Our approach is a comprehensive and evolutionary sociotechnological learning analytics and design process leading to a flexible infrastructure where professional communities can co-create their wearable enhanced learning solution.
Thesis Type |
|
Student |
Suchi Julidayani |
Status |
Finished |
Submitted in |
2021 |
Proposal on |
25/05/2021 12:35 am |
Proposal room |
|
Presentation on |
21/12/2021 1:00 am |
Supervisor(s) |
Ralf Klamma Stefan Decker |
Advisor(s) |
Benedikt Hensen |
Contact |
hensen@dbis.rwth-aachen.de |
Nowadays, we can use immersive interaction and display technologies in collaborative analytical reasoning and decision making scenarios. In order to support heterogeneous professional communities of practice in their digital transformation, it is necessary not only to provide the technologies but to understand the work practices under transformations as well as the security, privacy and other concerns of the communities. Our approach is a comprehensive and evolutionary sociotechnological learning analytics and design process leading to a flexible infrastructure where professional communities can co-create their wearable enhanced learning solution. At the core, we have a multi-sensory fusion recorder and player that allows the recordings of multi-actor activity sequences by human activity recognition and the computational support of immersive learning analytics to support training scenarios. The approach shall enable cross-domain collaboration by fusing, aggregating and visualizing sensor data coming from wearables and modern production systems. The software shall be open source and shall be based on the outcomes of several national and international funded projects.
Strong knowledge of Javascript, Java/C#/C++
Knowledge in Unity, Augmented/Virtual Reality
Interest in Mixed Reality Hardware and Software, Wearables, Web Technology
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