A targeted e-learning approach to reduce student mixing during a pandemic

Abstract

The COVID-19 pandemic has resulted in widespread closure of schools and universities. These institutions have turned to distance learning to provide educational continuity. Schools now face the challenge of how to reopen safely and resume in-class learning. However, there is little empirical evidence to guide decision-makers on how this can be achieved. 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. Analyses of >24 million student connections to the university Wi-Fi network revealed that 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 show that e-learning interventions can decrease potential for disease transmission while minimizing disruption to university operations. Universities should consider targeted e-learning a viable strategy for providing educational continuity during early or late stages of a disease outbreak.