scholarly journals Exploring patterns of technology use in UK college students: a cluster analysis of learners’ digital practices

2019 ◽  
Vol 24 (1) ◽  
pp. 20-36
Author(s):  
Rhona Sharpe ◽  
Qi Wu ◽  
Metaxia Pavlakou
Author(s):  
Susan E. Kotowski ◽  
Kermit G. Davis

Over the past year, the Covid-19 pandemic has led to a switch from a majority of classes being taught in-person to a majority being taught online. The switch has led to an increase in the amount of time students are utilizing technology for learning purposes. This study assessed how technology use has changed during the pandemic, particularly related to laptop use, and the postures students work in and the discomfort they’re experiencing while participating in online learning. The results of the survey (n=1,074) found that laptop use is up significantly (used the majority of the time by 70.2% of students), students are working in poor postures (up to 80% working with deviated neck postures), and are experiencing high levels of discomfort (up to ~60% reporting moderate/extreme discomfort in their upper extremities). The results bring to light the urgent need to provide ergonomics education and training for designing good work environments.


SLEEP ◽  
2019 ◽  
Vol 43 (6) ◽  
Author(s):  
Dorothee Fischer ◽  
Andrew W McHill ◽  
Akane Sano ◽  
Rosalind W Picard ◽  
Laura K Barger ◽  
...  

Abstract Study Objectives Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric—the Composite Phase Deviation (CPD)—to assess mistiming and irregularity of both sleep and event schedules to examine their relationship with self-reported well-being in US college students. Methods Daily well-being, actigraphy, and timing of sleep and first scheduled events (academic/exercise/other) were collected for approximately 30 days from 223 US college students (37% females) between 2013 and 2016. Participants rated well-being daily upon awakening on five scales: Sleepy–Alert, Sad–Happy, Sluggish–Energetic, Sick–Healthy, and Stressed–Calm. A longitudinal growth model with time-varying covariates was used to assess relationships between sleep variables (i.e. CPDSleep, sleep duration, and midsleep time) and daily and average well-being. Cluster analysis was used to examine relationships between CPD for sleep vs. event schedules. Results CPD for sleep was a significant predictor of average well-being (e.g. Stressed–Calm: b = −6.3, p < 0.01), whereas sleep duration was a significant predictor of daily well-being (Stressed–Calm, b = 1.0, p < 0.001). Although cluster analysis revealed no systematic relationship between CPD for sleep vs. event schedules (i.e. more mistimed/irregular events were not associated with more mistimed/irregular sleep), they interacted upon well-being: the poorest well-being was reported by students for whom both sleep and event schedules were mistimed and irregular. Conclusions Sleep regularity and duration may be risk factors for lower well-being in college students. Stabilizing sleep and/or event schedules may help improve well-being. Clinical Trial Registration NCT02846077.


NASPA Journal ◽  
2006 ◽  
Vol 43 (2) ◽  
Author(s):  
Erin L Gemmill ◽  
Michael Peterson

The purposes of this study were to explore the extent to which technology disrupts and occupies the time of a college student and to determine the degree to which these disruptions contribute to perceived stress. A 71-item survey to assess perceived stress, technology use and disruptions, and social support was administered to 299 undergraduate students. The results indicate 25% of participants have problems with disruptions from technology, and more disruptions from technology are related to higher levels of perceived stress. Experiencing disruptions from technology is a significant problem among college students and needs to be addressed by student affairs professionals.


2013 ◽  
Vol 411-414 ◽  
pp. 2173-2176
Author(s):  
Wei An ◽  
Qi Hua Liu

With the development of the Internet, more and more customers buy apparel online. This paper investigated Chinese college students lifestyle, and on this basis, the Chinese college students were divided into four categories with the method of factor analysis and cluster analysis, namely, Individualistic Shopper, Fashion Follower, Price-sensitive Shopper, and Fashion Shopper. After that, this paper researched which kind of apparel products these four styles of college students have purchased online and believe suitable to purchase online.


2017 ◽  
Vol 2 (2) ◽  
pp. 73-79
Author(s):  
Ryan D. Burns ◽  
You Fu ◽  
Timothy A. Brusseau ◽  
Nora Constantino

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