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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.

BackgroundA cross-sectional study to ascertain what the Singapore population would regard as material risk in the anaesthesia consent-taking process and identify demographic factors that predict patient preferences in medical decision-making to tailor a more patient-centered informed consent.MethodsA survey was performed involving patients 21 years old and above who attended the pre-operative evaluation clinic over a 1-month period in Singapore General Hospital. Questionnaires were administered to assess patients’ perception of material risks, by trained interviewers. Patients’ demographics were obtained. Mann–Whitney U test and Kruskal-Wallis one-way analysis of variance was used. Statistical significance was taken at p < 0.05.ResultsFour hundred fourteen patients were eligible of which 26 refused to participate and 24 were excluded due to language barrier. 364 patients were recruited. A higher level of education (p < 0.007), being employed (p < 0.046) and younger age group (p < 0.003) are factors identified in patients who wanted greater participation in medical decisions. Gender, marital status, type of surgery, and previous surgical history did not affect their level of participation. The complications most patients knew about were Nausea (64.8%), Drowsiness (62.4%) and Surgical Wound Pain (58.8%). Patients ranked Heart Attack (59.3%), Death (53.8%) and Stroke (52.7%) as the most significant risks that they wanted to be informed about in greater detail.Most patients wanted to make a joint decision with the anaesthetist (52.2%), instead of letting the doctor decide (37.1%) or deciding for themselves (10.7%). Discussion with the anaesthetist (61.3%) is the preferred medium of communication compared to reading a pamphlet (23.4%) or watching a video (15.4%).ConclusionAge and educational level can influence medical decision-making. Despite the digital age, most patients still prefer a clinic consult instead of audio-visual multimedia for pre-operative anaesthetic counselling. The local population appears to place greater importance on rare but serious complications compared to common complications. This illustrates the need to contextualize information provided during informed consent to strengthen the doctor-patient relationship.Electronic supplementary materialThe online version of this article (doi:10.1186/s12910-017-0172-2) contains supplementary material, which is available to authorized users.

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.