ArticleEducation and information technologies2023
Digital distraction levels of university students in emergency remote teaching.
Article in Education and information technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Stakeholder Perspectives on Remote Delivery of a Group-Based Child Obesity Prevention Program for Latino Families With Low Income.Journal of nutrition education and behavior · 2026Article
- The digital well-being among learners in higher education: a scoping review.BMC medical education · 2026Article
- The dual system model of distraction: explaining the cognitive mechanism of distraction.Frontiers in psychology · 2025Article
- Postural supporting cervical traction workstation to improve resting state brain activity in digital device users: EEG study.Digital healthArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The main objective of this study is to investigate the relationships between digital distraction, perceived learning, and general satisfaction in emergency remote teaching. Correlational design, one of the quantitative research methods, was used for the study. The study sample consists of 1532 university students in a Turkish university during Covid-19. The study results show a significant relationship between digital distraction, general satisfaction, and perceived learning. Digital distraction is negatively related to general satisfaction and perceived learning, and general satisfaction is positively related to perceived learning. When the independent demographic variables were analyzed, digital distraction scores were higher for females, those not working in any job, not participate orientation training, and not following the live class and watching it later. In addition, it was revealed that as the age of the students decreased, the digital distraction scores increased. It was found that digital distraction variables, the amount of digital distraction, sending instant messages, checking the time, boredom, sharing social media, and system usability were significant predictors of digital distraction.
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Registered trials
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