Lai, C. K. Y. | bioRxiv Science

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↗

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↗