Using learning analytics to understand collective attention in language MOOCs

Shuang Zeng, Jingjing Zhang*, Ming Gao, M. Xu, Jiang Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Web of Science)


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.
Original languageEnglish
Pages (from-to)1594-1619
Number of pages26
JournalComputer Assisted Language Learning
Issue number7
Early online date30 Sept 2020
Publication statusPublished - 3 Sept 2022


  • Collective attention
  • LMOOCs
  • language MOOCs
  • learning analytics


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