the ability to analyze participation / engagement throughout different levels and how these levels connect, interact and influence learning Another important aspect is dealing with the complexity of living practices in which people learn and the potential added value of learning analytics to raise awareness, help reflect on (social) learning behavior and to connect learners in networks and communities where value is being created.
Connecting Levels and Methods of Analysis in Networked Learning Communities
Visualizing InformalNetworked 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 valueThe 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 withpractitioners, 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 workplaceNetwork Awareness Tool: Creating a social learning browserA 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 organizationsA user generated tool to gather real time networked data on learning topics that can be updated by the participants when neededContribute to the understanding of informal workplace learning in contemporary face-to-face and virtual environmentsCapture and analyze traces of (social) informal learning in every day life and work networks
NAT - connecting levels:Dealing with multiple levels at onceD Social learning browser
NAT - connecting levels: Dealing with multiple levels at once3 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”?
ConclusionNAT • 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