A Web Based Intelligent Tutoring System
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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 students requirements and the students 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 [email protected]
INTRODUCTION
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 systems tended towards total constraint. He argues that the perfect tutoring system should be able to “slide” between these two extremes according to the student’s needs and state of knowledge, appearing as a traditional ITS to a novice student or a discovery learning, hypermedia system to an advanced student. Further research has shown that learning is improved when a student is allowed to follow pathways of their own choice, at their own pace and is able to monitor progress by instant feedback questions (Kibby, Mayes 1990).
A further concern with computer based learning systems is that they are generally applicable only to the domain for which they were specifically produced (Bergeron 1991). A reason for this is that most are of the constrained type, which lend themselves less readily to their implementation in a number of domains. The outcome is that every time a tutoring package is required for a new domain a complete new system must be produced. This is a major reason why intelligent tutoring systems are still predominantly in the domain of academic research (Elsom-Cook 1989, Kinshuk and Patel 1997).
This research therefore highlights the need for both a generic intelligent tutoring systems to provide teachers with a cheap and effective teaching medium and a method for providing a large corpus of students with an adaptable environment. The system is based on a semantically structured hypermedia domain with a novel student model. The World Wide Web may be used to allow the systems to be used with a large corpus of students.
TOWARDS GENERIC INTELLIGENT TUTORING
The above discussion suggests the need for a hybrid system that can both constrain the student to ensure that the basics are learned, and then allow them to explore the domain in a less constrained manner once they have achieved a sufficient level of experience. There is also a need for more generic systems, enabling the advantages of computer aided learning to be employed in many domains. This research is concerned with tackling these issues.
To address the problems outlined the research is based on a hypermedia architecture of nodes and links with an intelligent module to aid navigation through the nodes and to model the student’s current level of experience in the domain. To a novice student the system would offer a limited number of links from the total set of links available from that node, based upon information relating to their previous movements through the hypermedia. As the student’s knowledge increases then control is increasingly relinquished by the system, until the student is in total control of the system. Thus, the novice student is freed from much of the complexity