Title :
An automatic and customized support based on artificial ants
Author :
Ammor, F.Z. ; Bouzidi, D. ; Elomri, A.
Author_Institution :
Fac. of Sci., Ain Chock Hassan II Univ., Casablanca, Morocco
Abstract :
In recent years, the distance learning has become as a new approach to complete the training. Unfortunately, and despite all its advantages, it still suffers of a significant drop-out rate which is due, in particular, in the absence of an appropriate monitoring and support in learning. We propose to achieve an intelligent tutoring system able to classify learners into groups representing their learning profiles through their interactions, by taking into account their evolution during the learning. For then associate each learner with a tutor of the same profile that can react to its actions, providing them support and appropriate accompaniment throughout their learning The classification of learners in the profiles was carried out following several steps. Indeed, the profile derived by the classification algorithm cannot be considered as a correct profile, we have to consider the mood of learner, the personal and professional constraints that can significantly influence his performance and behavior. Thus, it is essential to continue to monitor his progress and to deduce his exact profile following behavior which dominates throughout his apprenticeship. However, the need of support by a tutor with a good knowledge of the needs and expectations of learner appears in his first contact with the apprenticeship system. Thus we have to be able to deduce his exact profile while associating an intelligent tutor providing ongoing support and as relevant as possible from the start of training. Due to the large number of learners, we propose a method for classification based on the behavior of ants; it is an improvement of the unsupervised algorithm ANTCLUST. The choice landed on the artificial ants because they are known for their dynamic and evolutionary aspects that will allow us to continue to supervise the development of learners.
Keywords :
distance learning; intelligent tutoring systems; pattern classification; professional aspects; training; apprenticeship system; artificial ant; distance learning; dynamic aspect; evolutionary aspect; intelligent tutoring system; learner classification; learner profile; learning; personal constraint; professional constraint; training; Bioinformatics; Classification algorithms; Genomics; Monitoring; Vectors; E-Learning; adaptation; artificial ants; behavior; classification; learner profile; tutoring;
Conference_Titel :
Information Science and Technology (CIST), 2012 Colloquium in
Conference_Location :
Fez
Print_ISBN :
978-1-4673-2726-8
Electronic_ISBN :
978-1-4673-2724-4
DOI :
10.1109/CIST.2012.6388055