ATR trainer: Intelligent CBT for the Rest of Us?
The last decade has seen a great deal of research on Intelligent Tutoring Systems (ITS), with very few (if any) commercially viable solutions. There are those who have been tempted to apply the more mature expert systems technology to learning, having noticed that expert system users tend automatically to learn from such systems.
The problem with relying on straightforward expert systems technology for learning concerns the blindness of such systems to student progress. Although the process of reasoning towards some solution is rendered accessible in expert systems, essentially, they do not need to care whether users understand this process. This means that to use them effectively, learners need to have learned how to learn in order to create and support their own learning objectives.
ATR trainer is a rule-based expert system shell with student modelling and tutorial facilities. The system runs in a 640K MS-DOS environment and is commercially available. Significantly, its logic constitutes an example description language that enables the system to generate novel, learner-centred examples and test material on the fly. The system is particularly interesting for its ability to function in both a tutorial and an expert system mode, using the same knowledge structure.
This paper examines the evolution of ATR traigner and outlines future developments in the light of commercial experience with Version 1.
KeywordsExpert System Rule Base Knowledge Structure Teaching Material Description Language
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