Proposal for an Automatic Identification Model of Learning Styles
Sanae El Attar1, Souhaib Aammou2, Az-Eddine Nasseh3
1Sanae El Attar, LIROSA, Abdelmalek Essaadi University, Faculty of Sciences, Tétouan, Morocco.
2Prof. Souhaib Aammou, LIROSA, Abdelmalek Essaadi University, Faculty of Sciences, Tétouan, Morocco.
3Prof. Az-Eddine Nasseh, LIROSA, Abdelmalek Essaadi University, Faculty of Sciences, Tétouan, Morocco.
Manuscript received on November 03, 2014. | Revised Manuscript Received on November 19, 2014. | Manuscript published on November 20, 2014. | PP: 1-3 | Volume-2, Issue-12, November 2014. | Retrieval Number: L05321121214/2014©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Hypermedia environments are becoming essential tools to enhance the educational value in teaching. This use is seen by facilitating the coming of the web world that offers us the opportunity to access hypermedia resources on the Web. The implementation of some educational activities in the form of hypermedia, can enhance the learning of cognitive skills in some learners. However, several LMS (Learning Management Systems) offer non-adapted to different types of learners learning activities. Now a Adaptive educational hypermedia system well designed, can generate varied and adapted to each profile educational activities. The consideration of values is very important to get to offer appropriate activities, and produce appropriate feedback. If this system called automatic identification of learning styles which is the subject of this document model, taking into account key factors such as learner preferences, values, characteristics and types of feedback, is to arrive interpret preferences peculiarities that distinguish each user. So we group a set of patterns each with its appropriate weight for each learner, through which one can determine the corresponding values of each characteristics in Learning Style Model. Once this is accomplished, we come to calculate the value of distinct preference for each profile, and the value of the confidence level based on the availability of data on each learner associated to each characteristic. To validate our model, which is still in experimental stage, we stage of implementation of the necessary tools. Once confirmation is made, the model will be used as an analytical tool.
Keywords: Adaptive hypermedia system, learner model, learning styles.