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Adaptable, personalised e-learning incorporating learning styles

Adaptable, personalised e-learning incorporating learning styles

Peter, Sophie E., Bacon, Elizabeth and Dastbaz, Mohammad (2010) Adaptable, personalised e-learning incorporating learning styles. Campus-Wide Information Systems, 27 (2). pp. 91-100. ISSN 1065-0741 (doi:https://doi.org/10.1108/10650741011033062)

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Abstract

Purpose – The purpose of this paper is to discuss how learning styles and theories are currently used within personalised adaptable e-learning adaptive systems. This paper then aims to describe the e-learning platform iLearn and how this platform is designed to incorporate learning styles as part of the personalisation offered by the system.
Design/methodology/approach – The paper discusses how learning styles and theories are currently being used within the area of adaptive e-learning and describes current research within this area. This paper then gives an overview of the iLearn project and describes how iLearn is using the VARK learning style to enhance the platform’s personalisation and adaptability for the learner. This
research also describes the system’s design and how the learning style is incorporated into the system design and semantic framework within the learner’s profile. Findings – The findings describe how the final implemented iLearn platform intends to address the issues found with the limited personalisation within common learning management systems and intends to provide the learner with a personalised learning experience. Originality/value – Adaptability and personalisation are large research areas, however, many limitations have been found during the current research. This research project, therefore adds value to this by proposing a system which will address the current personalisation limitations.

Item Type: Article
Uncontrolled Keywords: E-learning, personal needs, learning styles
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > eCentre
Related URLs:
Last Modified: 14 Oct 2016 09:09
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/3582

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