Back

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

Yeo, S. C.; Tan, J.; Lai, C. K. Y.; Lim, S.; Chandramoghan, Y.; Fung, F. M.; Chen, P.; Strauman, T. J.; Gooley, J. J.

2021-08-05 neuroscience
10.1101/2021.08.04.455177 bioRxiv
Show abstract

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.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
npj Science of Learning
19 papers in training set
Top 0.1%
18.8%
2
Scientific Reports
3612 papers in training set
Top 2%
12.9%
3
PLOS ONE
5266 papers in training set
Top 26%
6.3%
4
eLife
5828 papers in training set
Top 24%
4.9%
5
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 13%
4.1%
6
Journal of Biological Rhythms
25 papers in training set
Top 0.1%
3.3%
50% of probability mass above
7
Biological Psychiatry Global Open Science
60 papers in training set
Top 0.4%
2.8%
8
Physiology & Behavior
31 papers in training set
Top 0.2%
2.7%
9
Proceedings of the Royal Society B: Biological Sciences
393 papers in training set
Top 3%
2.5%
10
iScience
1154 papers in training set
Top 10%
2.4%
11
Molecular Psychiatry
282 papers in training set
Top 3%
2.4%
12
Journal of Neurophysiology
302 papers in training set
Top 2%
2.4%
13
Frontiers in Behavioral Neuroscience
49 papers in training set
Top 0.4%
2.4%
14
Brain, Behavior, and Immunity
116 papers in training set
Top 0.8%
2.2%
15
Sleep
58 papers in training set
Top 0.5%
1.9%
16
Translational Psychiatry
260 papers in training set
Top 3%
1.4%
17
Sleep Advances
11 papers in training set
Top 0.2%
1.4%
18
Nature Communications
5641 papers in training set
Top 52%
1.1%
19
Current Biology
665 papers in training set
Top 9%
1.1%
20
eneuro
439 papers in training set
Top 7%
1.0%
21
The Journal of Neuroscience
1025 papers in training set
Top 10%
0.9%
22
Frontiers in Neuroscience
256 papers in training set
Top 6%
0.9%
23
Psychological Science
18 papers in training set
Top 0.4%
0.9%
24
PNAS Nexus
159 papers in training set
Top 3%
0.9%
25
Communications Biology
993 papers in training set
Top 29%
0.9%
26
Sleep Medicine
19 papers in training set
Top 0.4%
0.6%
27
Science Advances
1243 papers in training set
Top 32%
0.6%