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

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.

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Software and code
Policy information about availability of computer code Data collection No software was used to collect data in this study.

Data analysis
In the actigraphy study, data were analyzed using Actiware software (version 6.0.9).
Pearson's correlation analysis and chi-squared tests were performed using SigmaPlot software (version 14.5, Systat Software, Inc.).
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March 2021
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy The actigraphy data that support the findings of this study are available as source data (Supplementary Information) with the published article. University-archived data cannot be shared publicly because of legal and university restrictions where the research was conducted. In compliance with the Singapore Personal Data Protection Act, data stored on the NUS Institute for Applied Learning Sciences and Educational Technology (ALSET) Data Lake is defined as personal data and cannot be shared publicly without student consent. Data can be accessed and analyzed on the ALSET Data Lake server with approval by the NUS Learning Analytics Committee on Ethics, in accordance with NUS data management policies. Researchers who wish to access the data should contact ALSET at NUS (email: alsbox1@nus.edu.sg).

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Behavioural & social sciences study design
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Study description
The study included (1) retrospective analyses of university-archived student data and (2) analyses of university students' sleep behavior using actigraphy. All data are quantitative.

Research sample
The sample comprised undergraduate students enrolled at the National University of Singapore (NUS). University-archived student data were representative of students enrolled at NUS (average age of 21 years, 51% female, 87% Chinese) because we used all available data of the student population. Actigraphy studies were performed in a smaller sample of students who were recruited from the general student population. The sample was representative of students at NUS but included a higher percentage of women (average age of 21 years, 64% female, 89% Chinese). The rationale for studying these samples was to test the hypothesis that early morning classes result in lower class attendance, shorter sleep, and poorer grades.

Sampling strategy
Analyses of students' university-archived data included Wi-Fi connection logs, Learning Management System logins and grades. No sample size calculation was performed because we performed a retrospective analysis of all available student data.
Analyses of actigraphy data were based on a 6-week study of students' natural sleep behavior during the school semester. The data were collected to investigate relationships between students' sleep behavior and neurobehavioral performance (results not reported here). The sample of 181 students was sufficient for comparing sleep behavior between different class start times because each student contributed data for multiple class start times. We restricted our analyses to class start times in which there were at least 20 individuals whose first class of the day started at that time (08:00, n=103; 09:00, n=61, 10:00, n=123, 11:00, n=35, 12:00, n=107; 14:00, n=71; 16:00, n=44) to ensure that we had enough participants to make meaningful comparisons between groups. The dataset comprised 3,701 nocturnal sleep recordings on school nights and 3,129 nocturnal sleep recordings on non-school nights. Effect sizes of class start times for the primary sleep variables (wake-up time and nocturnal sleep duration) were medium-to-large. No statistical methods were used to pre-determine the sample size for the actigraphy study. However, the sample size was comparable to prior studies conducted in high school students that compared sleep behaviour between different school start times (e.g., Dunster et al., Sleepmore in Seattle: Later school start times are associated with more sleep and better performance in high school students. Science Advances 4, 2018).

Data collection
University-archived datasets were obtained from the National University of Singapore (NUS) Institute for Applied Learning Sciences and Educational Technology (ALSET). ALSET stores and links de-identified student data for educational analytics research. Universityarchived datasets included students' demographic information (age, sex, ethnicity, year of matriculation), course enrolment and class timetables, Wi-Fi connection data, Learning Management System (LMS) data, and grades. Demographic information, course enrolment and class timetables, and grades were provided by the NUS Registrar's Office which is responsible for keeping all student records. Wi-Fi connection data and LMS data were provided by NUS Information Technology (IT). The NUS wireless network comprises several thousand Wi-Fi access points. Each time that a student's Wi-Fi enabled device associated with the NUS wireless network the transmission data were logged. Students' Wi-Fi connection data were added to the ALSET Data Lake by a data pipeline managed by NUS IT. Each data point included the tokenized student identity, the anonymised media access control (MAC) address used to identify the Wi-Fi enabled device (e.g., smartphone, laptop, or tablet), the name and location descriptor of the Wi-Fi access point, and the start and end time of each Wi-Fi connection. LMS login data were extracted from students' logged interactions with the NUS Integrated Virtual Learning Environment (IVLE). The IVLE is a LMS designed and built by NUS for administering course content. Each data point included the type of student interaction (e.g., login, download, upload, logoff) and timestamp. NUS IT was responsible for merging all data with the ALSET Data Lake. The same student-specific tokens were represented across data tables, allowing for different types of data to be combined without knowing students' identities.

