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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/8389
Title: D1.5 WEKIT Framework and Training Methodology
Authors: Limbu, Bibeg
Keywords: Expertise
Augmented Reality
Sesnors
4CID
Issue Date: 2017
Citation: Limbu, B. (2017). WEKIT D1.5 WEKIT Framework & Training Methodology. Retrieved from http://wekit.eu/category/results/deliverables/
Abstract: The document reports on the status of the WEKIT framework. Building up on the methodologies described in D1.3, it outlines the work done and progress made so far in the Task 1.3. The WEKIT framework was drafted to guide and support the development and implementation of the project. It aims to support the transition of the trainers from the traditional training platform to the WEKIT approach. The framework leverages on the vast knowledge and experience an expert possess. The framework aims to make the experts knowledge and experience available to the trainees by capturing the expert performance. The framework was built by performing an extensive literature review, expert interviews and through active participation of the WEKIT community and stakeholders. Experts that closely collaborated in the WEKIT project were from three completely different domains namely, Aeronautics engineering, Medical Imaging and Astronaut training. An overarching goal of the framework was to be domain independent and abstract, such that it does not rely heavily on domain related expertise. The framework is built by units termed as Transfer mechanisms which are individual learning design methods that exploit expert performance with the help of Augmented reality and Wearable technology. These Transfer mechanisms allow various forms of training to be adapted to the WEKIT approach. The Transfer mechanisms also support components of the Four component instructional design for ensuring optimal learning using the WEKIT methodology. Four component instructional design model defines learning of complex task in a didactic approach where the expert plays a critical role. The framework supplements the model by technology enhanced learning methods that rely on expert performance. The document also reports on current findings and future endeavours that WP1 will take to improve the framework and the project itself.
URI: http://hdl.handle.net/1820/8389
Appears in Collections:3. TELI Deliverables and reports

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