Optimisation à l’aide de styles d’apprentissage dans les applications hypermédia adaptatives
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Date
2019
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Abstract
Adaptive Hypermedia Systems (AHS) are Hypermedia Systems capable of adapting
the presentation of content, information formats and navigation options to the
characteristics of their potential users. Their goal is to provide the main information
and the most appropriate formats to each user. In AHS, several adaptations can be
found for a single learner. In this sense, the most appropriate information is the
information most adapted to a learner. So the question is: What is the most optimized
adaptation for a learner?
Some AHS have incorporated cognitive styles or learners' learning styles as
distinctive features of adaptation. This new way of adjusting information to each type of
learners takes into account the fact that they do not all reach the same level of
performance with the same type of education; they require learning strategies tailored
to their ways of dealing with and perceiving information.
AHS based on learning styles use different models of these styles to offer different
ways of adaption. These systems consider that adapting to learning styles is an
important task that must be taken into account during the design phase of the
pedagogical sequences.
However, AHSs based on learning styles cannot guarantee that the adaptation
provided to the learner is the most appropriate adaptation for him. So the questions
asked in this area are: Is the adaptation provided to the learner the most appropriate
adaptation for him? How to optimize the adaptation provided to the learners? The
objective of our work is to propose an adaptive hypermedia system based on learning
styles to deliver optimized adaptation to different learners
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Research Subject Categories::MATHEMATICS::Applied mathematics::Optimization, systems theory