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

References

• [1] L. de Marcos, J.-J. Martinez, J.-A. Gutierrez, Swarm intelligence in e-learning: a learning object sequencing agent based on competencies, The 10th annual conference on Genetic and evolutionary computation, 2008 (2008), 17--24
  • View Article
  • Google Scholar


• [2] J. El Bouhdidi, M. Ghailani, A. Fennan, An intelligent architecture for generating evolutionary personalized learning paths based on learner profiles, J. Theoretical Appl. Infor. Tech. (JATIT), 57 (2013), 294--304
  • Google Scholar


• [3] M. Fahim, A. Jakimi, L. El Bermi, Pedagogical Scenarization for Virtual Environments for Training: Towards Genericity, Coherence and Adaptivity, Int. J. Adv. Eng. Res. Sci. (IJAERS), 12 (2016), 96--103
  • View Article
  • Google Scholar


• [4] P. Fuchs, G. Moreau, Le traité de la réalité virtuelle, Les Presses de l'Ecole des Mines de Paris, Paris (2006)
  • View Article
  • Google Scholar


• [5] M. Ghailani, J. E. Bouhdidi, A. Fennan, Towards an adaptive e-learning solution based on ontologies and competencies approach, Int. J. Comput. Appl., 98 (2014), 42--52
  • View Article
  • Google Scholar


• [6] P. Karampiperis, D. Sampson, Automatic learning object selection and sequencing in web-based intelligent learning systems, Idea Group Inc., 2006 (2006), 56--71
  • View Article
  • Google Scholar


• [7] A. A. Kardan, M. A. Ebrahim, M. B. Imani, A new personalized learning path generation method: Acomap, Indian J. Sci. Res., 5 (2014), 17--24
  • View Article
  • Google Scholar


• [8] A. Kumar, J. E. Nalavade, V. Yeola, V. Vivek, Y. Srivastava, An adaptive learning system based on ant colony algorithm, Int. J. Soft Comput. Eng. (IJSCE), 3 (2013), 1--3
  • View Article
  • Google Scholar


• [9] C. T. Lin, C. L. Chin, Using fuzzy inference and cubic curve to detect and compensate backlight image, Int. J. Fuzzy Syst., 8 (2006), 2--13
  • View Article
  • Google Scholar


• [10] C. T. Lin, K. W. Fan, C. M. Yeh, H. C. Pu, F. Y. Wu, High-accuracy skew estimation of document images, Int. J. Fuzzy Syst., 8 (2006), 119--126
  • View Article
  • Google Scholar


• [11] F. Lin, P. Holt, S. Leung, Q. Li, A multiagent and service-oriented architecture for developing adaptive e-learning systems, Int. J. Cont. Eng. Edu. Life Long Learning, 16 (2006), 77--91
  • View Article
  • Google Scholar


• [12] E. H. Mamdani, S. Assilion, An Experiment in Linguistic Synthesis With a Fuzzy Logic Controller, Int. J. Man-Machine Stud., 7 (1975), 1--13
  • View Article
  • Google Scholar
  • MATH


• [13] N. Marion, Modélisation de scénarios pédagogiques pour les environnements de réalité virtuelle d'apprentissage humain, PhD Thesis (Universite de Bretagne Occidentale), France (2010)
  • View Article
  • Google Scholar


• [14] A. Naji, M. Ramdani, Using the ant colony algorithm to establish the best path of learning activities, Appl. Math. Sci., 7 (2013), 3873--3881
  • View Article
  • Google Scholar


• [15] E.-A. Ouraiba, A. Chikh, A. Taleb-Ahmed, Z. El Yeb dri, Automatic personalization of learning scenarios using svm, 2009 Ninth IEEE International Conference on Advanced Learning Technologies, 2009 (2009), 183--185
  • View Article
  • Google Scholar


• [16] Q. D. Pham, A. M. Florea, Adaptation To Learning Styles In A Multi-Agent ELearning System, Internet Learning, 2 (2013), 10 pages
  • View Article


• [17] M. Pushpa, ACO in e-learning: Towards an adaptive learning path, Int. J. Comput. Sci. Eng. (IJCSE), 4 (2012), 11 pages
  • Google Scholar


• [18] S. Talhi, M. Djoudi, S. Ouadfel, S. Zidat, Un Canevas de Tuteur Intelligent Hypermédia pour L'Apprentissage à Distance Universitaire, Proc. 4th International Conference on Computer Integrated Manufacturing, 2007 (2007), 5 pages
  • View Article
  • Google Scholar


• [19] G. Valigiani, E. Lutton, C. Fonlupt, P. Collet, Optimisation par" hommilière" de chemins pédagogiques pour un logiciel d'e-learning, Technique et Science Informatiques, 26 (2007), 1245--1268
  • View Article
  • Google Scholar


• [20] M. Wallace, S. Ioannou, K. Karpouzis, S. Kollias, Possibilistic rule evaluation: A case study in facial expression analysis, Int. J. Fuzzy Syst., 8 (2006), 219--223
  • View Article
  • Google Scholar


• [21] L. A. Zadeh, Fuzzy set, Information and Control, 8 (1965), 338--353
  • View Article
  • MathSciNet
  • Google Scholar


• [22] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning--I, Information Sci., 8 (1975), 199--249
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH