Screen Matters: Cognitive and Behavioral Divergence Between Smartphone-Native and Computer-Native Youth
July 20, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kanan Eldarov
arXiv ID
2508.03705
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
This study explores how different modes of digital interaction -- namely, computers versus smartphones -- affect attention, frustration, and creative performance in adolescents. Using a combination of digital task logs, webcam-based gaze estimation, and expert evaluation of task outcomes, we analyzed data from a diverse sample of 824 students aged 11-17. Participants were assigned to device groups in a randomized and stratified design to control for age, gender, and prior experience. Results suggest moderate but statistically significant differences in sustained attention, perceived frustration, and creative output. These findings indicate that the nature of digital interaction -- beyond mere screen time -- may influence cognitive and behavioral outcomes relevant to educational design. Practical implications for user interface development and learning environments are discussed.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted