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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/1218

Title: Personalised E-Learning Through Learning Style Aware Adaptive Systems
Author: Canavan, John
Advisor: Wade, Vincent
Keywords: Computer Science
Issue Date: Sep-2004
Abstract: There are many factors than can influence the extent of learning. These would include factors such as the student's learning style and motivation for learning. In a learning environment, each individual student will have different requirements and characteristics. An important role of the educator is to recognise that their pedagogy and educational material must cater for the individual learner's characteristics and requirements. This is true for both the classroom approach and for e learning. There is a need to move away from the "one size fits all" paradigm and to offer learner's a personalised learning experience. This research investigates how personalised courses can be delivered to the learner in an adaptive environment. More specifically, it examines how learning style information can be integrated into an Adaptive Hypermedia System to offer increased personalisation.
Learning style can be defined as the attitudes and behaviours that determine an individual's preferred way of learning. Many different learning style models exist today. The learning style model chosen for the integration into an Adaptive Hypermedia System during this research is the Honey and Mumford Learning Style Model, an information processing model type. This report presents a review of learning styles along with a state of the art pertaining to three adaptive systems that offer some degree of pedagogical support for learning style. An extension of the Adaptive Personalised e Learning Service (APeLS) framework is detailed to cater for the Honey and Mumford Learning Style Model. A prototype adaptive course was developed in conjunction with this report. A group of users evaluated this adaptive course offering some important feedback on the degree of personalisation perceived.
URI: http://hdl.handle.net/2262/1218
Appears in Collections:Computer Science (Theses and Dissertations)
Computer Science Technical Reports

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