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

• Published in: PloS one Vol. 16; no. 4; p. e0249839
• Main Authors: Yeo, Sing Chen, Lai, Clin K Y, Tan, Jacinda, Gooley, Joshua J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.04.2021; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Colleges & universities; Computer and Information Sciences; Computer-Assisted Instruction; Contact potentials; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - prevention & control; Disease control; Disease transmission; Disorders; Distance learning; Editing; Education, Distance; Educational aspects; Epidemics; Female; Health aspects; Humans; Learning; Male; Medicine and Health Sciences; Nervous system; Online instruction; Pandemics; People and Places; Prevention; Public health; Reviews; SARS-CoV-2; Singapore; Social aspects; Social Sciences; Socio-economic aspects; Students; Technology; Universities; Universities and colleges; Viral diseases; Visualization; Singapore
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0249839

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.