Academic Orientation Supported by Hybrid Intelligent Decision Support System - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 259

Fig. 7. Core subject difficulty advising

7. Concluding remarks

In this chapter we have introduced the problem of Academic Orientation, presented a
system which make use of Collaborative Filtering techniques in order to recommend
students an academic path by using their marks, and studying its limitations, proposed a
hybrid CB and CF system in order to overcome the problems of scarcity and new item
problem.
This system helps advisors in their task of supporting students and opens a matter of study
in the Academic Orientation with CBE in which academic profiles are going to be more
flexible and systems more capable of giving better recommendations for students in matter
of improve and develop capabilities.
The origin of upgrading the support model for academic orientation with content based
techniques is because of the recent adaptation of the Spanish Academic System to the
Competence Based Education. This change provoked the appearance of new subjects and
profiles that CF models in OrieB cannot managed because of the lack of data.
Consequently to overcome the cold start problem with these new subjects and due to the
available information the more suitable model to achieve the goal of supporting advisors was
the hybridizing with a content based model which provides a higher coverage but regarding
the accuracy we need to wait to obtain real data sets. Additionally this upgraded version is
ready to be extended and useful amid the ongoing changes of the academic system.
Eventually, we want to highlight though OrieB is performing pretty well, there exist
different challenges that should guide our research in the future:
 OrieB does not take into account subjective information provided by students such as

preferences, yet. So the system should be able to include not only information relative
to their academic tour but also subjective and own information .

 Information provided by the system should not directly guide students because some

reasoning about the results are necessary, so only advisors can use OrieB. More visual
and self-explicative recommendations would be needed in this sense not only for
allowing students using the system but also for providing advisors a better way of
exploring and explaining academics alternatives to students.

 So far OrieB is focused on secondary and Baccaulerate grades. It seems interesting its

extension to higher education.

8. References

Adomavicius, G. & Tuzhilin, A. (2005) Toward the next generation of recommender
systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions
on Knowledge and Data Engineering, 17(6), 734-749.