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Predicting Visuospatial and Verbal Working Memory by Individual Differences in E-Learning Activities
Umeå University, Sweden.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (CeLekt)ORCID iD: 0000-0001-9062-1609
Hypocampus AB, Sweden.
Umeå University, Sweden.
2020 (English)In: Frontiers in Education, E-ISSN 2504-284X, Vol. 5, article id 22Article in journal (Refereed) Published
Abstract [en]

E-learning is being considered as a widely recognized option to traditional learning environments, allowing for highly tailor-made adaptive learning paths with the goal to maximize learning outcomes. However, for being able to create personalized e-learning systems, it is important to identify relevant student prerequisites that are related learning success. One aspect crucial for all kind of learning that is relatively unstudied in relation to e-learning is working memory (WM), conceptualized as the ability to maintain and manipulate incoming information before it decays. The aim of the present study was to examine how individual differences in online activities is related to visuospatial- and verbal WM performance. Our sample consisted of 98 participants studying on an e-learning platform. We extracted 18 relevant features of online activities tapping on Quiz accuracy, Study activity, Within-session activity, and Repetitive behavior. Using best subset multiple regression analyses, the results showed that individual differences in online activities significantly predicted verbal WM performance (p < 0.001, R2Adjusted = 0.166), but not visuospatial WM performance (p = 0.058, R2Adjusted= 0.065). The obtained results contribute to the existing research of WM in e-learning environments, and further suggest that individual differences in verbal WM performance can be predicted by how students interact on e-learning platforms.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2020. Vol. 5, article id 22
National Category
Computer Sciences Pedagogy
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
URN: urn:nbn:se:lnu:diva-93103DOI: 10.3389/feduc.2020.00022OAI: oai:DiVA.org:lnu-93103DiVA, id: diva2:1416621
Available from: 2020-03-24 Created: 2020-03-24 Last updated: 2020-03-25Bibliographically approved

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Lincke, Alisa

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67891011129 of 159
CiteExportLink to record
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  • ieee
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  • de-DE
  • en-GB
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  • Other locale
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Output format
  • html
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