Adaptive Educational Hypermedia Systems (AEHS) have been proposed as the underlying facilitator for personalized web-based learning with the general aim of personalizing learning experiences for a given learner. Adaptive learning objects selection and sequencing is recognized as challenging research issues in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning objects in AEHS, the definition of adaptation behaviour, referred to as Adaptation Model, is required. The main drawback of the direct definition of adaptation rules is that there can be cases during the run-time execution of AEHS where no adaptation decision can be made due to insufficiency and/or inconsistency of the defined adaptation rule sets.
The main hypothesis of this thesis is that it is feasible to construct a semi-automated, decision-based approach, which generates a continuous decision function that estimates the desired AEHS response, aiming to overcome the problems of insufficiency and inconsistency in the AM of an AEHS.