ALS_CORR[LP]: An adaptive learning system based on the learning styles of Felder-Silverman and a Bayesian network

The aim of this paper is to present the adaptive learning system called ALS_CORR[LP]
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. This system belongs to a very specific class of the e-learning systems, which is the adaptive learning ones. In fact they have the ability to adapt the learning process according to each learner needs, learning styles, objectives, etc. ALS_CORR[LP] is based on the learner prerequisites and the learning styles of Felder-Silverman, to design the learner model. As for the domain model, it is designed according to the recommendations of the differentiated pedagogy, which advocates creating multiple versions of the same learning object. Finally in order to ensure the adaptation inside the system, a Bayesian network, to match the designed learning object with the specifics of the learner profile was developed. It is also necessary to emphasize, that the major feature of the system is, its ability to correct the generated learning path in case of a failure in the evaluation phase. The learning path relevance is questioned, based on a recommendation system which enables updating the initial profile, or recommending the most relevant versions of the learning object, in case where the similarity calculation in behavior, reveals that the observed behavior in the system does not fit the initial profile description.