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A targeted e-learning approach for keeping universities open during the COVID-19 pandemic while reducing student physical interactions

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  • Sing Chen Yeo
  • Clin K Y Lai
  • Jacinda Tan
  • Joshua J Gooley

Abstract

The COVID-19 pandemic led to widespread closure of universities. Many universities turned to e-learning to provide educational continuity, but they now face the challenge of how to reopen safely and resume in-class learning. This is difficult to achieve without methods for measuring the impact of school policies on student physical interactions. Here, we show that selectively deploying e-learning for larger classes is highly effective at decreasing campus-wide opportunities for student-to-student contact, while allowing most in-class learning to continue uninterrupted. We conducted a natural experiment at a large university that implemented a series of e-learning interventions during the COVID-19 outbreak. The numbers and locations of 24,000 students on campus were measured over a 17-week period by analysing >24 million student connections to the university Wi-Fi network. We show that daily population size can be manipulated by e-learning in a targeted manner according to class size characteristics. Student mixing showed accelerated growth with population size according to a power law distribution. Therefore, a small e-learning dependent decrease in population size resulted in a large reduction in student clustering behaviour. Our results suggest that converting a small number of classes to e-learning can decrease potential for disease transmission while minimising disruption to university operations. Universities should consider targeted e-learning a viable strategy for providing educational continuity during periods of low community disease transmission.

Suggested Citation

  • Sing Chen Yeo & Clin K Y Lai & Jacinda Tan & Joshua J Gooley, 2021. "A targeted e-learning approach for keeping universities open during the COVID-19 pandemic while reducing student physical interactions," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
  • Handle: RePEc:plo:pone00:0249839
    DOI: 10.1371/journal.pone.0249839
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    References listed on IDEAS

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    1. Ruiqi Li & Lei Dong & Jiang Zhang & Xinran Wang & Wen-Xu Wang & Zengru Di & H. Eugene Stanley, 2017. "Simple spatial scaling rules behind complex cities," Nature Communications, Nature, vol. 8(1), pages 1-7, December.
    2. Wei Pan & Gourab Ghoshal & Coco Krumme & Manuel Cebrian & Alex Pentland, 2013. "Urban characteristics attributable to density-driven tie formation," Nature Communications, Nature, vol. 4(1), pages 1-7, October.
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    Cited by:

    1. Adi Jafar & Ramli Dollah & Ramzah Dambul & Prabhat Mittal & Syahruddin Awang Ahmad & Nordin Sakke & Mohammad Tahir Mapa & Eko Prayitno Joko & Oliver Valentine Eboy & Lindah Roziani Jamru & Andika Ab. , 2022. "Virtual Learning during COVID-19: Exploring Challenges and Identifying Highly Vulnerable Groups Based on Location," IJERPH, MDPI, vol. 19(17), pages 1-16, September.
    2. Sing Chen Yeo & Clin K. Y. Lai & Jacinda Tan & Samantha Lim & Yuvan Chandramoghan & Teck Kiang Tan & Joshua J. Gooley, 2023. "Early morning university classes are associated with impaired sleep and academic performance," Nature Human Behaviour, Nature, vol. 7(4), pages 502-514, April.

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