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

  1. Sing Chen Yeo
  2. Clin K. Y. Lai
  3. Jacinda Tan
  4. Joshua J. Gooley

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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.

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  1. Version published to 10.1371/journal.pone.0249839
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    SciScore for 10.1101/2020.06.10.135533: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Modelling and regression analyses were performed using Sigmaplot software (Version 14; Systat Software, Inc) and R statistical software.
    Sigmaplot
    suggested: (SigmaPlot, RRID:SCR_003210)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.06.10.135533v1 on bioRxiv