Integrating Computational Intelligence Techniques and Assessment Agents in ELearning Environments
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.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] F. Bellifemine, G Caire, A. Poggi and G. Rimassa, "JADE A White
Paper", Telecom Italia EXP Magazine, Volume 3, Number 3 September
2003
[8] Foundation of Intelligent Physical Agents (FIPA), http://www.fipa.org/
[9] FIPA ACL Message Structure Specification, FIPA Standard,
http://www.fipa.org/repository/aclspecs.html
[10] W3C - Web Services Activity http://www.w3.org/2002/ws/
[11] F. V. Jensen, An Introduction to Bayesian Networks, Springer Verlag,
New York, 1996.
[12] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference, Morgan Kaufmann, San Mateo, CA, 1988.
[13] J. Martin and K. VanLehn, "Student assessment using bayesian nets",
International Journal of Human-Computer Studies, vol. 42, pp. 575-591,
1995.
[14] 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.
[15] 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.
[16] R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, Wiley-
Interscience, 2nd edition, October 2000.
[17] JavaBayes, Bayesian Networks in Java,
http://www.cs.cmu.edu/~javabayes/
[18] Eclipse Web Tools Platform (WTP), http://www.eclipse.org/webtools/
[19] Google API, http://www.google.com/apis/index.html
[20] 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).
[21] D. E. Goldberg (1989), Genetic Algorithms in Search, Optimization and
Machine Learning, Addison-Wesley, Reading, Mass.
[22] Z. Michalewicz (1999), Genetic Algorithms + Data Structures =
Evolution Programs, Springer-Verlag, Berlin.
[23] M. Mitchell (1996), An Introduction to Genetic Algorithms, The MIT
Press, Cambridge, Massachusetts, London, England.
[24] M. D. Vose (1998), The Simple Genetic Algorithm: Foundations and
Theory, MIT Press.
[25] L. D. Whitley (1993), Foundations of Genetic Algorithms 2, Morgan
Kaufmann.
[26] L. D. Whitley and M. D. Vose (1995), Foundations of Genetic
Algorithms 3, Morgan Kaufmann.
[27] D. B. Fogel (1995), Evolutionary Computation: Toward a New
Philosophy of Machine Learning, IEEE Press.
[28] Z. Michalewicz, D. B. Fogel (2000), How to Solve It: Modern
Heuristics, Springer-Verlag, Berlin, Heidelberg.
[29] 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.
[30] M. Gen, R. Cheng (1997), Genetic Algorithms and Engineering Design,
John Wiley & Sons, Ltd.
[31] 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.
[32] 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.
[33] M. Annunziato, M. Lucchetti, S. Pizzuti: ÔÇÿAdaptive Systems and
Evolutionary Neural Networks: a Survey-, EUNITE2002, Albufeira,
Portugal, Sept. 2002.
[34] 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.
[35] 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.
[36] GAlib - A C++ Library of Genetic Algorithm Components, Matthew
Wall, Massachusetts Institute of Technology (MIT). Available:
http://lancet.mit.edu/ga/.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] F. Bellifemine, G Caire, A. Poggi and G. Rimassa, "JADE A White
Paper", Telecom Italia EXP Magazine, Volume 3, Number 3 September
2003
[8] Foundation of Intelligent Physical Agents (FIPA), http://www.fipa.org/
[9] FIPA ACL Message Structure Specification, FIPA Standard,
http://www.fipa.org/repository/aclspecs.html
[10] W3C - Web Services Activity http://www.w3.org/2002/ws/
[11] F. V. Jensen, An Introduction to Bayesian Networks, Springer Verlag,
New York, 1996.
[12] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference, Morgan Kaufmann, San Mateo, CA, 1988.
[13] J. Martin and K. VanLehn, "Student assessment using bayesian nets",
International Journal of Human-Computer Studies, vol. 42, pp. 575-591,
1995.
[14] 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.
[15] 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.
[16] R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, Wiley-
Interscience, 2nd edition, October 2000.
[17] JavaBayes, Bayesian Networks in Java,
http://www.cs.cmu.edu/~javabayes/
[18] Eclipse Web Tools Platform (WTP), http://www.eclipse.org/webtools/
[19] Google API, http://www.google.com/apis/index.html
[20] 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).
[21] D. E. Goldberg (1989), Genetic Algorithms in Search, Optimization and
Machine Learning, Addison-Wesley, Reading, Mass.
[22] Z. Michalewicz (1999), Genetic Algorithms + Data Structures =
Evolution Programs, Springer-Verlag, Berlin.
[23] M. Mitchell (1996), An Introduction to Genetic Algorithms, The MIT
Press, Cambridge, Massachusetts, London, England.
[24] M. D. Vose (1998), The Simple Genetic Algorithm: Foundations and
Theory, MIT Press.
[25] L. D. Whitley (1993), Foundations of Genetic Algorithms 2, Morgan
Kaufmann.
[26] L. D. Whitley and M. D. Vose (1995), Foundations of Genetic
Algorithms 3, Morgan Kaufmann.
[27] D. B. Fogel (1995), Evolutionary Computation: Toward a New
Philosophy of Machine Learning, IEEE Press.
[28] Z. Michalewicz, D. B. Fogel (2000), How to Solve It: Modern
Heuristics, Springer-Verlag, Berlin, Heidelberg.
[29] 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.
[30] M. Gen, R. Cheng (1997), Genetic Algorithms and Engineering Design,
John Wiley & Sons, Ltd.
[31] 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.
[32] 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.
[33] M. Annunziato, M. Lucchetti, S. Pizzuti: ÔÇÿAdaptive Systems and
Evolutionary Neural Networks: a Survey-, EUNITE2002, Albufeira,
Portugal, Sept. 2002.
[34] 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.
[35] 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.
[36] GAlib - A C++ Library of Genetic Algorithm Components, Matthew
Wall, Massachusetts Institute of Technology (MIT). Available:
http://lancet.mit.edu/ga/.
@article{"International Journal of Information, Control and Computer Sciences:59336", author = "Konstantinos C. Giotopoulos and Christos E. Alexakos and Grigorios N. Beligiannis and Spiridon D.Likothanassis", title = "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.", keywords = "Bayesian Networks, Computational Intelligencetechniques, E-learning legacy systems, Service Oriented Integration,Intelligent Agents, Genetic Algorithms.", volume = "1", number = "12", pages = "3959-10", }
