Advances in E-learning-Experiences and Methodologies

Knowledge Discovery from E-Learning Activities
Figure 1. Virtual campus learning environment at UPA
BAckground
Knowledge discovery in databases (KDD), or just
knowledge discovery, is a subdiscipline of computer
science, which aims at finding interesting
regularities, patterns, and concepts in data. Usually
knowledge discovery has been related with
the global process spans from data to knowledge
using different statistical and heuristic techniques
called data mining techniques. However in the
current literature “knowledge discovery,” “data
mining,” and “machine learning” are often used
interchangeably. Recently, the data mining approach
has been applied in academic research.
Those applications include predictive or descriptive
modelling on educational data. Traditional
sources of data have been databases or questionnaires,
and more recently data from the Web.
Some of the works in educational predictive
models from databases or questionnaires are the
following: predicting whether the students graduates
in six years (Barker, Trafalis, & Rhoads,
2004), selecting students who would need remedial
classes (Ma, Liu, Wong, Yu, & Lee, 2000), and
predicting individual student’s final academic
achievement by modelling with decision trees
and hierarchical models (Gasar, Bohanec, & Rajkovic,
2002). Other predicting works on student’s
success, errors, or help requests are: predicting
the time spent in solving an exercise task by using
neural networks (Beck & Woolf, 1998), and
predicting in which word the student asks help in
reading English, where information of the student
(gender, approximated reading test results of the
day, help request behaviour) and word (length,
frequency, etc.) were processed (Beck, Jia, Sison,
& Mostow, 2003).
Regarding to descriptive data modelling
techniques applied to educational data, there are
several references in subjects such as analyzing
factors with affect academic success, desertion
and retention of students, mining navigation
patterns in log data, analyzing student’s competence
in course topics, analyzing student’s errors
in program codes, and mining student answers
from a Web-based tutoring tool database to get
pedagogically relevant information and to provide
feedback to the teacher (Kristofic & Bielikova,
2005; Merceron & Yacef, 2003; Romero, Ventura,
De Bra, & Castro, 2003; Salazar, Gosalbez, Bosch,
Miralles, & Vergara, 2004; Shin & Kim, 1999).
Data mining from Web data (Web mining) is a
new research area that pursues to understand the
information flow at the Web by means of automated
techniques for searching knowledge. This