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, 40(3) pp. 309–329.

DOI: https://doi.org/10.1080/01587919.2019.1637716

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.

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About

• Item ORO ID
• 62607
• Item Type
• Journal Item
• ISSN
• 0158-7919
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Future Education	Not Set	Advanced Innovation Center for Future Education, Beijing Normal University

• Keywords
• learning design; learning analytics; online distance learning; clustering; social network analysis
• Academic Unit or School
• Institute of Educational Technology (IET)
• Copyright Holders
• © 2019 Open and Distance Learning Association of Australia, Inc.
• Depositing User
• Wayne Holmes