Conference paper 2012

How CSCL moderates the influence of self-efficacy on students' transfer of learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference · Vol. 7563 LNCS · pp. 93-102
Abstract

There is an implicit assumption in learning research that students learn more deeply in complex social and technological environments. Deep learning, in turn, is associated with higher degrees of students' self-efficacy and transfer of learning. The present meta-analysis tested this assumption. Based on social cognitive theory, results suggested positive population correlation estimates between post-training self-efficacy and transfer. Results also showed that effect sizes were higher in trainings with rather than without computer support, and higher in trainings without rather than with collaboration. These findings are discussed in terms of their implications for theories of complex social and computer-mediated learning environments and their practical significance for scaffolding technology-enhanced learning and interaction. © Springer-Verlag 2012.

Keywords

Author Keywords

Training self-efficacy Transfer of learning Computer-Supported Collaborative Learning Meta-analytic moderator estimation

Index Keywords

Engineering education Personnel training Students Self Efficacy Computer aided instruction Online systems transfer of learning Meta-analysis Deep learning Technology enhanced learning effect size Scaffolds Computer Supported Collaborative Learning Social Cognitive Theory Computer support Computer-mediated learning environments Technological environment
Author Affiliations
Technische Universität München, Munich, Bayern, Germany
Turun yliopisto, Turku, Southwest Finland, Finland
Funding & Acknowledgements
No funding information
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