Biology subjects

Yeo, S. C.

Publications and source records attributed to Yeo, S. C..

3 recordsLinked to original sources

bioRxiv2021

University-wide chronotyping shows late-type students have lower grades, shorter sleep, poorer well-being, lower self-regulation, and more absenteeism

A persons preferred timing of nocturnal sleep (chronotype) has important implications for cognitive performance. Students who prefer to sleep late may have a selective learning disadvantage for morning classes due to inadequate sleep and circadian desynchrony. Here, (1) we tested whether late-type students perform worse only for morning classes, and (2) we investigated factors that may contribute to their poorer academic achievement. Chronotype was determined objectively in 33,645 university students (early, n=3,965; intermediate, n=23,787; late, n=5,893) by analyzing the diurnal distribution of their logins on the universitys Learning Management System (LMS). Late-type students had lower grades than their peers for courses held at all different times of day, and during semesters when they had no morning classes. Actigraphy studies (n=261) confirmed LMS-derived chronotype was associated with students sleep patterns. Nocturnal sleep on school days was shortest in late-type students because they went to bed the latest and woke up early compared with non-school days. Surveys showed that late-type students had lower self-rated health and mood (n=357), and lower metacognitive self-regulation (n=752). Wi-Fi connection data for classrooms (n=17,356) revealed that late-type students had lower lecture attendance than their peers for classes held in both the morning and the afternoon. Our findings suggest that multiple factors converge to impair learning in late-type students. Shifting classes later can improve sleep and circadian synchrony in late-type students but is unlikely to eliminate the performance gap. Interventions that focus on improving students well-being and learning strategies may be important for addressing the late-type academic disadvantage.

neuroscience↗

bioRxiv2021

Large-scale digital traces of university students show that morning classes are bad for attendance, sleep, and academic performance

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 universitys 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. Significance StatementWe show that morning classes are associated with lower attendance, shorter nocturnal sleep, and lower grade point average in university students. Scalable methods for measuring attendance and sleep were developed using students Wi-Fi connection data and interactions with the Learning Management System. Students had lower attendance rates and frequently slept past the start of early morning classes. However, students still lost about an hour of sleep on average when they had early morning classes due to waking up earlier than usual. Students who had morning classes on more days of the week had a lower grade point average. Our results suggest cumulative negative effects of morning classes on students academic performance. Universities should avoid scheduling early morning classes.

neuroscience↗

bioRxiv2020

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

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.

scientific communication and education↗