Advances in E-learning-Experiences and Methodologies

Knowledge Discovery from E-Learning Activities
order to exploit and complement the advantages
of each one of techniques. This work is developed
in a novel research line that pursues to discover
student learning styles in order to feedback and
improve the e-learning system using no model
based methods. The understanding of learning
styles is more difficult in e-learning education
than traditional education environment, and
then research in this topic is always a challenge.
Mining patterns from data is a classical task employed
for a long time and it is especially useful
and suitable in the e-learning context due to the
new generating data processes come from Web
technological innovations.
The algorithms applied search for patterns
in the data and its output could be stored in a
knowledge based system. However the scope of
the chapter does not comprise the stage of knowledge
storage, so metadata are not included for
the output of the algorithms; although from the
decision rules obtained and using, for instance,
logic programming languages, the creation of
a knowledge base can be easily undertaken. In
addition, the set of decision rules, cluster descriptions,
and learning styles detected in this work
implicitly define a kind of informatics ontology
that could be used to share or reuse the discovered
knowledge, using an implementation format and
language. There are several references comparing
informatics ontologies and statistical approaches
as the included in this chapter (Caragea, Pathak, &
Honavar, 2004; Gomez-Perez & Manzano-Macho,
2004; Pils, Roussaki, & Strimpakou, 2006).
Some of the definitions of informatics ontology
are the following: the informatics ontologies define
the kind of things that exists in the application
domain, allowing no confusion of the terms and
symbols (Sowa, 2000), informatics ontologies
define a kind of explicit specification of a concept
set (Gruber, 1995), informatics ontologies are defined
as formal specification of a shared concept
set (Borst, 1997). The principal motivation of the
informatics ontologies is allowing sharing and
reusing of knowledge bases computationally, by
using a common vocabulary.
Informatics ontology can be defined in several
ways, but necessarily it includes a vocabulary of
terms and some specification of their meanings.
This includes definitions and specifications on
relationship between terms, so, in general, a
structure is imposed on the domain and term
interpretation is constrained (Fridman & Mc-
Guinness D., 2001; Uschold, King, Morales, &
Zorgios, 1998; Weigand, 1997). Thus, informatics
ontology can be a logic theory, a semantic formal
description, the vocabulary of a logic theory, and
a specification on conceptions.
dAtA PreProcessIng
A reliable data warehouse was created from the
historical (2001-2005) log Web data from the UPA.
For this end, different operations were applied to
Figure 2. Structure of the Web data