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Link to original content: https://doi.org/10.1007/978-3-642-13470-8_8
Working Memory Span and E-Learning: The Effect of Personalization Techniques on Learners’ Performance | SpringerLink
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Working Memory Span and E-Learning: The Effect of Personalization Techniques on Learners’ Performance

  • Conference paper
User Modeling, Adaptation, and Personalization (UMAP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6075))

Abstract

This research paper presents the positive effect of incorporating individuals’ working memory (WM) span as a personalization factor in terms of improving users’ academic performance in the context of adaptive educational hypermedia. The psychological construct of WM is robustly related to information processing and learning, while there is a wide differentiation of WM span among individuals. Hence, in an effort to examine the role of cognitive and affective factors in adaptive hypermedia along with psychometric user profiling considerations, WM has a central role in the authors’ effort to develop a user information processing model. Encouraged by previous findings, a larger scale study has been conducted with the participation of 230 university students in order to elucidate if it is possible through personalization to increase the performance of learners with lower levels of WM span. According to the results, users with low WM performed better in the personalized condition, which involved segmentation of the web content and aesthetical annotation, while users with medium/high WM span were slightly negatively affected by the same techniques. Therefore, it can by supported it is possible to specifically address the problem of low WM span with significant results.

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Tsianos, N., Germanakos, P., Lekkas, Z., Mourlas, C., Samaras, G. (2010). Working Memory Span and E-Learning: The Effect of Personalization Techniques on Learners’ Performance. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-13470-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13469-2

  • Online ISBN: 978-3-642-13470-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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