Abstract
Working memory (WM) is a psychological construct that has a major effect on information processing, thus signifying its importance when considering individual differences and adaptive educational hypermedia. Previous work of the authors in the field has demonstrated that personalization on human factors, including the WM sub-component of visuospatial sketchpad, may assist learners in optimizing their performance. To that end, a deeper approach in WM has been carried out, both in terms of more accurate measurements and more elaborated adaptation techniques. This paper presents results from a sample of 80 university students, underpinning the importance of WM in the context of an e-learning application in a statistically robust way. In short, learners that have low WM span expectedly perform worse than learners with higher levels of WM span; however, through proper personalization techniques this difference is completely alleviated, leveling the performance of low and normal WM span learners.
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Tsianos, N., Germanakos, P., Lekkas, Z., Mourlas, C., Samaras, G., Belk, M. (2009). Working Memory Differences in E-Learning Environments: Optimization of Learners’ Performance through Personalization. In: Houben, GJ., McCalla, G., Pianesi, F., Zancanaro, M. (eds) User Modeling, Adaptation, and Personalization. UMAP 2009. Lecture Notes in Computer Science, vol 5535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02247-0_41
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DOI: https://doi.org/10.1007/978-3-642-02247-0_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02246-3
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