A targeted e-learning approach for keeping universities open during the COVID-19 pandemic while reducing student physical interactions
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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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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 Sentences Resources Modelling and regression analyses were performed using Sigmaplot software (Version 14; Systat Software, Inc) and R statistical software. Sigmaplotsuggested: (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 …
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 Sentences Resources Modelling and regression analyses were performed using Sigmaplot software (Version 14; Systat Software, Inc) and R statistical software. Sigmaplotsuggested: (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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