Designing Intelligent Tutoring Systems: A Personalization Strategy using Case-Based Reasoning and Multi-Agent Systems
Abstract Intelligent Tutoring Systems (ITSs) are educational systems that use artificial intelligence techniques for representing the knowledge. ITSs design is often criticized for being a complex and challenging process. In this article, we propose a framework for the ITSs design using Case Based Reasoning (CBR) and Multiagent systems (MAS). The major advantage of using CBR is to allow the intelligent system to propose smart and quick solutions to problems, even in complex domains, avoiding the time necessary to derive those solutions from scratch. The use of intelligent agents and MAS architectures supports the retrieval of similar students models and the adaptation of teaching strategies according to the student profile. We describe deeply how the combination of both technologies helps to simplify the design of new ITSs and personalize the e-learning process for each student
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A. Aamodt and E. Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1):39–59, 1994.
S. Cern. Intelligent tutoring systems. Springer Verlag Pub., 2002.
H. Chen and Z. Wu. On case-based knowledge sharing in semantic web. 15th International Conference on Tools with Artificial Intelligence, pages 200– 206, 2003.
V. Devedzic, L. Jerinic, and D. Radovic. The get-bits model of intelligent tutoring systems. Journal of Interactive Learning Research, 11(3):411–434, 2000.
R. Ellaway. Modeling virtual patients and virtual cases. Medbiquitous Annual Conference, 2005. 10
J. Elorriaga and I. Fernandez-Castro. Using case-based reasoning in instructional planning. towards a hybrid self-improving instructional planner. International Journal of Artificial Intelligence in Education, pages 416–449, 2000.
F. Fernandez-Riverola and J. M. Corchado. Employing tsk fuzzy models to automate the revision stage of a cbr system. 10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA, pages 302–311, 2004.
L. Giraffa and et. al. Multi-ecological: an learning environment using multi-agent architecture. Multia-Agent System: Theory and Application Proceedings, 1997.
C. Gonzalez, J. Burguillo, and M. Llamas. A qualitative comparison of techniques for student modeling in intelligent tutoring systems. ASEE/FIE Frontiers in Education Conference, 2006.
S.-G. Han, S.-G. Lee, and G.-S. Jo. Case-based tutoring system for procedural problem solving on the www. Expert Systems with applications, pages 573–582, 2005.
M. Kharrat, N. Reyhani, and K. Badie. A case-based reasoning approach to intelligent tutoring system by considering learner style model. Proceedings of the 2003 Ssystems and Information Engineering Design Symposium, 2003.
R. Koper, B. Olivier, and T. Anderson. Ims learning design best practice and implementation guide. 2003.
O. Kwon and N. Sadeh. Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping. Decision Support Systems, 37(2):199–213, 2004.
S. Li and Q. Yang. Active cbr: An agent system that integrates casebased reasoning and active databases. Knowledge and Information Systems, 3(2):225–251, 2004.
R. Lopez de Mantaras. A distance-based attribute selection measure for decision tree induction. Machine Learning, pages 81–92, 2005.
L. McGinty and B. Smyth. Collaborative case-based reasoning: Applications in personalised route planning. 4th International Conference on Case Based Reasoning, ICCBR. Lecture Notes in Computer Science, 2080, 2001. 362.
A. Montazemi and K. Gupta. An adaptive agent for case description in diagnostic cbr systems. Computers in Industry, 29(3):209–224, 2004.
J. Pavn and J. Gmez-Sanz. Agent oriented software engineering with ingenias. International Central and Eastern European Conference on Multi-Agent Systems, 2691:394–403, 2003.
O. Rodriguez, A. Martnez, A. Vizcaino, J. Favela, and M. Piattini. Developing a multi-agent knowledge management system with ingenias. 2005.
S. Sibte and A. Raza. A case base reasoning framework to author personalized health maintenance information. In 15th Symposium on Computer Based Medical Systems, 2002.
E. Sierra, R. Martnez, Z. Cataldi, and et.al. Towards a methodology for the design of intelligent tutoring systems. Research in Computing Science Journal, pages 181–189, 2006.
J. Tomas, P. Calero, and B. Diaz. Jcolibri: An object-oriented framework for building cbr systems. Lecture Notes in Artificial Intelligence, pages 32–46, 2004.
M. Urretavizcaya. Sistemas inteligentes en el ambito de la educacion. Revista Iberoamericana de Inteligencia Artificial, ISSN 1137-3601(12):5–12, 2001.
