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.

Attending classes and sleeping well are important for students’ academic success. Here, we tested whether early morning classes are associated with lower attendance, shorter sleep and poorer academic achievement by analysing university students’ digital traces. Wi-Fi connection logs in 23,391 students revealed that lecture attendance was about ten percentage points lower for classes at 08:00 compared with later start times. Diurnal patterns of Learning Management System logins in 39,458 students and actigraphy data in 181 students demonstrated that nocturnal sleep was an hour shorter for early classes because students woke up earlier than usual. Analyses of grades in 33,818 students showed that the number of days per week they had morning classes was negatively correlated with grade point average. These findings suggest concerning associations between early morning classes and learning outcomes.

Attending classes and sleeping well are important for students' academic success. However, early classes might impede learning by contributing to absenteeism and insufficient sleep. We used big datasets collected passively from university students to test the hypothesis that morning classes are associated with poorer attendance, shorter sleep, and lower grades. Wi-Fi connection data were used to estimate attendance rates of 24,678 students enrolled in lecture courses with start times ranging from 08:00 to 16:00. Students' interactions with the university's Learning Management System (LMS) were used to estimate nocturnal sleep opportunities by compiling 17.4 million logins from 39,458 students with data sorted by students' first class of the day. Objective sleep behavior was assessed in 181 students who took part in a 6-week actigraphy study. We found that Wi-Fi confirmed attendance was about 15 percentage points lower in students taking classes at 08:00 compared with later start times. Actigraphy data revealed that students frequently slept past the start of morning classes. LMS and actigraphy data showed that nocturnal sleep opportunities and total sleep time decreased with earlier class start times due to students waking up earlier. Analyses of grades in 27,281 students showed that having morning classes on more days of the week resulted in a lower grade point average. These findings suggest cumulative negative effects of morning classes on learning. Early morning classes force many students to decide to either sleep more and skip class, or sleep less to attend class. Therefore, universities should avoid scheduling early morning classes.

Background. Historically, brain death legislation was adopted in Asia at a much later stage than it was in the West, with heated public debates surrounding these laws. In this study, we investigated whether the poor acceptance of brain death continues to the present day, focusing on the following: (1) what the Asian public understands brain death to be; (2) how views toward brain death are compared with those of cardiac death; and (3) the extent to which brain death perception contributes to the low rate of deceased organ donation that has been observed amongst Asians. Methods. Using a door-to-door sampling strategy, we recruited 622 residents in Singapore between September 2016 and July 2017. Results. Our results suggest that resistance toward brain death persists, with the majority of respondents equating this as a bleak outcome but not as death. Correspondingly, they considered cardiac death a better indicator of death and were more fearful of being alive during organ donation. In turn, these views predicted a decreased willingness to donate either their own or their family members’ organs. Conclusions. Taken together, our results suggest that views of brain death continue to hamper organ donation, and are seemingly resistant to both time and legislation.

This paper investigates what drives countries to legislate presumed consent-making citizens organ donors by default unless they opt out-instead of explicit consent. A wide range of economic, social, political, institutional, and demographic variables is used. Results reveal the following: (i) civil law predicts presumed consent, which uncovers a mechanism by which an institution that long pre-dates transplantation medicine has an impact on current health outcomes; (ii) Protestantism predicts explicit consent; and (iii) higher pro-social behavior decreases the likelihood of presumed consent. The plausible mechanisms and implications are discussed.