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Adaptive Delivery of Trainings Using Ontologies and Case-Based Reasoning

  • Research Article - Computer Engineering and Computer Science
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Abstract

The delivery of trainings to diversified and constantly changing audiences is expensive and time consuming. We propose a computational approach addressing this issue by providing an adaptive training delivery framework. The approach relies on case-based reasoning (CBR) as a problem-solving method whereby cases are used rather than a prohibitive number of rules to store knowledge, i.e., experience. CBR is indeed accepted as one of the mainstream paradigms in artificial intelligence since it represents both knowledge and reasons about it. This choice is further motivated by the fact that the process of adaptation to different audiences is built on the traces left by previous learning tasks and practices that can be stored and automatically retrieved. Moreover, to address the crucial and pending issue of case indexing in CBR, we use ontologies to model and index the learning objects that represent the trainings core, thus reducing the retrieval process and improving search. Substantially, we develop an adaptation algorithm responsible for the required corrective actions in the adaptive delivery of trainings destined to diversified and heterogeneous learners.

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References

  1. Lenz, M.; Bartsch-Sporl, B.; Burkhard, H.D.; Wess, S. (eds.): Case-Based Reasoning Technology-From Foundations to Applications, LNAI 1400, Springer, Berlin 1998

  2. Aamodt A., Plaza E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)

    Google Scholar 

  3. Kolodner J.L.: Case-Based Reasoning. Morgan Kaufmann, USA (1993)

    Google Scholar 

  4. Aamodt, A.: Knowledge-intensive case-based reasoning in CREEK. In: Proceeding of ECCBR’04, pp. 1–15. Springer (2004)

  5. Bartlett, F.C.: Remembering: A Study in Experimental and Social Psychology, Chapter 10, Cambridge University Press, http://www.ppsis.cam.ac.uk/bartlett/TheoryOfRemembering.htm (1932)

  6. Schank, R.-C.: Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press, Cambridge (1982)

  7. Tulving, E.: Episodic and semantic memory. In: Tulving, E., Donaldson, W. (eds.) Organization of Memory. Academic Press, San Diego (1972)

  8. Smith, E.; Medin, D.: Categories and Concepts. Harvard University Press, USA (1981)

  9. Gruber T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)

    Article  Google Scholar 

  10. Nelson, P.; Viriato, M.; Antonio, B.; Farinha, J.T.: E-learning by experience-How CBR can help. In: Proceedings of the 6th WSEAS International Conference on Distance Learning and Web Engineering, Lisbon, Portugal (2006)

  11. Hamdi-Cherif, A.; Hamdi-Cherif, C (alias Kara-Mohammed).; Rasheedy, A.; Rasheedy, M.: Fuzzy agent for e-learner profile construction. Invited conference article. In: Recent Advances in Systems, Comm. and Computers-Selected Papers from the WSEAS Conference, pp. 61–74. Hangzhou, China (2008)

  12. Hamdi-Cherif, A.; Hamdi-Cherif, C. (alias Kara-Mohammed): Interactions between e-learning and agents. In: International Conference On Information and Computer Systems (ICICS’04), IEEE Computer Society, pp. 115–129, Dhahran, Saudi Arabia (2004)

  13. Heraud, J.M.: Pixed: Une approche collaborative de l’expérience et l’expertise pour guider l’adaptation des hypermédias, Ph.D. Thesis in Computer Science, Université Lyon I, France (2002)

  14. Mansouri, D.; Mille, A.: Raisonnement à partir de cas pour l’ingénierie de formation: une approche fondée sur l’expérience tracée du processus de formation, Plate-forme AFIA, Grenoble, France (2007)

  15. ADL (Advanced Distributed Learning), SCORM 2004, 4th edn, http://www.adlnet.gov/capabilities/scorm/scorm-2004-4th (2004)

  16. Institute of Electrical and Electronics Engineers: IEEE 1484.12.1 Standard for Learning Object Metadata. In: IEEE Standards Association, Institute of Electrical and Electronics Engineers, New York, NY, USA (2002)

  17. Hernandez, N.; Mothe, J.; Bertin, A.B.; Ramamonjisoa, O.; Ralalason, B.; Stolf, P.: Indexation multi facettes des ressources pédagogiques pour faciliter leur réutilisation. ftp://ftp.irit.fr/IRIT/SIG/2008_RNTI_HMRRS.pd (2008)

  18. Jinan, F.; Mohammed, S.: A framework for semantic transcoding of multimedia learning objects. In: Proceeding of the 5th WSEAS International Conference on Telecommunications and Informatics, pp. 13–18. Istanbul, Turkey http://www.seas.us/e-library/conferences/2006istanbul/papers/520-156.pdf(2006)

  19. d’Aquin, M.; Lieber, J.; Napoli, A.: Decentralized case-based reasoning for the semantic Web. In: Gil Y et al (eds.) ISWC 2005. Springer-Verlag, LNCS 3729, pp. 142–155 (2005)

