dc.contributor.author | Tsianos, Nikos | en |
dc.contributor.author | Germanakos, Panagiotis | en |
dc.contributor.author | Lekkas, Zacharias | en |
dc.contributor.author | Mourlas, Constantinos | en |
dc.contributor.author | Samaras, George S. | en |
dc.contributor.author | Belk, Marios | en |
dc.creator | Tsianos, Nikos | en |
dc.creator | Germanakos, Panagiotis | en |
dc.creator | Lekkas, Zacharias | en |
dc.creator | Mourlas, Constantinos | en |
dc.creator | Samaras, George S. | en |
dc.creator | Belk, Marios | en |
dc.date.accessioned | 2019-11-13T10:42:33Z | |
dc.date.available | 2019-11-13T10:42:33Z | |
dc.date.issued | 2009 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/55102 | |
dc.description.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 | en |
dc.description.abstract | however, through proper personalization techniques this difference is completely alleviated, leveling the performance of low and normal WM span learners. © 2009 Springer Berlin Heidelberg. | en |
dc.source | 17th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2009 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349810575&doi=10.1007%2f978-3-642-02247-0_41&partnerID=40&md5=3806e53fa1973ebbdc29003c158768d3 | |
dc.subject | Education | en |
dc.subject | Hypermedia | en |
dc.subject | Data processing | en |
dc.subject | Multimedia systems | en |
dc.subject | Human factors | en |
dc.subject | Human engineering | en |
dc.subject | Personalizations | en |
dc.subject | Working memories | en |
dc.subject | Individual Differences | en |
dc.subject | Information processing | en |
dc.subject | User Profiling | en |
dc.subject | e-Learning application | en |
dc.subject | Adaptive Hypermedia | en |
dc.subject | Working Memory | en |
dc.subject | E-learning environment | en |
dc.subject | Accurate measurement | en |
dc.subject | Adaptation techniques | en |
dc.subject | E-Learning | en |
dc.subject | University students | en |
dc.title | Working memory differences in e-learning environments: Optimization of learners' performance through personalization | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/978-3-642-02247-0_41 | |
dc.description.volume | 5535 LNCS | en |
dc.description.startingpage | 385 | |
dc.description.endingpage | 390 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Conference code: 77286 | en |
dc.description.notes | Cited By :1</p> | en |
dc.source.abbreviation | Lect. Notes Comput. Sci. | en |
dc.contributor.orcid | Belk, Marios [0000-0001-6200-0178] | |
dc.contributor.orcid | Lekkas, Zacharias [0000-0003-2049-8187] | |
dc.gnosis.orcid | 0000-0001-6200-0178 | |
dc.gnosis.orcid | 0000-0003-2049-8187 | |