ALT 1992: Algorithmic Learning Theory pp 1-12 | Cite as
Discovery learning in intelligent tutoring systems
Invited Papers
First Online:
Abstract
A brief history of Intelligent Tutoring Systems and their necessary educational functions which have already been realized and not yet been realized are presented separately, then problems to be solved within the framework of ITS and problems that transcend the framework of ITS are discussed. Lastly, it is indicated that the problems will be solved by an amalgamation of an open-end system like a micro world and a discovery system with direct manipulation into ITS and that the central problem to realize the amalgamation is a discovery learning by a machine itself.
Keywords
Intelligent Tutor System Student Model Discovery Learning Target Rule Error Origin
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- [1]Oettinger, A.G.: Run. Computer Run: The Mythology of educational Innovation, Harvard University Press, Cambridge, MA., P.180, (1969).Google Scholar
- [2]Carbonell, J.R.: “AI in CAI”: An artificial intelligence approach to Computer-Assisted Instruction, IEEE trans. Man-Machine Systems 11(4), December, 190/202, (1970).Google Scholar
- [3]Kearsley, G.: Intelligent CAI, in Encyclopedia of Artificial Intelligence, John Wiley & Sons, Inc., (1987).Google Scholar
- [4]Sleeman, D. & Brown, J. S.: Intelligent Tutoring Systems, Academic Press, (1982).Google Scholar
- [5]Wenger, E.: Artificial Intelligence and Tutoring Systems, Morgan Kaufmann Publishers, Inc., (1987).Google Scholar
- [6]Self, J.: Bypassing the Intractable Problem of Student Modeling, Proc. of ITS'88, 18/24, (1988).Google Scholar
- [7]Invited talks in Proc. of the 4th international conference on AI and Education, IOS, (1989).Google Scholar
- [8]Matz, M.: Toward a Process Model for High School Algebra, in Intelligent Tutoring Systems (D.H. Sleeman, et al. eds.), Academic Press, London, 25/50, (1982).Google Scholar
- [9]Brown, J.S. et. al.: Repair Theory: A Generative Theory of Bugs in Procedural Skills, Cognitive Science, Vol.4, No.4, 379/426, (1980).Google Scholar
- [10]Takeuchi, A. & Otsuki, S.: Formation of Learner Model by Perturbation method and teaching knowledge, Vol.28, No.1, 54/63, (1987).Google Scholar
- [11]E. Shapiro: Algorithmic Program Debugging, MIT Press, (1982).Google Scholar
- [12]J. Doyle, A Truth Maintenance System, Artificial Intelligence, Vol.12, 231/272, (1979).Google Scholar
- [13]Mizoguti, R. & Ikeda, M.: A Generic Framework for ITS And Its Evaluation. International Conference on ARCE, Advanced Research on Computers in Education, (eds. Lewis, R. & Otsuki, S.) Elsvier Science Publishers, pp.63–72, (1991).Google Scholar
- [14]J. Self: Supporting The Disembedding of Learning, Proc. of East-West Conference on Emerging Computer Technologies in Education, April, (1992).Google Scholar
- [15]W. J. Clancey: Guidon-Manage revisited: a socio-technical approach, in Intelligent Tutoring Systems, Lecture Notes in Computer Science 608, Springer-Verlag, (1992).Google Scholar
- [16]Takeuchi, A., Shingae, T. & Otsuki, S.: Intelligent CAI Supporting both Inductive Learning and Deductive Learning, Japanese Society for AI, Sig-HICG'92.1,(1992).Google Scholar
- [17]Shingae, T., Takeuchi, A. & Otsuki, S.: A Study on Inner Experiments and Formulation in a Micro World, Japanese Society for AI, Sig-IES'92.2, (1992).Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 1993