K. VanLehn. Student Modelling. M. Polson. Foundations of Intelligent Tutoring Systems, 1988.
M. Wooldridge. Introduction to MultiAgent Systems. John Wiley and Sons, 2002.
J. Zapata-Rivera and J. Greer. Smodel server: Student modelling in distributed multi-agent tutoring systems. International Conference on Artificial Intelligence in Education AIED 2001, 2001.
S. Cern. Intelligent tutoring systems. Springer Verlag Pub., 2002.
H. Chen and Z. Wu. On case-based knowledge sharing in semantic web. 15th International Conference on Tools with Artificial Intelligence, pages 200– 206, 2003.
V. Devedzic, L. Jerinic, and D. Radovic. The get-bits model of intelligent tutoring systems. Journal of Interactive Learning Research, 11(3):411–434, 2000.
R. Ellaway. Modeling virtual patients and virtual cases. Medbiquitous Annual Conference, 2005. 10
J. Elorriaga and I. Fernandez-Castro. Using case-based reasoning in instructional planning. towards a hybrid self-improving instructional planner. International Journal of Artificial Intelligence in Education, pages 416–449, 2000.
F. Fernandez-Riverola and J. M. Corchado. Employing tsk fuzzy models to automate the revision stage of a cbr system. 10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA, pages 302–311, 2004.
L. Giraffa and et. al. Multi-ecological: an learning environment using multi-agent architecture. Multia-Agent System: Theory and Application Proceedings, 1997.
C. Gonzalez, J. Burguillo, and M. Llamas. A qualitative comparison of techniques for student modeling in intelligent tutoring systems. ASEE/FIE Frontiers in Education Conference, 2006.
S.-G. Han, S.-G. Lee, and G.-S. Jo. Case-based tutoring system for procedural problem solving on the www. Expert Systems with applications, pages 573–582, 2005.
M. Kharrat, N. Reyhani, and K. Badie. A case-based reasoning approach to intelligent tutoring system by considering learner style model. Proceedings of the 2003 Ssystems and Information Engineering Design Symposium, 2003.
R. Koper, B. Olivier, and T. Anderson. Ims learning design best practice and implementation guide. 2003.
O. Kwon and N. Sadeh. Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping. Decision Support Systems, 37(2):199–213, 2004.
S. Li and Q. Yang. Active cbr: An agent system that integrates casebased reasoning and active databases. Knowledge and Information Systems, 3(2):225–251, 2004.
R. Lopez de Mantaras. A distance-based attribute selection measure for decision tree induction. Machine Learning, pages 81–92, 2005.
L. McGinty and B. Smyth. Collaborative case-based reasoning: Applications in personalised route planning. 4th International Conference on Case Based Reasoning, ICCBR. Lecture Notes in Computer Science, 2080, 2001. 362.
A. Montazemi and K. Gupta. An adaptive agent for case description in diagnostic cbr systems. Computers in Industry, 29(3):209–224, 2004.
J. Pavn and J. Gmez-Sanz. Agent oriented software engineering with ingenias. International Central and Eastern European Conference on Multi-Agent Systems, 2691:394–403, 2003.
O. Rodriguez, A. Martnez, A. Vizcaino, J. Favela, and M. Piattini. Developing a multi-agent knowledge management system with ingenias. 2005.
S. Sibte and A. Raza. A case base reasoning framework to author personalized health maintenance information. In 15th Symposium on Computer Based Medical Systems, 2002.
E. Sierra, R. Martnez, Z. Cataldi, and et.al. Towards a methodology for the design of intelligent tutoring systems. Research in Computing Science Journal, pages 181–189, 2006.
J. Tomas, P. Calero, and B. Diaz. Jcolibri: An object-oriented framework for building cbr systems. Lecture Notes in Artificial Intelligence, pages 32–46, 2004.
M. Urretavizcaya. Sistemas inteligentes en el ambito de la educacion. Revista Iberoamericana de Inteligencia Artificial, ISSN 1137-3601(12):5–12, 2001.
K. VanLehn. Student Modelling. M. Polson. Foundations of Intelligent Tutoring Systems, 1988.
M. Wooldridge. Introduction to MultiAgent Systems. John Wiley and Sons, 2002.
J. Zapata-Rivera and J. Greer. Smodel server: Student modelling in distributed multi-agent tutoring systems. International Conference on Artificial Intelligence in Education AIED 2001, 2001.
González, C., Burguillo, J. C., Llamas, M., & Laza, R. (2013). Designing Intelligent Tutoring Systems: A Personalization Strategy using Case-Based Reasoning and Multi-Agent Systems. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2(1), 41–54. https://doi.org/10.14201/ADCAIJ2013244154
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