AUTHOR=Fellman Daniel , Lincke Alisa , Berge Elias , Jonsson Bert 

TITLE=Predicting Visuospatial and Verbal Working Memory by Individual Differences in E-Learning Activities

JOURNAL=Frontiers in Education

VOLUME=5

YEAR=2020

URL=https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2020.00022

DOI=10.3389/feduc.2020.00022

ISSN=2504-284X

ABSTRACT=<p>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 <italic>Quiz accuracy, Study activity, Within-session activity</italic>, and <italic>Repetitive behavior</italic>. Using best subset multiple regression analyses, the results showed that individual differences in online activities significantly predicted verbal WM performance (<italic>p</italic> &lt; 0.001, <inline-formula><mml:math id="M1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mtext>Adjusted</mml:mtext></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula> = 0.166), but not visuospatial WM performance (<italic>p</italic> = 0.058, <inline-formula><mml:math id="M2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mtext>Adjusted</mml:mtext></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula> = 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.</p>