Construction of the UXAR-CT -- a User eXperience Questionnaire for Augmented Reality in Corporate Training
November 19, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Stefan Graser, Martin Schrepp, Stephan BΓΆhm
arXiv ID
2411.12288
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Measuring User Experience (UX) with questionnaires is essential for developing and improving products. However, no domain-specific standardized UX questionnaire exists for Augmented Reality (AR) in Corporate Training (CT). Thus, this study introduces the UXAR-CT questionnaire - an AR-specific UX questionnaire for CT environments. We describe the construction procedure and the evaluation process of the questionnaire. A set of candidate items was constructed, and a larger sample of participants evaluated several AR-based learning scenarios with these items. Based on the results, we performed a Principal Component Analysis (PCA) to identify relevant measurement items for each scale. The three best-fitting items were selected based on the results to form the final questionnaire. The first results regarding scale quality indicate a high level of internal consistency. The final version of the UXAR-CT questionnaire is provided and will be evaluated in further research.
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