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

Abstract

A person's 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 university's 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.

An Interdisciplinary Study of Perceptions, Efficacy, and Adaptation in Pandemic Pedagogy Supported by the Institutional Data Lake

“…In addition to these demographic data, the ADL also makes available usage data and activity logs related to students' use of university's learning management system. 10,12,13 Data made available through the ADL are used by the university's faculty for educational analytics research purposes 9 . Using data repository to conduct educational analytics is not new.…”

Development and testing of the Sleep Health And Wellness Questionnaire (SHAWQ) in adolescents and university students: composite SHAWQ scores are associated with sleep problems, depression symptoms, and academic performance

“…Most adolescents also get up earlier than their natural wake-up time to reach school on time, resulting in chronic sleep deprivation during the school week (Minges and Redeker, 2016;Wheaton et al, 2016;Bowers and Moyer, 2017;Marx et al, 2017;Alfonsi et al, 2020). Many of these factors persist into young adulthood, when sleep opportunities during the work/school week are often restricted by late night activities and early university start times (Basner et al, 2014;Yeo et al, 2021Yeo et al, , 2023. Sleep problems frequently coincide with mood disturbances and depression.…”