  20. Keane, M.T.: Analogical asides of case-based reasoning. In: Wess, S.; Althoff, K.-D.; Richter, M.M. (eds.) Topics in Case-Based Reasoning, vol. 837. Springer, LNCS, pp. 21–32 (1994)

  21. Falkenhainer B., Forbus K.D., Gentner D.: The structure mapping engine: algorithm and examples. Artif. Intell. 41, 1–63 (1990)

    Article  Google Scholar 

  22. Richter, M.; Aamodt, A.: Case-based reasoning foundations. In: The Knowledge Engineering Review, vol. 20(3), Cambridge University Press, pp. 203–207 (2006)

  23. Wess, S.; Althoff, K.D.; Derwand, G.: Using k-d trees to improve the retrieval step in case-based reasoning. In: Wess, S.; Althoff, K.D.; Richter, M.M. (eds.) Topics in Case-Based Reasoning, vol. 837. Springer, LNCS, pp. 167–181 (1994)

  24. Russell, S.; Norvig, P.: Artificial Intelligence-A Modern Approach (AIMA), Chapter 2, Intelligent Agents, 3rd edn, Prentice Hall, (2011)

  25. Kendal S., Creen M.: An Introduction to Knowledge Engineering, 2nd edn. Springer, USA (2007)

    MATH  Google Scholar 

  26. Institute of Electrical and Electronics Engineers: IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. Institute of Electrical and Electronics Engineers, New York, NY, USA (1990)

  27. ISO, (International Organization for Standardization): JTC 1/SC 36-Information technology for learning, education and training. ISO, Retrieved (2012)

  28. Collier, G.; Robson, R.: E-learning Interoperability Standards. SunTM White Paper, SunTM Microsystems, Inc., 901 San Antonio Road, Palo Alto (2002)

  29. ADL (Advanced Distributed Learning): SCORM Users Guide for Instructional Designers, SCORM 2004, 4th edo. Version 8, p 55 http://www.adlnet.gov/wp-content/uploads/2011/12/SCORM_Users_Guide_for_ISDs.pdf (2011)

  30. ADL (Advanced Distributed Learning): SCORM Users Guide for Programmers, SCORM 2004, 4th edn. Version 10, p. 102. http://www.adlnet.gov/wp-content/uploads/2011/12/SCORM_Users_Guide_for_Programmers.pdf (2011)

  31. Path, I.; Maniu, I.M.; Maniu, G.; Damoan, S.: A conceptual framework based on ontologies for knowledge management in e-learning systems. In: 6th WSEAS International Conference on Education and Educational Technology, pp. 283–286, Italy (2007)

  32. Roth-Berghofer, T.: Knowledge Maintenance of Case-Based Reasoning Systems: The SIAM Methodology. PhD Thesis, University of Kaiserslautern (2002)

  33. Mille, A.: Raisonnement basé sur l’expérience pour coopérer à la prise de décision. Ph.D. Thesis in Computer Science, Université Jean Monnet, Saint Etienne, France (1995)

  34. Fuchs, F.; Mille, A.; Lieber, J.; Napoli, A.: Une première formalisation de la phase d’élaboration du raisonnement à partir par cas. 14ème Atelier de Raisonnement à Partir de Cas, Besançon, France (2006)

  35. Jaczynski, J.: Modèle et plate-forme à objets pour l’indexation des cas par situations comportementales : applications à l’assistance à la navigation sur le Web. Ph.D. Thesis in Computer Science, Université de Nice Sophia Antipolis, France (1998)

  36. Karoui, H.: Agent RàPC pour la gestion coopérative de bases bibliographiques personnelles. Plate-forme AFIA, Nice, France (2005)

  37. Jaczynski, M.; Trousse, B.: Broadway: a case-based system for cooperative information browsing on the World-Wide-Web. Collaboration between Human and Artificial Societies, LNCS 1624264-283 (1999)

  38. Melis, E.; Lieber, J.; Napoli, A.: Reformulation in Case-Based Reasoning. EWCBR’98, Springer, pp. 172–183 (1998)

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Authors and Affiliations

  1. Computer Science Department, College of Science, Ferhat Abbas Setif University (UFAS), 19000, Setif, Algeria

    Dounia Mansouri

  2. LIRIS CNRS, UMR 5202, Université Lyon 1, Lyon, France

    Alain Mille

  3. Computer Science Department, Computer College, Qassim University, PO Box 6688, Buraydah, 51452, Saudi Arabia

    Aboubekeur Hamdi-Cherif

Authors
  1. Dounia Mansouri
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  2. Alain Mille
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  3. Aboubekeur Hamdi-Cherif
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Correspondence to Aboubekeur Hamdi-Cherif.

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Mansouri, D., Mille, A. & Hamdi-Cherif, A. Adaptive Delivery of Trainings Using Ontologies and Case-Based Reasoning. Arab J Sci Eng 39, 1849–1861 (2014). https://doi.org/10.1007/s13369-013-0761-4

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  • DOI: https://doi.org/10.1007/s13369-013-0761-4

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