![](bg4.png)
Recall that data mining is both an art and a
science, and whatever we generate by means of data
mining technologies and tools, we only do arrive at
hypotheses. There can not be any guarantee at all
that models (like, e.g., decision trees) generated over
given data behave as successful as expected over
other data in the future.
There is a need to verify generated models. How
to do that appropriately, which alternatives do exist,
and what the results mean and how they are backed
up by statistics, can be studied in DaMiT – another
case of learning when doing.
Figure 5: Verifying a Generated Decision Tree
The present Figure 5 shows a verification result
for the decision tree of Figure 4 generated by means
of the C4.5 implementation of QuDA.
Once a model has been established, as shown in
Figure 6, it can be exported from the generation tool
and saved for use in the future.
Figure 6: A Solution to the Application Problem
XML standards like PMML allow for an integra-
tion into an enterprise’s IT infrastructure.
There is no closing sentence about learning when
doing, because in areas like knowledge discovery
and data mining, learning never ends. Systems like
DaMiT are further developed to support this type of
lifelong learning.
5 SUMMARY & CONCLUSIONS
On the one hand, the Internet in pervading the world
has changed our daily life, and it is currently
changing human learning at all stages ranging from
schools through higher education to training in
enterprises, research labs and governmental institu-
tions and to lifelong learning. Technology enhanced
learning is providing quite new opportunities.
On the other hand, there is the obviously eternal
gap between academia and practice which appears as
a certain reluctance to e-learning in practice.
Despite these obvious difficulties, we are at the
cusp of closing the gap. As exemplified in the data
mining domain, even academic e-learning has the
urgent need of doing, i.e. learning by doing. In
practice, for sure, there is the need of knowledgeable
doing which leads to learning when doing. Enter-
prise application integration will allow for a proper
dovetailing of learning and doing. Data mining may
be an area worth for doing it now.
The present paper is based on work of colleagues
and friends from 11 academic institutions and draws
benefit from many other partners using the DaMiT
system in their educational practice.
Note that as a courtesy to interested readers,
there is an extended version of this paper available
(see http://www.dfki.de/~jantke).
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LEARNING BY DOING AND LEARNING WHEN DOING: DOVETAILING E-LEARNING AND DECISION
SUPPORT WITH A DATA MINING TUTOR
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