provided by the theory of conceptual maps [63] and influence diagrams [75]. In [29,
42, 95], personalized Web-based learning systems were defined, applying Web usage
mining techniques to personalized recommendation services. The approach is based
on a Web page classification method, which uses attribute-oriented induction
according to related domain knowledge shown by a concept hierarchy tree.
2.2.4 Association Rules
Association Rules for classification, applied to e-learning, have been investigated in
the areas of learning recommendation systems [18, 98, 99], learning material
organization [89], student learning assessments [38, 45, 52, 54, 69, 70], course
adaptation to the students’ behaviour [19, 35, 50], and evaluation of educational web
sites [21].
Data Mining techniques such as Association Rule mining, and inter-session and
intra-session frequent pattern mining, were applied in [98, 99] to extract useful
patterns that might help educators, educational managers, and Web masters to
evaluate and interpret on-line course activities. A similar approach can be found in
[54], where contrast rules, defined as sets of conjunctive rules describing patterns of
performance disparity between groups of students, were used. A computer-assisted
approach to diagnosing student learning problems in science courses and offer
students advice was presented in [38], based on the concept effect relationship (CER)
model (a specification of the Association Rules technique).
A hypermedia learning environment with a tutorial component was described in
[19]. It is called Logiocando and targets children of the fourth level of primary school
(9-10 years old). It includes a tutor module, based on if-then rules, that emulates the
teacher by providing suggestions on how and what to study. In [52] we find the
description of a learning process assessment method that resorts to Association Rules,
and the well-known ID3 DT learning method. A framework for the use of Web usage
mining to support the validation of learning site designs was defined in [21], applying
association and sequence techniques [80].
In [50], a framework for personalised e-learning based on aggregate usage profiles
and a domain ontology were presented, and a combination of Semantic Web and Web
mining methods was used. The Apriori algorithm for Association Rules was applied
to capture relationships among URL references based on the navigational patterns of
students. A test result feedback (TRF) model that analyzes the relationships between
student learning time and the corresponding test results was introduced in [35]. The
objective was twofold: on the one hand, developing a tool for supporting the tutor in
reorganizing the course material; on the other, a personalization of the course tailored
to the individual student needs. The approach was based in Association Rules mining.
A rule-based mechanism for the adaptive generation of problems in ITS in the
context of web-based programming tutors was proposed in [45]. In [18], a web-based
course recommendation system, used to provide students with suggestions when
having trouble in choosing courses, was described. The approach integrates the
Apriori algorithm with graph theory.