Modeling collective attention in online and flexible learning environments

ABSTRACT

Understanding how collective attention flow circulates amid an over-abundance of knowledge is a key to designing new and better forms of online and flexible learning experiences. This study adopted an open flow network model and the associated distance metrics to gain an understanding of collective attention flow using clickstream data in a massive open online course. Various patterns and dynamics of attention flow were identified and are discussed here in relation to learning performance. The results show that the effective accumulation, circulation, and dissipation of attention flow are important contributors to academic attainment. Understanding the patterns and dynamics of attention flow will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.

KEYWORDS:

• MOOCs
• collective attention
• open flow network
• learning performance
• free
• distance education

Acknowledgments

The authors would like to thank XuetangX for providing us clickstream data for this research work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the BNU Big Data Centre for Technology mediated Education Foundation [Project No. 312231103], and Chinese National Science Foundation [Project No. 61673070]This project is funded by Interdisciplinary Platform Project [N01390].

Notes on contributors

J. Zhang

J. 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.

X. Lou

X. Lou is a PhD student in the School of Systems Science, Beijing Normal University.

H. Zhang

J. 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.

H. Zhang is a master’s student in the Research Centre of Distance Education, Beijing Normal University.

J. Zhang

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