This paper describes an Intelligent Tutoring System that provides adaptable facilities to students over the World Wide Web. The system is able to adapt to a large corpus of students, in terms of allowing commonly used paths to emerge, and adapts to individual students by employing a novel student model. The lack of intelligent tutoring systems outside of academic research has largely been attributed to the fact that they are domain specific. Whilst very powerful for the tutoring of a specific subject, the inability of a knowledge-based approach to artificial intelligence to generalise that has rendered it less useful for generic systems. Neural networks, or connectionist models, are the antithesis of knowledge-based approaches in that they are extremely adept at generalising which gives them the ability to work with very noisy data. The research project described in the paper employs both knowledge-based representations and neural networks to model students using non-domain specific parameters, such as browse strategies and ability to answer questions. The domain is structured in a hypermedia network using semantic linking that enables the system to automatically produce and weight new links. The weighting system is tailored according to a student's requirements and the student's ability level and is continuously updated. This novel paraigm is of great potential in a tele-education environment, since the system s generic and is therefore useful to a multitude of authors/domains and the system is able t adapt to a large number of students, such as may be found on the World Wide Web. Keywords: Hypermedia, neural networks, domain independence, browsing strategy, student modelling 1 Faculty of Information and Engineering Systems, Leeds Metropolitan University, Beckett Park, Headingley, LS6 3QS, Tel 0113 832600, Fax 0113 833182, email firstname.lastname@example.org
Hypermedia systems when used for learning generally offer no constraint on the user. Indeed this according to some is the beauty of such systems (Jonassen, Grabinger 1991). However as has often been pointed out, hypermedia does suffer from some problems, especially when used for education, most notably "getting lost in (hyper) space" (Conklin 1987), where a user becomes so bemused by the wealth of choice on offer that they become lost in a maze of information. Research has suggested that this is caused by "cognitive overload"; i.e. the brain can only cope with a limited number of tasks (Kibby, Mayes 1990). In the early stages of using an unfamiliar system, much load occurs in the use of the unfamiliar features. It is therefore perhaps better to reduce the plethora of complexities found in a hypermedia system until the user has reached a level such that the complexities will not induce so much load (Dillon 1990). Traditional computer based learning/tutoring systems (CBL/CBT) are generally the antithesis of hypermedia learning systems, in that they constrain the student and force them to learn a predetermined method (Ridgeway 1989). Intelligent tutoring systems (ITS) use a model of the student’s knowledge so that they are presented with new information only when they require it, to reinforce a point or to progress in the learning and to identify misconceptions and mal-rules (Sleeman, Brown 1982). Such systems have been criticised for constraining the way students solve a given problem (Ridgeway 1989). In most complex problem domains, there can be many methods to achieve a correct solution and some learners may find one particular method suits their way of thinking better than others. It has been argued that students should be able to experiment with their own ideas and find the method that suites them individually (Ridgeway 1989). Elsom-Cook (1989) reviews some computer based training packages and grades them between two extremes, total constraint, (such as a typical intelligent tutoring system) and totally unconstrained (like a typical hypermedia system). Most...
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