Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 243

 Utility Based Recommender Systems (Guttman 1998) compute their recommendations on
the match between the user's need and the available options that could maximize the
accomplishment of the expected utility.
 Hybrid Recommender Systems (Albadvi 2009, Burke 2002b): To overcome the
disadvantages of previously mentioned recommender systems sometimes is used
hybrid techniques which try to make useful the best of methods used in hybridization.
The most spread recommender systems are collaborative and content based systems both
have provided good results in different areas as tourism (Sebastia 2009), e-learning (Romero
2009), academic orientation (Castellano 2009b), etc.
In (Castellano 2009b) was introduced the use of collaborative filtering for academic
orientation, in the case of study of Spanish Academic System, by using marks as ratings of
user's profile that are filtered in order to support advisors in their orientation for the
students. In (Castellano 2009a) these processes were merged with fuzzy techniques
(Martínez 2008, Martínez 2007a, Zadeh 1965) to improve the comprehension of the results
by the advisors.
Even though the DSS, OrieB , presented in (Castellano 2009a, Castellano 2009b) provides good
results in general, it suffers the same weaknesses and limitations that any collaborative RS
(Herlocker 2004, Lee 2003, Pazzani 1999), such as, grey sheep, historical data, cold-start, scarcity ,
etc. Therefore, we detected a problem in OrieB , because academic systems are not static but
they suffer changes quite often and new subjects can appear due to modification or adaptation
of academic profiles. Hence these changes imply the appearance of cold start and historical data
problems decreasing the performance of the system for academic orientation.
In this chapter we aim to improve the performance of academic orientation support by
using a hybrid technique that supplies support even in those situations in which there is
scarcity information or new items. First, we should find what type of information would
be available when marks do not exist yet for new subjects and choose the techniques that
should be hybridizing with collaborative filtering to obtain good results in the academic
orientation situations in which such a filtering technique is not enough. In this case the
Competency based Education (CBE), that is an emerging curriculum model that tries to
satisfy the demands of learning contexts, by the developing competencies, enabling
students to act in a complex world in constant transformation (Zalba 2006), might play an
important role. A competence is the ability to perform effectively in a given situation, it is
based on knowledge but it is not limited to it (Perrenoud 1999). Competences are complex
knowledge and represent the know-how to integrate conceptual, procedural and
attitudinal knowledge. The current importance and relevance of the CBE is showed in
Tuning Educational Structures in Europe (Europe TUNING 2000). As well in different
countries this educational paradigm is being developed in secondary education and high
schools and students are now being evaluated focusing on competences. So the use of
competences will be very useful in our aims because they keep relevant information for
academic orientation.
Therefore, once we know the information available when the collaborative filtering is not
working our second step is to define a hybrid model that that hybridizes collaborative and
content-based techniques (CB), where the content-based model will be based on the textual
description of subject competences. This model will be implemented in a DSS for Academic
Orientation. In such a way the DSS can provide more and better support to the academic
advisors in their task.