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HYBRID ANN AND FIREWORKS ALGORITHM FOR REAL-TIME SOFTWARE RELIABILITY PREDICTION

Ivy Botchway Felix Larbi Aryeh Boniface Kayode Alese
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Personalized E-Learning Environment based on Ontology to Improve Students’ School Levels

by Mohammed Amine Alimam, Hamid Seghiouer
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 12
Year of Publication: 2013
Authors: Mohammed Amine Alimam, Hamid Seghiouer
10.5120/14625-2977

Mohammed Amine Alimam, Hamid Seghiouer . Personalized E-Learning Environment based on Ontology to Improve Students’ School Levels. International Journal of Computer Applications. 84, 12 ( December 2013), 1-6. DOI=10.5120/14625-2977

@article{ 10.5120/14625-2977,
author = { Mohammed Amine Alimam, Hamid Seghiouer },
title = { Personalized E-Learning Environment based on Ontology to Improve Students’ School Levels },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 12 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number12/14625-2977/ },
doi = { 10.5120/14625-2977 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:42.245856+05:30
%A Mohammed Amine Alimam
%A Hamid Seghiouer
%T Personalized E-Learning Environment based on Ontology to Improve Students’ School Levels
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 12
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Personalization of learning is the core of current research in the E-learning area. For effective learning, a personalized content provided to each student remains necessary. Students who use this educational web-based applications aim at learning knowledge which corresponds to their expectations. However, their school level and interests are different. Thus, the system should recommend to students a learning content that match the most their knowledge levels and is interesting enough to keep their attention. This paper describe a method enabling system to classify students depending on their school levels. This method will allow a semantic classification of profiles integrated within ontology. Accordingly, the system will be able to categorize students in a definite profile based on their school level and make a decision about the interfaces content recommended by each type of profile. To evaluate the effectiveness of the proposed approach, an experiment was conducted on students from middle school. The experimental results show that, such a system recommends to each student a field of study (FoS) whose subjects taught will match the best their skills. The E-Learning content based on those subjects will allow the enhancement of student's school level.

References
  1. Swan, K. (2003). Learning effectiveness: what the research tells us. In J. Bourne, & J. C. Moore (Eds. ), Elements of quality online education, practice and direction (pp. 1345). Needham, MA: Sloan Center for Online Education.
  2. Klasnja-Milicevic, A. , Vesin, B. , Ivanovic, M. , Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education 56, 885-899.
  3. Doucet, A. , Lumineau, N. , Berrut, C. , Denos, N. , Rumpler, B. , Rocacher, D. , Boughanem, M. , Soule-Dupuy, C. , Mouaddib, N. , Kostadinov, D. (2004). Action Specifique sur la Personnalisation de l'Information. Rapport de fin de contrat, Groupe MRIM - CLIPS-IMAG.
  4. Abbas, K. (2008). Personalized access to information system: Application to medical domain. Ph. D. thesis, INSA de Lyon.
  5. Ioannidis, Y. E. , Koutrika, G. (2005). Personalized systems: Models and methods from an ir and db perspective. In K. Bohm, C. S. Jensen, L. M. Haas, M. L. Kersten, P. - A. Larson, B. C. et Ooi (Eds. ) VLDB, p. 1365.
  6. Kathia Marcal de Oliveira, Firas Bacha, Houda Mnasser, & Mourad Abed (2013). Transportation ontology definition and application for the content personalization of user interfaces. Expert Systems with Applications 40, 3145-3159.
  7. P. Resnick and H. R. Varian, Recommender systems, Communications of the ACM, 40 (1997) 56-58.
  8. Prof. Dr. Arif ALTUN 'Ontologies for Personalization: A new challenge for instructional designers', Procedia - Social and Behavioral Sciences 64 (2012) 691 - 698, Elseiver.
  9. Sawang, S. , Newton, C. , & Jamieson, K. (2013). Increasing learners' satisfaction/intention to adopt more e-learning. Education + Training, 55(1).
  10. Manouselis, N. , Drachsler, H. , Vuorikari, R. , Hummel, H. and Koper, R. (2010) Recommender Systems in Technology Enhanced Learning, in Kantor P. , Ricci F. , Rokach L. , Shapira, B. (Eds. ), Recommender Systems Handbook: A Complete Guide for Research Scientists & Practitioners.
  11. Haiyang Jia, Minhong Wanga, Weijia Ran, Stephen J. H. Yang, Jian Liao, Dickson K. W. Chiu (2011). Design of a performance-oriented workplace e-learning system using ontology. Expert Systems with Applications, 38, 33723382.
  12. Abel, F. , Bittencourt, I. , Costa, E. , Henze, N. , Krause, D. , & Vassileva, J. (2010). Recommendations in Online Discussion Forums for E-Learning Systems. IEEE transactions on learning technologies, VOL. 3, NO. 2.
  13. N. Soonthornphisaj, E. Rojsattarat, & S. Yim-ngam (2006), Smart E-Learning Using Recommender System, Proc. Intl Conf. Intelligent Computing.
  14. Elyusufi, y. , Seghiouer, h. , Benkaddour, a. (2013). Customization of Human-Computer Interface Guided by Ontological Approach in Web 2. 0. International Journal of Computer Applications (0975 8887). Volume 81 - No. 5.
  15. R. Burke, 'Hybrid Recommender Systems: Survey and Experiments' User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331-370, 2002.
  16. Wiley D. 2000. 'The Instructional Use of Learning Objects' AECT.
  17. Mizoguchi, R. (1998). A Step Towards Ontological Engineering. 12lh National Conference on AI of JSAI, pp. 24-31. Online: http://www. ei. sanken. osaka-u. ac. jp/english/ step-onteng. html.
  18. Psyche, V. , 2007. Role of ontologies in ILE engineering : Case of assistance system to pedagogical design. Ph. D. Thesis, Department of Computer Science, University of Quebec in Montreal(UQAM).
  19. Noy, N. F. , & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology, Citeseer. BR, Kuo-Kuang Chu.
  20. Nilsson M. , Palmer M. et Naeve A. 'Semantic Web Metadata for e-Learning: Some Architectural Guidelines'. Proceedings of the 11th World Wide Web Conference (WWW 2002), Hawaii, USA, 2002.
  21. Ronald G. 'Sultana Guidance policies in the knowledge society. Trends, challenges and responses across Europe. A Cedefop synthesis report' Luxembourg: Office for Official Publications of the European Communities, 2004 (Cedefop Panorama series; 85 - ISSN 1562-6180).
  22. C. Mangard, A. Channouf. 'Do orientation decisions depend of social stereotypes? Under what conditions?' European Review of Applied Psychology, Volume 61, Issue 3, Pages 161-170, 2011, ELSEIVER.
  23. Cheng Chih Chang, Pei-Ling Chen, Fei-Rung Chiu, Yan-Kwang Chen 'Application of neural networks and Kano's method to content recommendation in web personalization', Expert Systems with Applications 36 (2009) 5310-5316, Elseiver.
  24. Liu, H. , Keselj, V. (2007). Combined mining of web server logs and web contents for classifying user navigation patterns and predicting users' future requests. Data and Knowledge Engineering, 61, 304-330.
Index Terms

Computer Science
Information Sciences

Keywords

Information systems Semantic Web Ontology domain model user model Personalization.

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