Advances in E-learning-Experiences and Methodologies

Knowledge Discovery from E-Learning Activities
ing and teaching styles, pedagogical innovation,
and informatics ontologies.
The balance between interactive and personal
activities is a critical factor for e-learning systems
(distributed passive learning (DPL) and distributed
interactive learning (DIL)). An interesting area of
research is the proposal of new e-learning activities
or determining the suitable mixture of those
activities considering, for instance, contents, multimedia
resources, and ubiquitous networks.
The quality of the discovered knowledge
is directly proportional to the cleanness and
relevance of data. E-learning processes could
generate a lot of useless information, so efficient
algorithms to filtering and summarizing data; to
resolve inconsistencies, to estimate missing data,
and solving data heterogeneity are valuable for
the knowledge discovery approach.
Pattern recognition is a wide area that includes
many kinds of machine learning algorithms. Independent
component analysis (ICA) algorithms,
as applied in the present chapter, have yielded
important results in areas as image filtering and
segmentation, brain to computer interface, and
electrocardiographic diagnosis. Recently the mixture
of ICAs has emerged as a flexible generating
model to arbitrary data densities using mixtures
of Gaussians or Laplacians distributions or nonparametric
distributions for the components.
Those ICA mixtures could be used to model data
or knowledge in the Web.
Usually the evaluation of the knowledge is
made by experts. Nowadays, aspects as novelty
or interestingness are estimated by novelty detection
algorithms. Those algorithms could be used
by intelligent agents in the Web in order to make
decisions considering user behaviours. In the field
of e-learning it is a novel approach.
Recently, second level patterns in data mining
have been studied. Those approaches have been
used in protein and ADN research, where the
results of a first level of data mining conforms a
huge knowledge domain. In Web applications that
kind of methods would be useful. In addition the
automatic conversion of knowledge or semantic
information obtained by Web mining techniques,
represented by structures as the decision trees, to
ontologies would make possible the exchange and
reuse of the domain knowledge. Methodologies
to create hierarchical structures of patterns are
suitable to create ontologies; to make that possible,
conversion procedures to translate statistical
information to standard data interchange formats
are needed. All of those approaches may contribute
to develop the semantic Web.
AcknowLedgment
Special thanks go to the Universidad Politécnica
Abierta personnel for giving the Web data and
information about the virtual campus. This work
has been supported by Spanish Administration
under grant TEC 2005-01820.
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