A Fuzzy Expert System Architecture for Intelligent Tutoring Systems: A Cognitive Mapping Approach

Mohammad Hossein Fazel Zarandi, Mahdi Khademian, Behrouz Minaei-Bidgoli, Ismail Burhan Türkşen

Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran.
Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;.
Department of Industrial Engineering, TOBB Economy and Technology University, Ankara, Turkey..
Departmentof Industrial Engineering, Amirkabir University of Technology, Tehran, Iran;.

DOI: 10.4236/jilsa.2012.41003                

Abstract

An Intelligent Tutoring System (ITS) is a computer based instruction tool that attempts to provide individualized instructions based on learner’s educational status. Advances in development of these systems have rose and fell since their emergence. Perhaps the main reason for this is the absence of appropriate framework for ITS development. This paper proposes a framework for designing two main parts of ITSs. Besides development framework, the second main reason for lack of significant advances in ITS development is its development cost. In general, this cost for instructional material is quite high and it becomes more in ITS development. The proposed method can significantly reduce the development cost. The cost reduction mainly is because of characteristics of applied mapping techniques. These maps are human readable and easily understandable by people who are not aware of knowledge representation techniques. The proposed framework is implemented for a graduate course at a technical university in Asia. This experiment provides an individualized instruction which is the main designing purpose of the ITSs.

Keywords

Concept Mapping; Expert and Student Models; Fuzzy Cognitive Maps; Intelligent Tutoring Systems; Expert Systems

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. L. Gordon, “Creating Knowledge Maps by Exploiting Dependent Relationships,” Knowledge Based Systems, Vol. 13, No. 2-3, 2000, pp. 71-79. doi:10.1016/S0950-7051(00)00048-4
[2]	M. Moundridou and M. Virvou, “Analysis and Design of a Web-Based Authoring Tool Generating Intelligent Tutoring Systems,” Computers & Education, Vol. 40, No. 2, 2003, pp. 157-181. doi:10.1016/S0360-1315(02)00119-7
[3]	T. Murray, “Authoring Intelligent Tutoring Systems: An Analysis of the State of the Art,” International Journal of Artificial Intelligence in Education, Vol. 10, No. 1, 1999, pp. 98-129.
[4]	B. P. Woolf and P. A. Cunningham, “Multiple Knowledge Sources in Intelligent Teaching Systems,” IEEE Expert, Vol. 2, No. 2, 1987, pp. 41-54. doi:10.1109/MEX.1987.4307063
[5]	S.-Y. Jung and K. VanLehn, “Developing an Intelligent Tutoring System Using Natural Language for Knowledge Representation,” Intelligent Tutoring Systems, Vol. 6095, 2010, pp. 355-358. doi:10.1007/978-3-642-13437-1_69
[6]	V. Aleven, B. M. McLaren, J. Sewall and K. R. Koedinger, “A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors,” International Journal of Artificial Intelligence in Education, Vol. 19, No. 2, 2009, pp. 105-154.
[7]	V. Aleven and J. Sewall, “Hands-On Introduction to Creating Intelligent Tutoring Systems without Programming Using the Cognitive Tutor Authoring Tools (CTAT),” Proceedings of the 9th International Conference of the Learning Sciences, Vol. 2, Chicago, 29 June-2 July 2010.
[8]	S. Stankov, M. Rosi?, B. ?itko, and A. Grubi?i?, “TExSys Model for Building Intelligent Tutoring Systems,” Computers & Education, Vol. 51, No. 3, 2008, pp. 10171217.
[9]	W. R. Murray, “Statistical Relational Learning in Student Modeling for Intelligent Tutoring Systems,” Artificial Intelligence in Education, Vol. 6738, 2011.
[10]	P. Fournier-Viger, R. Nkambou and E. M. Nguifo, “A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems,” Advances in Artificial Intelligence, Vol. 5317, 2008, pp. 765-778.
[11]	B. P. Butz, M. Duarte and S. M. Miller, “An Intelligent Tutoring System for Circuit Analysis,” IEEE Transactions on Education, Vol. 49, No. 2, 2006, pp. 216-223.
[12]	J. D. Novak and A. J. Ca?as, “The Theory Underlying Concept Maps and How to Construct Them,” 2006. http://www.cmap.ihmc.us
[13]	J. Novak and D. Musonda, “A Twelve-Year Longitudinal Study of Science Concept Learning,” American Educational Research Journal, Vol. 28, No. 1, 1991, pp. 117153.
[14]	B. Kosko, “Fuzzy Cognitive Maps,” International Journal Man-Machine Studies, Vol. 24, No. 1, 1986, pp. 6575. doi:10.1016/S0020-7373(86)80040-2
[15]	A. Konar, “Computational Intelligence. Principles Techniques and Application,” Springer, Berlin, 2006.
[16]	D. Brubaker. (1996, Apr.) EDN Access: For Design, By Design. Uhttp://www.edn.com/archives/1996/042596/09column.htmU
[17]	J. P. Carvalho and J. A. B. Tomé, “Rule Based Fuzzy Cognitive Maps—Qualitative Systems Dynamics,” 19th International Conference of the North American, Atlanta, 13-15 July 2000, pp. 407-411.
[18]	R. A. Brooks, “A Robust Layered Control System for a Mobile Robot,” IEEE Journal of Robotics and Automation, Vol. 2, No. 1, 1986, pp. 14-23. doi:10.1109/JRA.1986.1087032