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Analysing online teaching and learning systems using MEAD

Leitch, Shona and Warren, Matthew J. 2008, Analysing online teaching and learning systems using MEAD, Interdisciplinary journal of knowledge and learning objects, vol. 4, pp. 259-266.

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Title Analysing online teaching and learning systems using MEAD
Author(s) Leitch, Shona
Warren, Matthew J.
Journal name Interdisciplinary journal of knowledge and learning objects
Volume number 4
Start page 259
End page 266
Publisher Informing Science Institute
Place of publication Santa Rosa, Calif.
Publication date 2008
ISSN 1552-2210
1552-2237
1552-2229
Keyword(s) Soft Systems Methodology (SSM)
Method for Educational Analysis and Design (MEAD)
online learning
Summary The review of literature pertaining to systems analysis and design and the design of systems for online teaching and learning has identified some “gaps” and has shown the need for a more specialised and specific method for the design of such systems. This paper presents research that was conducted to collect information to assist in the filling of the gaps of the systems analysis and design knowledge within Australia and also presents a method for the development of online teaching and learning systems. Currently design is done in an ad-hoc fashion with little formal input from the student users; this research aims to rectify this. The paper puts forwards an educational design approach based upon Soft Systems Methodology (SSM). The outcome of the research is a practical method – the Method for Educational Analysis and Design (MEAD).
Notes Reproduced with the kind permission of the copyright owner.

New title of journal : Interdisciplinary journal of E-learning and learning objects
Language eng
Field of Research 080609 Information Systems Management
Socio Economic Objective 890399 Information Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2008
Copyright notice ©2008, Informing Science Institute
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017901

Document type: Journal Article
Collections: School of Information and Business Analytics
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