Terry Anderson introduces some key distinctions in collective p2p dynamics. It’s part of an essay on networked modes of learning.
“the model illustrates three levels of aggregation of learners in either formal or informal learning.
The most familiar level is the group. Groups are cohesive and often have formal lines of authority and roles, such as designated chair/chairperson, team leader teacher, enrolled student etc. Groups consist of individuals who see themselves as part of that group. Groups are often structured around particular tasks or activities that may be term-based or ongoing. Groups may institute various levels of access control to restrict participation, review of group artifacts or transcripts to members so as to provide a less public domain in which to operate. Group members often use and create opportunities to meet face-to-face or online through group synchronous activities. Groups are more or less tightly knit teams of individuals who are committed to each other and usually to a task or tasks. Classic examples of groups include online education classes and short or long term business teams.
The second level of the â€œmanyâ€ is the network. Networks connect distributed individuals. (Koper, Rusman& Sloep, 2005) define A Learning Network as â€œan ensemble of actors, institutions and learning resources which are mutually connected through and supported by information and communication technologies in such a way that the network self-organizesâ€(P. 18). Learners may be connected to other learners either directly or indirectly and may not even be aware of all those who form part of the wider network. The shape of the network is emergent, not designed. Most of us are members of many networks. Some are associated with religions, (church congregations), sports (home town fans), hobbies and interests (car clubs) vocations (school teachers or members of the chamber of Commerce) and many other networks. Entry and exit to networks is usually easy and persons drift in and out of network activity and participation based on relevance, time availability and other personal constraints. Many of the social networking sites such as FaceBook, Linked In and MySpace are recent web examples of network support and facilitation tools, but earlier email lists and threaded discussions can also support networked learning.
The final level of aggregation of the Many is collectives. Collectives are machine-aggregated representations of the activities of large number of individuals. They achieve value by extracting information from the individual, group, and network activities of large numbers of networked users. Commercial examples of collectives include recommender systems such as Amazonâ€™s book recommendations that are derived from aggregating and comparing books I have ordered with the purchases of thousands of others and deriving recommendations for further purchase. There are many so called web 2.0 applications that create value through aggregation and analysis of collective activities such as user clickthroughs (Google Pageranks), information contributions (Wikipedia), photo and video tags and downloads (Flickre, Utube), article evaluations (Digg, SlashDot) and consumer rating services (ratemtyteacher.ca). Collective behavior can be as easy to extract as mere participation on the Net at individual, group or network levels. This data is harvested and aggregated to create collective knowledge. For example storing oneâ€™s favorite net resources on a social bookmarking site such as del.icio.us can have individual benefit as the resource can easily be retrieved, organized and managed by that individual owner. These resources, especially when they are aggregated with recommendations from others, could be very useful to group or network members. Moreover, when large numbers of resources are sorted, annotated and rated by many, the resultant resource listing gains considerable collective value.”
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