Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 255

Now, with subjects and students profiles, we will see how a CBRS would work in order to
make a recommendation.

5.2 Neighborhood formation
To provide recommendations, system needs to find those subjects that require a competence
degree such that user had already developed. This is not exactly to find subjects with
profiles equal to a student profile, but to find subjects profiles with required level of
competences equal or lower that level in user profile. For example, looking at Table 4 we
could think that a student with 5 in all competences will not be recommended any subject as
he or she do not match well with the levels specified. However, the reality is that this
student is able of studying any of them, because the requirements are fulfill enough.
To solve this question, we need a similarity measure which treat as well student which
exceed requirements as students that simple fulfill them. For this reason we will not use
similarity measure explained before, the cosine, and we will use a normalized variant of the
Euclidean distance upgraded in order to follow this guideline, hence if student has a greater
level for the desired competences, the similarity will give a positive result.
If student only need to fulfill the competence but system must take into account the rest of
them, we will chose as competence development level the minimum between the student's
value and de required for the subject so that it will be the same if student simply fulfill the
requirement or overpass it anyway. This is achieved by using the minimum between the
level required for the subject and the level accomplished by the student, instead of using
only the level accomplished. This way the system treats equally those subjects with
overpassed and simply fulfilled competence requirements, giving priority to those
competence requirements not accomplished.
Consequently, let r i,c be the required level of development for competence i in subject c , and
v i,s the computed value for student s in competence i. A is the amplitude of the value of
development domain calculated as A = b - a , being b the top limit and a the bottom limit of
such domain. In our case, interval of values used is between 1 and 5, both included.

( r  i ( r , v ))

ic , ic , is ,

n
i



2

 1

A

 

ucs ,  (3)

n

Equation 3 computes the similarity measure able of comparing student's capabilities and
subject's requirements. The greater the value obtained the greater prepared will be the
student to course that subject.

5.3 Top-N list generation
This equation will be applied to those subjects belonging to the target grade which student
has to study next. The most often used technique for the generation of the top- N list is the
one that select the N most valuated candidates with the similarity measure.
Once these N candidates are selected, they are presented as recommendation by the system.

6. Hybrid-OrieB: CB and CF in academic orientation
In section 4 was overviewed OrieB (Castellano 2009b), a web based Decision Support
System built to support Spanish advisors in their task of helping students which modality to