March 2021
The researchers were not present during collection of university-archived datasets. The data were collected from naturally behaving students who were using the university's resources (e.g., Wi-Fi network and LMS) as part of normal student life. During the period of data collection the students would have interacted with other individuals on campus, including other students, faculty, full-time employees of the university, and visitors. The researchers only had access to student data on the ALSET Data Lake. The researchers were not blinded to the experimental conditions (i.e., students' class start time) or study hypothesis when analysing the data.
Actigraphy data were collected from NUS undergraduates who were recruited to take part in a 6-week research study of their sleepwake patterns during the school semester. Participants wore an actigraphy watch (Actiwatch Spectrum Plus or Actiwatch 2; Philips Respironics Inc., Pittsburgh, PA) on their non-dominant hand and made weekly visits to a classroom to have their data downloaded by the researchers. Students submitted their class timetable at the end of the study period. The researchers were not blinded to the experimental conditions of the study (i.e., students' class start time) or the study hypothesis. The researchers checked whether participants complied with wearing the actigraphy watch, but they did not analyze the data until after all data was collected. Actograms were inspected, reviewed, and approved by all members of the research team before analyzing the data to derive sleep variables. Subsequently, the sleep data were sorted by students' first class of the day using their class timetable.

Timing
University-archived data were analyzed using all available data on the ALSET Data Lake prior to the COVID-19 pandemic: (1)

Data exclusions
In analyses of university-archived student data: (1) Wi-confirmed attendance was investigated only for courses that (i) were categorized as a lecture course according to the university timetable, (ii) were held once per week, (iii), were held at least 7 times over the 13-week semester, (iv) lasted 2 h per session, and (v) had an enrollment of at least 100 undergraduate students. The rationale for these criteria was to ensure that comparable types of courses were included in analyses across different class start times. Among the 436 courses that met these criteria, 71 were excluded due to missing or incomplete Wi-Fi connection data or inconsistencies with the class timetable (e.g., due to cancelled or rescheduled classes). The remaining 365 courses were sorted by their start time, and data were analyzed only for those start times in which there were at least 5 courses per semester. This ensured that all class start times included at least 15 different courses spanning a comparable time period (08:00, 21 courses; 09:00, 18 courses; 10:00, 89 courses; 12:00, 67 courses; 14:00, 72 courses; 16:00, 70 courses). The final dataset included 337 courses and 23,391 unique students.
(2) All available Learning Management System (LMS) data were used. There were no exclusionary criteria.
(3) Grade point average was analyzed in students who earned 20 course credits in a given semester. The rationale for this criterion was to ensure that students in our analyses had a comparable total workload. The criterion was determined by taking the mode of the distribution for course credits.
In the actigraphy study, data were excluded (i) for 2 individuals because of poor quality data (the researchers could not determine the time-in-bed intervals for sleep scoring), and (ii) for 1 individual who failed to provide his course timetable with his class start times. The dataset included 181 student participants with 7,329 nocturnal sleep recordings (range, 27-42 days per individual).

Non-participation
In analyses of university-archived student data, the issue of non-participation is not applicable.
In the actigraphy study, there were 202 undergraduate students who enrolled in the study. There were 13 participants who withdrew before the end of the data collection period (no longer available, n = 6; personal reasons, n = 5; falling ill, n = 2), and 5 participants who were withdrawn from the study by the researchers for not complying with study procedures (e.g., not wearing the actigraphy watch or not showing up on time for appointments).