Personalization of learning activities within a virtual environment for training based on fuzzy logic theory

Volume 3, Issue 1, pp 12--17 http://dx.doi.org/10.22436/mns.03.01.02

Publication Date: July 30, 2019 Submission Date: June 03, 2017 Revision Date: June 01, 2018 Accteptance Date: June 10, 2018

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Authors

Fahim Mohamed - Software Engineering and Information Systems Engineering Team UMI , Faculty of Sciences and Technology, Errachidia, Morocco. Jakimi Abdeslam - Software Engineering and Information Systems Engineering Team UMI , Faculty of Sciences and Technology, Errachidia, Morocco. El Bermi Lahcen - Software Engineering and Information Systems Engineering Team UMI , Faculty of Sciences and Technology, Errachidia, Morocco.

Abstract

The development of computers and multimedia technology has opened up new possibilities for training based on virtual reality. Virtual reality is the most powerful extension of simulation based systems. In virtual reality there is a move to three dimensional, multi-sensory interfaces. A virtual environment for training (VET) can be defined as a computer-generated environment based on virtual reality, to simulate the real world. Learning through a VET can personalize learning needs for learners to promote the quality of learning. However, learners can't be provided with appropriate learning activities because often there is no personalized service to respond to each learner's particular needs. The obvious solution is to generate learning activities based on each learner's profile. Yet it is a complex process, especially with the inaccuracy of data that may contains a learner's profile. The main goal of this paper is to associate suitable learning activities to each learner based on his profile, to do so, we propose to employ fuzzy logic technique, and the fuzzy inference system to handle reasoning under uncertainty and inaccuracy which is one major issue of great concern in learner model design.

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Keywords

• Virtual environments for training
• learning activities
• fuzzy logic
• fuzzy inference system

MSC

•  68T05
•  68T27

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