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Evaluating the personalised and adaptable learning provided by e-learning platforms

Evaluating the personalised and adaptable learning provided by e-learning platforms

Peter, S.E., Bacon, E. and Dastbaz, M. (2009) Evaluating the personalised and adaptable learning provided by e-learning platforms. In: Bastiaens, Theo, Dron, Jon and Xin, Cindy, (eds.) Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2009. Association for the Advancement of Computing in Education (AACE), Chesapeake, VA, USA, pp. 3089-3096. ISBN 1-880094-76-2

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Abstract

Many common e-learning platforms (for example Moodle) offer good course and learner management tools but to what extent do they offer a “personalised” learning experience for the learner? This research evaluates current common e-learning platforms to assess how much personalization and adaptable learning they offer, i.e. to what extent do they adapt the learning content or course structure to the learner’s specific needs. This paper describes current e-learning platforms evaluation research and then proposes evaluation criteria and the research findings are then described in detail. An overview of the e-learning platform iLearn (Peter, Bacon and Dastbaz 2008) is given and it describes
how it uses the VARK (Fleming 1995) learning style and semantic web technologies to enhance the platform’s personalisation and adaptability for the learner by providing the learner with a bespoke learning package based on their specific requirements.

Item Type: Book Section
Additional Information: [1] Paper presented at E-Learn 2009: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, held 26-30 October 2009, Vancouver, BC Canada.
Uncontrolled Keywords: evaluation, personalised, adaptable learning, e-learning platforms
Subjects: L Education > LB Theory and practice of education
Pre-2014 Departments: School of Computing & Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:17
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/6583

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