![](bg3.png)
Gutierez, 1993).
Another common challenge to face is to move
away from the idea that a knowledge environment
should provide ready-made solutions. Grabbing a
few hints from here and there, then to adapt it to
your own context and to carve your own good
practice is also not necessarily obvious for everyone.
5.3 Knowledge Management
Challenges
You never start from scratch. Therefore, linking new
practices to your own way of doing things is a
challenge, not mentioning the technical challenge
you face when you already have databases that need
to become evolving databases if they were not
designed with such a philosophy.
The level of granularity of knowledge mentioned
above is certainly the hardest challenge, since the
utility of good practices strictly depends upon its
applicability to as many situations you face as
possible. If the learning objects are too detailed, they
will not serve much of a purpose. On the contrary, if
they are too general, they will not be seen as
bringing an added value to the situation.
The issue of validation of knowledge is also a
tricky one although in principle the idea behind
knowledge management is not to come up with a
validating process in the same way as you would for
scientific papers for instance. The term good
practice is carved precisely to suggest that,
depending on the context, a practice may be more
adequate than another one. However, some practices
are standard and therefore need to be validated as
“best practices”, while others are not (Argyris and
Schön, 1978).
Finally, the biggest challenge probably stems
from a huge confusion overwhelmingly present
between document management and knowledge
management. The paradigm behind the environment
developed here is the recursive loop between the
capture, organisation and reuse of good practices in
the form of yet new sharing scenarios. The added
value in the knowledge management process does
not stem from just a one time capture of knowledge
and its storing into a database but precisely its
progressive refinement through a recursive process
(Boder, 1992).
6 CONCLUSIONS
The design of such an environment must obviously
be based upon a careful analysis of users’ needs and
requirements. The platform emphasize concrete
hints, allowing for quick and easy solutions for the
user. But at the same time, the idea is to trigger
reflexion and to induce comparisons between the
various practices. Hence, the structure and the
material have been conceived to provide both ready-
made solutions but also to push the user to create his
or her own solution adapted to the specific context.
Clearly, this calls for an environment where you do
not find “the” best practice but instead a variety of
ideas to choose from.
The most critical requirement is clearly the user
friendliness and the relevance and the speed of
results of the search function. Here, the platform’s
efficiency is dependant upon the way the metadata
have been built in. Namely, the material shall not be
tagged too narrowly, again allowing the user to
compare between sometimes even contradictory
possibilities to address a challenge.
Finally, the two major lessons to be learned
when designing such a tool include first and
foremost a philosophy emphasizing blended
learning, anticipating that the platform shall be used
in parallel with face-to-face sessions where
complementary and more in-depth pieces of
knowledge may be shared and created. Secondly,
there is a tendency to believe that each context
should rely upon specific material whereas in fact
more generic knowledge may often be applicable
across domains and across topics.
REFERENCES
Argyris, C., Schön, D., 1978. Organisational Learning: a
Theory of Action Perspective, Reading, MA :
Addison-Wesley.
Boder, A., 1992. The process of reification in human-
human interaction. In Journal of Computer Assisted
Learning, vol. 8 no. 3. pp. 177-185.
Boder, A., 2006. Collective Intelligence: A Keystone in
Knowledge Management. In Journal of Knowledge
Management, Vol.10, No. 1. pp. 81-93.
Boder, A., Cavallo, D., 1990. An Epistemological
Approach to Intelligent Tutoring Systems. In
Intelligent Tutoring Media, vol. 1 no. 1. pp. 23-29.
Boder, A., Gardiol Gutierez, Ch., 1993. The JITOL model
as a way to capitalize professional know-how. In
ISMICK proceedings, Compiègne.
Davenport, T. H., Prusak, L., 1998. Working Knowledge.
How organizations manage what they know. Harvard
Business School Press.
Nisbett, R. E., Wilson, T. D., 1977. Telling more than we
can know: verbal reports on mental processes, In
Psychological Review, Vol. 84:231-259.
AKnowledgeSharingEnvironmentforSportManagement
105