Using learning analytics to understand collective attention in language MOOCs

Abstract

Learning analytics (LA) has the potential to generate new insights into the complexities of learning behaviours in language massive open online courses (LMOOCs). In LA, the collective attention model takes an ecological system view of the dynamic process of unequal participation patterns in online and flexible learning environments. In this study, the ‘Oral Communication for EFL Learners (spring)’ on XuetangX was selected as an example with which to examine the allocation of learner attention in the context of LMOOCs. The open-flow network of collective attention was used to model the dynamics of learning behaviours to understand how different cohorts of second language (L2) learners allocated their attention at the collective level. The results showed that what distinguished high-performing L2 learners was related less to where they started with LMOOC resources or how much attention they allocated to certain learning units and more to the extent to which their attention could be maintained and circulated into other learning units. In addition, learners’ attention typically followed the pre-designed course structure each time they entered the online space. No learning resources offered in the selected LMOOC were found to dominate the collective attention flow, which suggested that L2 learners’ online engagement followed classroom learning patterns. The use of LA to understand the allocation of L2 attention at the collective level provides new perspectives on digital behaviour in LMOOCs, which may facilitate the design of cost-effective L2 resources that prevent learner overload in the information-rich age.

Keywords:

• Collective attention
• LMOOCs
• language MOOCs
• learning analytics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research is supported by Chinese National Science Foundation “Collective Attention in Online Education” (No. 61907004).

Notes on contributors

Shuang Zeng

Shuang Zeng holds a BA in English literature from SISU, an MSc from the University of Oxford and a PhD from the UCL Institute of Education. She now lectures at the Faculty of Foreign Languages, USST. Her research focuses on out-of-class language learning and language learners.

Jingjing Zhang

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 BNU, she trained at the OECD, Paris, and then interned at the UN headquarters in New York.

Ming Gao

Ming Gao is a PhD student at Research Centre of Distance Education, Beijing Normal University. He is interested in using learning analytics to assist the design of online learning.

Kate M. Xu

Kate M. Xu holds a PhD in educational psychology from Oxford University and has trained as a postdoctoral researcher in mental health research in Cambridge University. Her current work includes learning and motivation in traditional and online education.

Jiang Zhang

Jiang Zhang is a Professor in Systems Science. He is specialized in complex systems, human online behaviour, and artificial intelligence. He is the founder of Swarma Club in China.