[Air-L] aWEAR'16 Conference on wearable technologies, knowledge development, and learning, Nov 14-15, 2016, Stanford University
sreckojoksimovic at yahoo.com
Mon May 30 16:01:56 PDT 2016
CALL FOR PARTICIPATION - 2016 aWEAR Conference
November 14-15, 2016
Stanford University, Stanford, California, US
Abstract Submission Deadline: 31 July, 2016
Author Notification: mid-August, 2016
Format: 500 word abstracts (https://easychair.org/conferences/?conf=awear16)
About the Conference
aWEAR: The First International Conference on wearable technologies, knowledge development, and learning
The rapid development of mobile phones has contributed to increasingly personal engagement with our technology. Building on the success of mobile, wearables (watches, smart clothing, clinical-grade bands, fitness trackers, VR) are the next generation of technologies offering not only new communication opportunities, but more importantly, new ways to understand ourselves, our health, our learning, and personal and organizational knowledge development.
Wearables hold promise to greatly improve personal learning and the performance of teams and collaborative knowledge building through advanced data collection. For example, predictive models and learner profiles currently use log and clickstream data. Wearables capture a range of physiological and contextual data that can increase the sophistication of those models and improve learner self-awareness, regulation, and performance.
When combined with existing data such as social media and learning management systems, sophisticated awareness of individual and collaborative activity can be obtained. Wearables are developing quickly, including hardware such as fitness trackers, clothing, earbuds, contact lens and software, notably for integration of data sets and analysis.
The 2016 aWEAR (awear.interlab.me) conference is the first international wearables in learning and education conference. It will be held at Stanford University and provide researchers and attendees with an overview of how these tools are being developed, deployed, and researched. Attendees will have opportunities to engage with different wearable technologies, explore various data collection practices, and evaluate case studies where wearables have been deployed.
This conference will appeal to individuals in K-12, higher education, corporate learning, and existing technology companies, including startups. In addition to sharing emerging research, the conference will take a hands-on approach to exploring wearable technologies, including pilot and prototype developments.
Topics of interest to the conference include, but are not limited to:
- Bridging the gap between humans and technology
- Wearable technology in the classroom
- Wearable technology in online educational settings
- Scaling wearable technology for education and learning
- Collecting and processing data about learning and learning context from wearable devices
- Collaboration and connectivity using wearable technology for education and learning
- Wearables and virtual reality in learning
- Using wearable technology to support student mental and physical wellbeing
- Institutional adoption of wearable technology in the classroom
- Physiological data collection: analyses and implications for learning and education
- Prototypes and early stage pilots of wearables in classroom, blended and online settings, including schools, higher education, and corporate learning
- Contextual and ambient computing (internet of things, sensors, smart glasses) in learning and education
- Quantified self: wearables to improve self-regulation
- User experience in self/institutional surveillance
- Openness: algorithms, technologies, and learner models
- Integration of wearable with existing social media, learning management systems, student information systems, and related technologies
- Face recognition and emotion detection through automated video
- Non-touch sensor interaction with hardware, software, and knowledge elements
- Ethics of physiological data collection and analysis.
This conference is organized by LINK Research Lab (University of Texas, Arlington), Stanford University, and University of Edinburgh.
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