Personalization of learning situations within a virtual environment for training based on fuzzy clustering

Mohamed Fahim, Abdeslam Jakimi, Lahcen El Bermi


Advances in networks, computers and multimedia technology have changed traditional
methods for learning and skills training. Nowadays, Virtual Environments for Training (VET) have
been popular, they can provide an environment where virtual reality can be used to create
interactive interfaces and real-time software that can control every response and action made
by the user. VET have proven to be advantageous to put learners into varied training
situations to acquire knowledge and competencies, especially when these situations are taking place in uncontrolled circumstances, or when they are dangerous, unrealizable, or expensive to establish in reality. However individual learners find it difficult to select suitable
learning situations for their particular situation because often, there is no personalized service
to response to the user needs. Personalization of learning in a VET is a very important way of
improving the effectiveness and the quality of the training, yet it also a complex process that
requires consideration of several factors such as learner‘s profiles. The goal is to associate
suitable learning situations to each learner based on his profile.
However, personalization of learning becomes an issue with the uncertainty and imprecision
of data that may contains a learner profile. To address this issue, this study is an attempt to
integrate a fuzzy clustering into the process of the personalization of learning.

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