Using of Intelligence Tutoring Systems For Knowledge Representation in Learning& Teaching Process
DOI:
https://doi.org/10.31642/JoKMC/2018/010501Keywords:
representation of knowledge, TEx-Sys knowledge, system – Tutor-ExpertAbstract
Intelligent tutoring systems (ITS) are a new generation of computer systems for support and improvement of learning and teaching. The usual definition of an ITS characterizes it as a system based on some kind of knowledge which includes domain, teachers' and students' knowledge. In the research , we elaborate on the representation of knowledge in an intelligent authoring shell – which is an ITS generator system – Tutor-Expert System. Within TEx-Sys knowledge is represented through semantic networks with frames and production rules. Nodes are used for representation of domain knowledge objects, while links show relations among them. Besides, TEx-Sys supports properties and frames, as well as property inheritance and frames containing a conclusion-making mechanism. In this research , we try to explain of practice ITS application in learning and teaching process. Because, Intelligent tutoring systems have been shown to be highly effective at increasing students' performance and motivation. For example, students using Software systems, an ITS for computers , performed equally well as students taking a theoretical & practice courses in computers, but required half as much time covering the material.
Downloads
References
F. Chabris 1987. Artificial Intelligence &
Turbo PASCAL, Multiscience Press, Inc.
S. Stankov, V. Glavinic, M. Rosic " On
Knowledge Representation in an
Intelligent Tutoring System, project ,
Ministry for Science and Technology of
the Republic of Croatia,2003.
M. Urban-Lurain .2000:" Intelligent
Tutoring Systems": An Historic Review in
the Context of the Development
ofArtificial Intelligence and Educational
Psychology,
http://www.cse.msu.edu/~urban/ITS.htm.
Eliot, C. and Woolf, B. 1995. "An
Adaptive Student Centered Curriculum for
an Intelligent Training System". User
Modeling and User-Adapted Interaction, 5,
, pp. 67-86.
Shute, V., R. Glaser, and K. Raghaven.
"Inference and Discovery in an
Exploratory Laboratory". Learning and
Individual Differences, Ackerman, P., R.
Sterberg, and R. Glaser, eds., pp. 279-326.
Haugsjaa, E. and Woolf, B. 1996. "3D
Visualization Tools in a Design for
Manufacturing Tutor". In Proceedings of
Educational Multimedia and Hypermedia,
Boston, Mass.
Shute, V. 1995. Smart: Student Modeling
Report, AI Lab Memo 406, MIT.
Anderson, J. 1993." Rules of the Mind".
New Jersey, Lawrence Erlbaum AssociatesApproach for Responsive Tutoring.
User Modeling and User-Adapted
Interaction, 5, 1, pp. 1-44.
Bloom, B. (ed.) 1956. "Taxonomy
of Educational Objectives". New York,
NY, Mackay Publishing.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 Abdual Satar Jabbar Nasir AL-Qusi
This work is licensed under a Creative Commons Attribution 4.0 International License.
which allows users to copy, create extracts, abstracts, and new works from the Article, alter and revise the Article, and make commercial use of the Article (including reuse and/or resale of the Article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made and the licensor is not represented as endorsing the use made of the work.