The value of adaptive link annotation in e-learning: A study of a portal-based approach
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
ACMDate
2010Citation
10.1145/1810617.1810657
HT '10: Proceedings of the 21st ACM conference on Hypertext and hypermedia. New York: ACM, 2010. 223 - 228
ISBN
978-1-4503-0041-4DOI
10.1145/1810617.1810657Funded by
This work is supported by National Science Foundation under Grant IIS-0447083, Spanish Ministry of Science and Education (TIN2007-64718) and the Comunidad Autónoma de Madrid (S2009/TIC-1650)Project
Comunidad de Madrid. S2009/TIC-1650/E-MADRIDEditor's Version
http://dx.doi.org/10.1145/1810617.1810657Subjects
Adaptive hypermedia; E-learning; Motivation; Navigation support; Open corpus adaptive hypermedia; Portal self-assessment; InformáticaNote
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 21st ACM conference on Hypertext and hypermedia, http://dx.doi.org/10.1145/1810617.1810657Rights
© Copyright 2010 ACMAbstract
Adaptive link annotation is one of the most popular adaptive educational hypermedia techniques. It has been widely studied and demonstrated its ability to help students to acquire knowledge faster, improve learning outcomes, reduce navigation overhead, increase motivation, and encourage the beneficial non-sequential navigation. However, almost all studies of adaptive link annotation have been performed in the context of dedicated adaptive educational hypermedia systems. The role of this technique in the context of widely popular learning portals has not yet been demonstrated. In this paper, we attempt to fill this gap by investigating the value of adaptive navigation support embedded into the learning portal. We compare the effect of portal-based adaptive navigation support to both the effect of the adaptive navigation support in adaptive educational hypermedia systems and to non-adaptive learning portals.
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Google Scholar:Hsiao, I. Han
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Brusilovsky, Peter L.
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Yudelson, Michael V.
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Ortigosa Juárez, Álvaro Manuel
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