Abstract
In adaptive E Learning systems, learner properties are often modeled to provide information about their preferences and their learning style. Thus, the learning style model is the most used personalization parameter in the modeling of learners. The major problem is how this model can be used to provide efficient learner modeling. In this paper, we are studying this important research topic to create a learner model that can facilitate the detection of learning style. The basic idea is to introduce into the proposed model a new field of information concerning the motivation about each dimension of the learning style model considered. To this end, the dimensions of Felder’s and Silverman’s learning styles model are considered. The motivation rate corresponding to each dimension is measured and then stored in the model built to allow immediate detection of the learning style by simply consulting the field associated with the motivation rate and without resorting to treatments dedicated to the detection of styles nor the use of classification techniques. The proposed modeling approach exploits the benefits of existing standards to be able to reuse other models, which makes it possible to add the proposed new information field, namely the field associated with the motivation rate. To represent and store the profiles of the learners the XML standard is used.
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Azzi, I., Laaouina, L., Jeghal, A., Radouane, A., Yahyaouy, A., Tairi, H. (2022). A Modeling Learner Approach for Detecting Learning Styles in Adaptive E Learning Systems. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-02447-4_37
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