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Predicting learning success in online learning environments: Self-regulated learning, prior knowledge and repetition

Ledermüller, Karl and Fallmann, Irmgard (2017) Predicting learning success in online learning environments: Self-regulated learning, prior knowledge and repetition. Zeitschrift für Hochschulentwicklung, 12 (1). pp. 79-99. ISSN 22196994

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Abstract

The emergence of new trends sometimes carries the risk that established, well-proven concepts rooted in other disciplines are not properly integrated into new approaches. As Learning Analytics seems to be evolving into a highly multidisciplinary field, we would like to demonstrate the importance of embedding classic theories and concepts into a Learning Analytics, system-data-driven setting. Our results confirm that classical factors that are operationalized with the help of system-generated data outperform more recent survey-based models. Therefore, we want to stress the point that system-generated data should not be left behind in the quickly evolving field of Learning Analytics.

Item Type: Article
Keywords: Repetition, memory, prior knowledge, self-regulated learning, learning effectiveness
Divisions: Departments > Management
Version of the Document: Published
Variance from Published Version: None
Depositing User: Mohammad Al Hessan
Date Deposited: 15 Mar 2018 13:42
Last Modified: 15 Mar 2018 17:02
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/82354/
URI: http://epub.wu.ac.at/id/eprint/6138

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