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Paper (refereed) Student-Faculty

Recommended Citation: Liegle, J O and H G Woo. Developing Adaptive Intelligent Tutoring Systems: A General Framework and its Implementations. In The Proceedings of the Information Systems Education Conference 2000, v 17 (Philadelphia): §915.

Developing Adaptive Intelligent Tutoring Systems: A General Framework and its Implementations

Refereed

Jens O. Liegle    [a1] [a2]
Department of Computer Information Systems
Georgia State University    [u1] [u2]
Atlanta, Georgia, USA    [c1] [c2]

Han-Gyun Woo    [a1] [a2]
Department of Computer Information Systems
Georgia State University    [u1] [u2]
Atlanta, Georgia, USA    [c1] [c2]

Web-based training is increasingly gaining popularity both in industry and education. Although a number of studies, experiments, and developments have been conducted in this area, few evidence cases of success have been reported. One likely reason for the lack of success is that just placing lecture notes on the web does not train. This situation can be improved through the use of training software such as Intelligent Tutoring Systems (ITS). ITS incorporate built-in expert systems in order to monitor the performance of a learner and to personalize instruction on the basis of adaptation to learners' learning style, current knowledge level, and appropriate teaching strategies.

However, researchers and developers quickly find out that developing such systems is an enormous task, which is further complicated by the fact that one cannot simply borrow tools from other systems and incorporate them due to various levels of incompatibility at the programming and knowledge base level. To allow for more general ITS, which means that it can be used in other domains, it is required that ITS should be designed and implemented so as to support easy modification of lecture content, modification of decision rules in the expert system, and to support various methods to measure the performances of learning.

In this paper, we propose a general framework and data model for web-based adaptive ITS that allows knowledge to be stored in such a way that is not only independent of the knowledge domain, but also supports the storage of transfer knowledge relationships and prerequisite knowledge relationships. We expect that our unified approach could contribute to the establishment of cumulative research traditions in ITS studies.

Keywords: intelligent tutoring systems, cognitive style, learning style, system development, framework

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