Visualizing Informal

Networked Learning Activities

Bieke Schreurs, Chris Teplovs & Maarten de Laat

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.

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

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

NAT - connecting levels:Dealing with multiple levels at once

Social learning browser

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

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”?

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

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

Thanks for your attention!