Surf or sleep?

Abstract

Poor sleep habits may cause serious problems of mind and body, and it is a commonly observed issue for college students due to study workload as well as peer and social influence. Understanding its impact and identifying students with poor sleep habits matters a lot in educational management. Most of the current researches is either based on self-reports and questionnaires, suffering from small sample size and social desirability bias, or the methods used are not suitable for the education system. In this paper, we develop a general data-driven method for identifying students' sleep patterns according to their Internet access pattern stored in the education management system and explore its influence from various aspects. First, we design a Possion-based probabilistic mixture model to cluster students according to the distribution of bedtime and identify students who are used to stay up late. Second, we profile students from five aspects (including eight dimensions) based on campusbehavior data and build Bayesian networks to explore the relationship between behavioral characteristics and sleeping habits. Finally, we test the predictability of sleeping habits. This paper not only contributes to the understanding of student sleep from a cognitive and behavioral perspective but also presents a new approach that provides an effective framework for various educational institutions to detect the sleeping patterns of students.

Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review

“…Highlight the potential for sleep metrics from wearable devices to provide novel insights into data generated from a large cohort [ 34 - 36 ]…”

“…Although sleep tracking apps such as SleepAsAndroid were widely downloaded and used by millions of users, their ability to distinguish between quiet awakenings, deep sleep, or empty beds was still limited [ 20 ]. In a related vein, studies by Jayarajah et al [ 31 ] and Faust et al [ 36 ] have defined sleep time as the longest period during which there was no activity on users’ smartphones. However, the authors acknowledged that this method was not reliable because not everyone had the habit of using smartphones until they fell asleep.…”

“…Along with sleep quality metrics, researchers have considered a wide range of contextual factors in their studies. As presented in Table 3 , the contextual factors of interest include the sleep environment [ 16 , 20 , 42 ], daily activities [ 16 , 19 , 20 , 31 , 36 , 37 , 40 , 42 , 43 ], physiological states [ 16 , 35 , 36 , 43 , 44 ], and mental states [ 16 , 19 ]. It was a common practice to collect demographic information (eg, age, gender, and BMI) and medical history using questionnaires at the beginning of a sleep tracking study [ 16 , 35 , 36 , 43 , 44 ].…”

“…Other contextual factors were recorded during the data collection experiments. Researchers have been curious about how web activities of university students could be coupled with their sleep quality [ 31 , 34 , 36 ]. These studies developed their own tools to track users’ web-based behavior and linked them to sleep quality metrics.…”

“…These studies developed their own tools to track users’ web-based behavior and linked them to sleep quality metrics. Using consumer wearable trackers such as Fitbit, MS Band, and Garmin, researchers were able to expand their list of factors to include, for example, bedtime, steps, distance, hours of exercise, and calories burned [ 16 , 19 , 20 , 31 , 36 , 37 , 40 , 42 , 43 ]. Biosignals such as heart rate during sleep and daytime activities were included in the studies by Faust et al [ 35 ], Farajtabar et al [ 43 ], and Choi et al [ 44 ].…”