Shared Boundary Interfaces: can one fit all? A controlled study on virtual reality vs touch-screen interfaces on persons with Neurodevelopmental Disorders
April 24, 2024 Β· Declared Dead Β· π InteracciΓ³n
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Francesco Vona, Eleonora Beccaluva, Marco Mores, Franca Garzotto
arXiv ID
2404.15970
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
InteracciΓ³n
Last Checked
4 months ago
Abstract
Technology presents a significant educational opportunity, particularly in enhancing emotional engagement and expanding learning and educational prospects for individuals with Neurodevelopmental Disorders (NDD). Virtual reality emerges as a promising tool for addressing such disorders, complemented by numerous touchscreen applications that have shown efficacy in fostering education and learning abilities. VR and touchscreen technologies represent diverse interface modalities. This study primarily investigates which interface, VR or touchscreen, more effectively facilitates food education for individuals with NDD. We compared learning outcomes via pre- and post-exposure questionnaires. To this end, we developed GEA, a dual-interface, user-friendly web application for Food Education, adaptable for either immersive use in a head-mounted display (HMD) or non-immersive use on a tablet. A controlled study was conducted to determine which interface better promotes learning. Over three sessions, the experimental group engaged with all GEA games in VR (condition A), while the control group interacted with the same games on a tablet (condition B). Results indicated a significant increase in post-questionnaire scores across subjects, averaging a 46% improvement. This enhancement was notably consistent between groups, with VR and Tablet groups showing 42% and 41% improvements, respectively.
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