Web-based Learning Solutions for Communities of Practice

Mining Unnoticed Knowledge in Collaboration Support Systems
use. More specifically, by identifying clusters of
users within a community, it is possible to reveal
intellectual, social or spiritual differences among
community members and the way these users selforganize
to smaller groups. The above remarks
may either be proven of vital importance to assure
the community’s survivability, or may possibly
expose the need to review the community’s current
structure and future. Furthermore, by applying
social network analysis on clusters, it is possible
to identify whether a social network is centralized
or not. Establishment of de-centralized networks is
considered a healthier network, since there are no
single points of failure. In that manner, members
that are found to have crucial impact on the group
activity or collaboration may be notified and assume
their corresponding duties and tasks, thus
making sure that information is being propagated
properly. Moreover, users that share common ideas
and tend to rate other users’ contribution similarly
are brought together close, since our proposed
framework reveals relationships that are yet to be
promoted. In such a way, communication among
members may be certified and collaboration can
persist, even if some communication problems
occur. Finally, one more issue that was taken under
consideration and may be subject of exploitation is
enlargement of legitimate peripheral participation
(Lave & Wenger, 2001). By this term, we refer to
the ability CoP has to attract newcomers and to
accommodate their ability unfolding.
If we take under consideration all of the above
scenarios of use, it is obvious that the estimated
benefits from our proposed framework can provide
great use to the members of the CoP. Analyzing
user actions, identification of user relationships
and extraction of several metrics regarding the
communication level among CoP members is a
promising technique in order to improve community
productivity and awareness; members
will have the chance to resolve issues faster and
more efficiently and members’ competencies will
emerge in a more natural way. In this paper we
describe a system that takes into account all of the
afore-mentioned operations. This assures that the
CoP will advance faster and more securely, while
its evolution remains under constant observation
and examination. In that way, a community may be
assured that its purpose is served more satisfyingly
and its members’ actions are clearly diffused.
CONCLUSION
In this paper, we presented a framework which can
be applied to a wide range of software platforms
aiming at facilitating collaboration among users.
Our motivation stems from the fact that contemporary
collaboration support environments suffer
from low user engagement. Having described the
basic characteristics of our prototype system, we
focus on a model which combines techniques from
both data mining and social network analysis
disciplines. More precisely, we formulated two
different clustering approaches in order to find the
values of some interesting metrics. Moreover, we
combined the outcomes of the proposed clustering
methodology with social network metrics. The
result of the above effort is to unfold meaningful
knowledge which resides at a CoP. This knowledge
may be used in a variety of ways, to enhance
the system ability and allow users communicate
(and thus collaborate) more effectively. It is our
belief that the whole framework is both general
and flexible.
It is necessary to underline that the exact tool
that will be responsible to present the outcome
of the analysis taking place in our framework is
only functionally outlined and is yet to be fully
described in later work. Future work plans include
the conduction of experiments with real data from
diverse CoPs using a particular collaboration
support tool (namely CoPe_it! – http://copeit.cti.
gr), and investigation of scenarios for a further
exploitation of the proposed framework.
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