This document describes a Network Awareness Tool being developed to detect and analyze informal workplace learning. The tool aims to capture traces of social informal learning through everyday work interactions. It will use social network analysis to map learning networks and communities of practice within organizations. The tool collects data on learning topics, individual networks, and the strength and content of interactions to provide feedback on informal learning structures and themes. This will help understand informal learning and identify support needs to foster professional development. Future plans include combining online and offline data, integrating it with learning analytics dashboards, and improving social browsing and network analysis through semantic techniques.

1. -Network Awareness Tool -
Learning Analytics in the workplace:
Detecting and Analyzing Informal
Workplace Learning
Bieke Schreurs, Maarten de Laat

2. Our research context:
Informal workplace learning
• Tacit knowledge
• Hidden, spontaneous, aimed at solving work
related problems
• Important driver for professional development
• Hard to manage and reward its value
• Our research mostly takes place in face-to-face
and in work practices
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 research Context:
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).

4. Network Awareness Tool

5. Network Awareness Tool:
Creating a social learning browser
A web2.0 Tool that, informed by social network analysis,
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
Provide instant and understandable feedback to the
users
Interactive and Dynamic data
Gain insight into the dimensions of informal networks in
organizations

6. Theoretical Background:
Networked Learning
‘’Networked Learning is a form of informal learning
situated in practice, where people rely strongly on
their social contacts for assistance and
development’’

7. Theoretical Background:
Social Network Theory
Understanding the network structure can reveal
important evidence on the information flow and
shared knowledge within an organisation
The structural dimension of a network can be
investigated by using Social Network Analysis.

8. Theoretical Background:
Social Capital
Social Capital Theory provides a lens to look more
closely at the relational resources embedded in social
ties and how actors interact to gain access to these
resources

9. Theoretical Background:
Communities of Practice
The social learning dimension from a collective
perspective
referring to the development of a shared identity within a
network of people and the collective development of
a particular domain. A shared identity represents a
collective intention, mostly related to a certain
practice.

10. What data we collect
• Participant demographics
• Learning topics
• Ego networks around topics
• Quality of the Ego network
• Strength of the tie: frequency, quality
• Content of the interactions

11. http://portal.rdmc.ou.nl/nib

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

13. NAT - connecting levels:
How to translate it to Learing Analytics?
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”?

14. Conclusion
Research tool in development
Social (Learning) Browser
Instant feedback of the development of social structures
and themes
(Informal) Learning as a process of value creation
Detect multiple (isolated) networks in the organization
and connect ideas and stimulate participants to think
of solutions to support their own professional
development in certain domains.
Needed interventions can be discussed, based on the
results of the multi method approach

15. 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

16. Questions?

17. Thanks for your attention!