Commenced in January 2007
Paper Count: 30576
Integrating Computational Intelligence Techniques and Assessment Agents in ELearning Environments
Abstract:In this contribution an innovative platform is being presented that integrates intelligent agents and evolutionary computation techniques in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting: I) various assessment agents for e-learning environments, II) a specific resource retrieval agent for the provision of additional information from Internet sources matching the needs and profile of the specific user and III) a genetic algorithm designed to extract efficient information (classifying rules) based on the students- answering input data. The agents are implemented in order to provide intelligent assessment services based on computational intelligence techniques such as Bayesian Networks and Genetic Algorithms. The proposed Genetic Algorithm (GA) is used in order to extract efficient information (classifying rules) based on the students- answering input data. The idea of using a GA in order to fulfil this difficult task came from the fact that GAs have been widely used in applications including classification of unknown data. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074930Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1371
 K. C. Giotopoulos, C. E. Alexakos, G. N. Beligiannis and S. D. Likothanassis, "Computational Intelligence Techniques and Agents- Technology in E-learning Environments", International Journal of Information Technology, Volume 2, Number 2, 2005,ISSN:1305-2403, pp. 147-156.
 S. Buraga, "Developing Agent-Oriented E-Learning Systems", in Proceedings of The 14th International Conference on Control Systems And Computer Science - vol. II, I. Dumitrache and C. Buiu, Eds, Politehnica Press, Bucharest, 2003.
 A. Angehrn, T. Nabeth, L. Razmerita, and C. Roda., "K-inca: Using artificial agents for helping people to learn new behaviors", in Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT 2001), Madison USA, August 2001, pp. 225-226.
 A. Andronico, A. Carbonaro, G. Casadei, L. Colazzo, A. Molinari, and M. Ronchetti, "Integrating a multi-agent recommendation system into a Mobile Learning Management System", in Proceedings of Artificial Intelligence in Mobile System 2003 (AIMS2003), October 12, Seattle, USA.
 O. R. Zaiane, "Building a Recommender Agent for e-Learning Systems", in Proceedings of the International Conference on Computers in Education, Auckland, New Zealand, December 2002, pp. 55-59.
 V. Pankratius, O. Sandel, W. Stucky, "Retrieving Content with Agents in Web Service E-Learning Systems", Symposium on Professional Practice in AI, IFIP WG12.5 - in Proceedings of the First IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI), Toulouse, France, August 2004.
 F. Bellifemine, G Caire, A. Poggi and G. Rimassa, "JADE A White Paper", Telecom Italia EXP Magazine, Volume 3, Number 3 September 2003
 Foundation of Intelligent Physical Agents (FIPA), http://www.fipa.org/
 FIPA ACL Message Structure Specification, FIPA Standard, http://www.fipa.org/repository/aclspecs.html
 W3C - Web Services Activity http://www.w3.org/2002/ws/
 F. V. Jensen, An Introduction to Bayesian Networks, Springer Verlag, New York, 1996.
 J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA, 1988.
 J. Martin and K. VanLehn, "Student assessment using bayesian nets", International Journal of Human-Computer Studies, vol. 42, pp. 575-591, 1995.
 Cristina Conati, Abigail Gertner and Kurt Van Lehn, "Using Bayesian Networks to Manage Uncertainty in Student Modelling", User Modelling and User-Adapted Interaction, vol. 12, pp. 371-417, Kluwer Academic Publishers, Printed in the Netherlands, 2002.
 C. Conati, A. S. Gertner, K. Van Lehn, and M. J. Druzdel, "On-line student modelling for coached problem solving using Bayesian networks", in Proceedings of the Sixth International Conference on User Modelling, A. Jameson, C. Paris and C. Tasso, Eds, Vienna, New York, Springer, 1997, pp. 231-242.
 R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, Wiley- Interscience, 2nd edition, October 2000.
 JavaBayes, Bayesian Networks in Java, http://www.cs.cmu.edu/~javabayes/
 Eclipse Web Tools Platform (WTP), http://www.eclipse.org/webtools/
 Google API, http://www.google.com/apis/index.html
 J. H. Holland (1975), Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press (second edition: MIT Press, 1992).
 D. E. Goldberg (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, Mass.
 Z. Michalewicz (1999), Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin.
 M. Mitchell (1996), An Introduction to Genetic Algorithms, The MIT Press, Cambridge, Massachusetts, London, England.
 M. D. Vose (1998), The Simple Genetic Algorithm: Foundations and Theory, MIT Press.
 L. D. Whitley (1993), Foundations of Genetic Algorithms 2, Morgan Kaufmann.
 L. D. Whitley and M. D. Vose (1995), Foundations of Genetic Algorithms 3, Morgan Kaufmann.
 D. B. Fogel (1995), Evolutionary Computation: Toward a New Philosophy of Machine Learning, IEEE Press.
 Z. Michalewicz, D. B. Fogel (2000), How to Solve It: Modern Heuristics, Springer-Verlag, Berlin, Heidelberg.
 T. Back, D.B. Fogel, and Z. Michalewicz (1997), Handbook of Evolutionary Computation, Bristol, UK: Institute of Physics, and New York, NY: Oxford University Press.
 M. Gen, R. Cheng (1997), Genetic Algorithms and Engineering Design, John Wiley & Sons, Ltd.
 Xiang Wu, Bayan S. Sharif and Oliver R. Hinton, ÔÇÿAn Improved Resource Allocation Scheme for Plane Cover Multiple Access Using Genetic Algorithm-, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 1, February 2005.
 J. D. Schaffer, D. Whitley, and L. J. Eshelman, ÔÇÿCombinations of Genetic Algorithms and Neural Networks: A Survey of the State of the Art-, International Workshop on Combinations of Genetic Algorithms and Neural Networks, Baltimore, Maryland, June 6, 1992, pp. 1-37.
 M. Annunziato, M. Lucchetti, S. Pizzuti: ÔÇÿAdaptive Systems and Evolutionary Neural Networks: a Survey-, EUNITE2002, Albufeira, Portugal, Sept. 2002.
 E. Georgopoulos , S. Likothanassis and A. Adamopoulos, -Evolving Artificial Neural Networks using Genetic Algorithms-, International Conference on Artificial Neural Networks and Intelligent Systems (NNW 2000), Prague, Chez Republic, July 9-12, 2000.
 O. Cordon, H. Herrera, and M. Lozano, ÔÇÿA classified review on the combination fuzzy logic-genetic algorithms bibliography-, Tech. Report 95129, URL:http://decsai.ugr.s/herrera/flga.html, Department of Computer Science and AI, Universidad de Granada, Granada, Spain, 1995.
 GAlib - A C++ Library of Genetic Algorithm Components, Matthew Wall, Massachusetts Institute of Technology (MIT). Available: http://lancet.mit.edu/ga/.