Decrypting Log Data : A Meta-Analysis on General Online Activity and Learning Outcome Within Digital Learning Environments





Faculty/Professorship: Leibniz Institute for Educational Trajectories (LIfBi) 
Author(s): Klose, Maria ; Steger, Diana; Fick, Julian ; Artelt, Cordula  
Title of the Journal: Zeitschrift für Psychologie
ISSN: 2151-2604, 2190-8370
Publisher Information: Göttingen [u.a.] : Hogrefe
Year of publication: 2022
Volume: 230
Issue: 1
Pages: 3-15
Language(s): English
DOI: 10.1027/2151-2604/a000484
Abstract: 
Analyzing log data from digital learning environments provides information about online learning. However, it remains unclear how this information can be transferred to psychologically meaningful variables or how it is linked to learning outcomes. The present study summarizes findings on correlations between general online activity and learning outcomes in university settings. The course format, instructions to engage in online discussions, requirements, operationalization of general online activity, and publication year are considered moderators. A multi-source search provided 41 studies (N = 28,986) reporting 69 independent samples and 104 effect sizes. The three-level random-effects meta-analysis identified a pooled effect of r = .25 p = .003, 95% CI [.09, .41], indicating that students who are more active online have better grades. Despite high heterogeneity, Q(103) = 3,960.04, p < .001, moderator analyses showed no statistically significant effect. We discuss further potential influencing factors in online courses and highlight the potential of learning analytics.
GND Keywords: E-Learning; Studienerfolg; Lernpsychologie
Keywords: online learning, log data, learning analytics, academic achievement, meta-analysis
DDC Classification: 370 Education  
RVK Classification: AL 4050   
Peer Reviewed: Ja
International Distribution: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/54291
Release Date: 1. July 2022