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Invisible Learning in Online Systems.

Authors: Khursheed, Bridget;

Invisible Learning in Online Systems.

Abstract

This paper shows how to maximise the online learning experience for different user groups. Its premise of invisible learning centres on the argument that, while users are absorbing information from the screen by reading or scanning, the message can be reinforced by subliminal uptake resulting from the online system s structure, layout and navigation. This paper discusses design and information architecture techniques that optimise invisible learning and avoid its potential pitfalls; it also explains how to test for its occurrence amongst users. Examples are provided from the Enabling Mothers to Understand Maternity (E-Mum) project at Oxford University whose target audience of teenage mothers are often hampered by lack of both computer and general literacy. Special consideration has been given during the development period of this project to exploiting invisible learning opportunities culled from fields as diverse as gaming and teenage magazines.

Proceedings of the 7th International Conference of European University Information Systems, EUNIS2001, 28.03.2001 - 30.03.2001, Humboldt-Universität zu Berlin, pp 21-24

Country
Germany
Related Organizations
Keywords

usability, online education, tele-monitoring, learning object, 01 Wissenschaft und Kultur allgemein

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green