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Learning analytics for learning design in online distance learning

Holmes, Wayne; Nguyen, Quan; Zhang, Jingjing; Mavrikis, Manolis and Rienties, Bart (2019). Learning analytics for learning design in online distance learning. Distance Education (Early Access).

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DOI (Digital Object Identifier) Link: https://doi.org/10.1080/01587919.2019.1637716
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Abstract

There has been a growing interest in how teaching might be informed by learning design (LD), with a promising method for investigating LD being offered by the emerging field of learning analytics (LA). In this study, we used a novel LA for LD methodology to investigate the implementation of LD in an online distance learning context. A key innovation is the focus on patterns of LD. Using data from the virtual learning environment, outcomes data, and self-reports, for 47,784 students, we investigated the impact of those patterns on student behaviour, pass rates and satisfaction. A second innovation involves social network analysis. Our study revealed that different patterns of LD were associated with statistically significant differences in behaviour, but not in pass rates or satisfaction. Nonetheless, the study highlights that applying LA to LD might, in a virtuous circle, contribute to the validity and effectiveness of both, and to the enhancement of online distance learning.

Item Type: Journal Item
Copyright Holders: 2019 Open and Distance Learning Association of Australia, Inc.
ISSN: 0158-7919
Project Funding Details:
Funded Project NameProject IDFunding Body
Future EducationNot SetAdvanced Innovation Center for Future Education, Beijing Normal University
Keywords: learning design; learning analytics; online distance learning; clustering; social network analysis
Academic Unit/School: Faculty of Wellbeing, Education and Language Studies (WELS)
Item ID: 62607
Depositing User: Wayne Holmes
Date Deposited: 29 Jul 2019 08:35
Last Modified: 30 Jul 2019 04:36
URI: http://oro.open.ac.uk/id/eprint/62607
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