Abstract
This paper presents an approach that uses a decision model for resolving variations in a so-called learning space, which aim is to enhance the reuse of explicitly documented experiences by providing context-aware learning content. Decision models promise a better possibility to separate the variabilities in e-learning content, and address the problem of closed corpus of adaptive hypermedia systems. Adaptation is not coupled to a fixed set of learning resources, but to types of learning space concepts. The system adapts and personalizes the learning space to the learner’s situation. A controlled experiment provides first statistically significant results, which show an experience package reuse improvement regarding knowledge acquisition and application efficiency. Further, it provides a baseline for future evaluations of different adaptation methods and techniques.
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Ras, E., Ilin, D. (2008). Using Decision Models for the Adaptive Generation of Learning Spaces. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2008. Lecture Notes in Computer Science, vol 5149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70987-9_18
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DOI: https://doi.org/10.1007/978-3-540-70987-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70984-8
Online ISBN: 978-3-540-70987-9
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