Web-based Learning Solutions for Communities of Practice

Mining Unnoticed Knowledge in Collaboration Support Systems
the following properties:
◦ Avg(DocumentRate ij (k))≡null if author
k≠j, or user i has not ranked document
k.
◦ Affinity ij ≠Affinity ji i,j (non-reflective
affinity)
This means that there are cases, where the user
affinity is not reciprocal. This remark is of great
importance and will be taken into consideration
later when the discussion concerning the adopted
clustering technique will continue.
A Modeling Approach
As described above, we use a set of tools and
metrics that can be found useful to extract information
regarding a network of users forming a CoP
(Wasserman & Faust, 1994), (Han & Kamber,
2001), (Hand, Mannila & Smyth, 2000), (Bock,
1989):
1. Clusters: are groups of entities (users in our
occasion), in a way that objects in one cluster
are very similar, while objects in different
clusters are quite distinct. Each cluster can
combine various plausible criteria (Bock,
1989). Sometimes there are some requirements
about the objects in the cluster such
as: a) to share the same or closely related
properties; b) to show small mutual distances
or dissimilarities; or c) to have “contacts”
or “relations” with at least one other object
in the cluster.
2. Degree: expresses the number of people a
CoP member is connected to. Members with
central role may be considered of major
importance in the network hub, since they
keep the CoP tightly connected.
3. Betweenness: While someone may be tightly
connected with someone else, it might be
the case that some CoP members express
the CoP’s integrity “better”. People with
high betweenness degree are considered to
be obliging and may express a collective
consensus effectively. So it can be expressed
by the total number of shortest paths between
pairs of members that pass through
a member.
4. Closeness: We already described affinity as
a measure to describe one member’s opinion
about another. Closeness is extracted from
the above in order to describe an “overall”
affinity, or else closeness regarding the rest
of the CoP members. So the closeness of
a member can be expressed by the total
number of links that a member must go
through in order to reach everyone else in
the network.
Mining Hidden Knowledge
Collaboration support systems provide to users
the opportunity to share and express their opinion
about several issues. These systems are used by
communities of users and each community has
spaces where users can collaborate with each other.
Each user can participate in several spaces of a
selected community, where he can take actions
such as read/write or make a comment or rank a
given idea. All these actions turn out to be useful
input data in order to characterize and model
users. Thus, we can separate them into groups
according to their relationship so as to give out
some useful conclusions.
We aim at separating users into groups in order
to provide them with better services such as recommendations.
The most common technique for this
purpose is clustering. Clustering can be defined
as the process of organizing objects in a database
into clusters/groups such that objects within the
same cluster have a high degree of similarity, while
objects belonging to different clusters have a high
degree of dissimilarity (Anderberg, 1973), (Jain
& Dubes, 1988), (Kaufman & Rousseeuw, 1990).
Generally speaking, clustering methods about
numerical data have been viewed in opposition
to conceptual clustering methods developed in
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