Towards improving the e-learning experience for deaf students: e-LUX
November 27, 2019 Β· Declared Dead Β· π Lecture Notes in Computer Science, Springer, 2014, Universal access in human-computer interaction: universal access to information and knowledge, 8514 (2), pp.221-232
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Fabrizio Borgia, Claudia S. Bianchini, Maria de Marsico
arXiv ID
1911.13231
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CL
Citations
0
Venue
Lecture Notes in Computer Science, Springer, 2014, Universal access in human-computer interaction: universal access to information and knowledge, 8514 (2), pp.221-232
Last Checked
5 months ago
Abstract
Deaf people are more heavily affected by the digital divide than many would expect. Moreover, most accessibility guidelines addressing their needs just deal with captioning and audio-content transcription. However, this approach to the problem does not consider that deaf people have big troubles with vocal languages, even in their written form. At present, only a few organizations, like W3C, produced guidelines dealing with one of their most distinctive expressions: Sign Language (SL). SL is, in fact, the visual-gestural language used by many deaf people to communicate with each other. The present work aims at supporting e-learning user experience (e-LUX) for these specific users by enhancing the accessibility of content and container services. In particular, we propose preliminary solutions to tailor activities which can be more fruitful when performed in one's own "native" language, which for most deaf people, especially younger ones, is represented by national SL.
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