informal workplace learning in
contemporary face-to-face and virtual environments
Capture and analyze traces of (social) informal learning in every
day life and work networks

5. NAT - connecting levels:
Dealing with multiple levels at once
D
Social learning browser

6. NAT - connecting levels:
Dealing with multiple levels at once
3 Main perspectives:
1. Theme’s – tag clouds – based on ‘sets’
defined by content – organizational level
2. Theme networks – visualization of the
relation within a ‘set’ – ‘group’ level
3. Ego-networks – individual network
relations per person and the sets

7. NAT - connecting levels:
What data to collect on each level?
• Individual level:
• Are their ways to combine individual learning analytics data of participants in a
virtual environment to add information to the individual level of a person’s social
learning activities?
• Tie level:
• Are there existing solutions to analyse the quality of a relation, based on
frequency and the quality of the interaction based on semantic analysis? (f.e.
length of discussions in a forum, levels of discussion topics).
• Network level:
• Are there existing solutions to analyse social learning activities based on
semantic analysis?
• Community level:
• Can we use tagging or rating systems to investigate the presence of a “shared
language”, “shared identity”, or “common ground”?

8. Conclusion
NAT
• Research tool in development
• Social (Learning) Browser
• ‘Neutral’ tool to be used for collecting SNA data
• Instant feedback of the development of social structures and
themes
• (Informal) Learning as a process of value creation

9. Future Plans
• Combining on and off-line
• Plugin in Learning Analytics dashboard (f.e. Sociallearn –
UKOU)
• Dynamic development of social structures & themes – time
slider –
• Improving social browsing by semantic analysis
• Analyzing user activity logs

10. Thanks for your attention!