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Early morning university classes are associated with impaired sleep and academic performance

Original Research By: Sing Chen Yeo, Clin K. Y. Lai, Jacinda Tan, Samantha Lim, Yuvan Chandramoghan, Teck Kiang Tan & Joshua J. Gooley

Summarized By: Neurobit

Regular attendance in classes and adequate sleep are key factors that contribute to academic success in university students. Attending classes offers students an opportunity to interact with instructors and peers and provides a structured approach to cover important learning points. Sleep is essential for cognitive performance, and insufficient sleep can impair attention and memory processes, resulting in decreased learning potential in class. Feeling tired and oversleeping are some of the reasons why students skip classes. Presenteeism and absenteeism, which can be caused by poor attendance and sleep behavior, can have long-term consequences on students’ employment opportunities, job performance ratings, and salary.

The class start times can also affect students' sleep and daytime functioning, with early start times being particularly detrimental for university students. Students who stay up late and wake up early for class may have shorter nocturnal sleep, leading to daytime sleepiness and impaired cognitive performance. In the university context, changes in students' social and learning environments may influence their sleep and learning behavior. University students may have later bedtimes on school nights compared to high school students due to increased autonomy in how they spend their time. Attendance is also rarely monitored in university lectures or seminars, leading to students skipping classes and impacting their grades.

Universities need scalable methods to evaluate the impact of class start times on students' behavior. Mobile digital technologies that detect when students are present in the classroom, such as smartphone sensors, Wi-Fi connections, and mobile application data, could be used to track class attendance on a larger scale. Such methods can help universities understand how class start times affect attendance and sleep behavior and can inform policies that improve students' academic success. Large-scale analyses of students' digital traces, such as social media and smartphone interactions, can also provide insights into the relationship between class start times and sleep-wake behavior. Overall, adopting practices that improve attendance rates and sleep behavior can help position university students for success in the classroom and the workforce.

In the current study, Sing Chen Yeo and colleagues (2023), researchers from the National University of Singapore (NUS) Institute for Applied Learning Sciences and Educational Technology (ALSET), have developed a method of using Wi-Fi metadata to confirm class attendance. The study involved logging the Wi-Fi connections of students at NUS, which was cross-referenced with their course timetables. The researchers validated their method by collecting attendance data from instructors across 53 class sessions in 13 different courses. The Wi-Fi-confirmed attendance rate was determined for 337 large lecture courses that met certain criteria. The study also analyzed students' interactions with the university's learning management system (LMS) over five semesters using data from the ALSET Data Lake. The researchers sorted the LMS data according to each student's first class start time of the day and analysed the diurnal time courses of logins separately in each semester.

The study analyzed the course grades of students over six semesters using data on Wi-Fi connection and learning management system (LMS). Students' grades were represented by letter grades which were converted to grade points. The study grouped data by morning, afternoon, and mixed-timing courses. Linear mixed-effects models were used to examine associations between class start time and academic performance. The study used a cross-classified model to test the association between class start time and Wi-Fi-confirmed attendance. The study found significant associations between class start time and academic performance, Wi-Fi-confirmed attendance, and LMS-derived parameters. Finally, linear mixed-effects models were used to test the association between students' first-class time of the day and actigraphy-derived nocturnal sleep variables.

The results of the analysis found that the time of day of a university student's first class had an association with their sleep behavior and learning-related outcomes. Early class start times often resulted in students having to choose between sleeping longer or attending class. Attendance rates were lower in students who took classes at 08:00 compared to later times, leading to a potential loss of 1 hour of sleep on average. The study found that students who had more days of morning classes had a lower grade point average, suggesting negative effects on absenteeism and presenteeism, leading to poorer academic achievement.

The study also found that Wi-Fi-confirmed attendance rates were lower for classes that started earlier in the day, and combining students' Wi-Fi connection logs with their course timetables allowed for the estimation of class attendance across hundreds of courses with different start times. The LMS login data was used to estimate the nocturnal sleep opportunities, and the study observed a delay in the nocturnal inactive period on weekends and holiday periods compared with weekdays, but they did not investigate the effects of different school/work start times. Also, the researchers found that the LMS login onset closely tracked students' wake-up time for morning classes, resulting in a shorter LMS inactive period compared with afternoon start times or non-school days.

The study provided evidence that early morning classes contributed to university-wide sleep debt and circadian misalignment. Nocturnal sleep duration was shorter on nights that preceded morning classes, and the midpoint of sleep occurred earlier for morning classes, suggesting greater social jet lag. The frequency of daytime naps was higher when students had morning classes, and this suggested that students were sleepier compared to days with later class start times. The study found that course grades were statistically lower for courses held only in the morning versus only in the afternoon, but the difference was very small and probably not meaningful.


Yeo, S. C., Lai, C. K. Y., Tan, J., Lim, X. Y., Lee, C. K., & Ngiam, J. N. (2023). Early morning university classes are associated with impaired sleep and academic performance. Nature Human Behaviour.

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