Learning analytics for learning design in online distance learning

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.

KEYWORDS:

• learning design
• learning analytics
• online distance learning
• clustering
• social network analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was funded by the Data-informed Learning Design for Future Schools of Beijing Advanced Innovation Center for Future Education (AICFE), Beijing Normal University, China (Grant AICFE-IO-013), and this research was supported by 2019 Comprehensive Discipline Construction Fund of Faculty of Education, Beijing Normal University.

Notes on contributors

Wayne Holmes

Dr Wayne Holmes is a lecturer in learning sciences and innovation at the Institute of Educational Technology, The Open University. He holds a PhD and an MSc from the University of Oxford and an MA in Philosophy. He is a member of the UK Parliament’s All Party Parliamentary Group on AI.

Quan Nguyen

Mr Quan Nguyen is a PhD candidate in learning analytics at the UK’s Open University, funded by the Leverhulme Trust. His research focuses on the use of learning analytics on a large scale to understand and improve learning design in higher education.

Jingjing Zhang

Dr Jingjing Zhang is the Director of the Big Data Centre for Technology-mediated Education at Beijing Normal University. She holds a PhD and an MSc from the University of Oxford. Before joining Beijing Normal University, she trained at the OECD, Paris, and then interned at the United Nations headquarters in New York.

Manolis Mavrikis

Dr Manolis Mavrikis is a Reader in Learning Technologies at UCL Knowledge Lab. His interests lie at the intersection of artificial intelligence, human-computer interaction, and educational technology. His research centres on designing evidence-based intelligent technologies that provide feedback to learners, and in employing learning analytics to encourage awareness and understanding of learning processes.

Bart Rienties

Dr Bart Rienties is professor of learning analytics and programme director learning analytics at the Institute of Educational Technology at the UK’s Open University. He is also head of Data Wranglers, where he leads of group of LA academics who conduct evidence-based research and sense making of big data.