Supporting Training of Expertise with Wearable Technologies: The WEKIT Reference Framework

Authors: Bibeg Limbu, Mikhail Fominykh, Roland Klemke, Marcus Specht and Fridolin Wild
Type: Book chapter
SourceMobile and Ubiquitous Learning
Publisher: Springer, Singapore
Date: 18 November, 2017
Linkhttps://link.springer.com/chapter/10.1007/978-981-10-6144-8_10

Abstract: In this chapter, we present a conceptual reference framework for designing augmented reality applications for supporting training. The framework leverages the capabilities of modern augmented reality and wearable technology for capturing the expert’s performance in order to support expertise development. It has been designed in the context of Wearable Experience for Knowledge Intensive Training (WEKIT) project which intends to deliver a novel technological platform for industrial training. The framework identifies the state-of-the-art augmented reality training methods, which we term as “transfer mechanisms” from an extensive literature review. Transfer mechanisms exploit the educational affordances of augmented reality and wearable technology to capture the expert performance and train the trainees. The framework itself is based upon Merrienboer’s 4C/ID model which is suitable for training complex skills. The 4C/ID model encapsulates major elements of apprenticeship models which is a primary method of training in industries. The framework complements the 4C/ID model with expert performance data captured with help of wearable technology which is then exploited in the model to provide a novel training approach for efficiently and effectively mastering the skills required. In this chapter, we will give a brief overview of our current progress in developing this framework.

D1.6 Requirements for Scenarios and Prototypes – second version

In this deliverable, we present the outcomes of the follow-up activities of the trials concerning the collection of new requirements. We particularly highlight emerged demands in the areas of usability and analytics. The results are prioritized using the Requirements Bazaar methodology and tool. They are currently fed into the development process by WP2. WP6 is using the outcomes of this deliverables to improve the execution of the next planned trials. The final iteration of this report will be released in the last month of the project.

Read more… “D1.6 Requirements for Scenarios and Prototypes – second version”

Affordances for Capturing and Re-enacting Expert Performance with Wearables

Authors: Will Guest, Fridolin Wild, Alla Vovk, Mikhail Fominykh, Bibeg Limbu, Roland Klemke, Puneet Sharma, Jaakko Karjalainen, Carl Smith, Jazz Rasool, Soyeb Aswat, Kaj Helin, Daniele Di Mitri and Jan Schneider
Type: Conference proceedings
Source: 12th European Conference on Technology Enhanced Learning (ECTEL 2017)
Publisher: Springer, Cham
Date: 05 September, 2017
Linkhttps://link.springer.com/chapter/10.1007%2F978-3-319-66610-5_34

Abstract: The WEKIT.one prototype is a platform for immersive procedural training with wearable sensors and Augmented Reality. Focusing on capture and re-enactment of human expertise, this work looks at the unique affordances of suitable hard- and software technologies. The practical challenges of interpreting expertise, using suitable sensors for its capture and specifying the means to describe and display to the novice are of central significance here. We link affordances with hardware devices, discussing their alternatives, including Microsoft Hololens, Thalmic Labs MYO, Alex Posture sensor, MyndPlay EEG headband, and a heart rate sensor. Following the selection of sensors, we describe integration and communication requirements for the prototype. We close with thoughts on the wider possibilities for implementation and next steps.

D1.5 WEKIT Framework and Training Methodology – second version

The document reports on the status of the WEKIT framework. Building up on the methodologies described in D1.3 “WEKIT Framework & Training Methodology – First version”, 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.

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WEKIT Press release 2 – Industrial Trials 2017

A trans-European team of researchers and developers is transforming industrial learning and training with the use of innovative Augmented Reality and Wearable Technology (AR/WT).

The new WEKIT.one AR system, involving the Hololens and other wearable devices, was recently put into action for the first time when it was tested with 142 experts and trainees at three separate organisations; in Tromsø, halfway to the North Pole in the Arctic circle, and in Turin and Genoa, Italy.

Read more… “WEKIT Press release 2 – Industrial Trials 2017”

Community Learning Analytics with Industry 4.0 and Wearable Sensor Data

Authors: István Koren and Ralf Klamma
Type: Conference proceedings
Source: Third International Conference of the Immersive Learning Research Network (iLRN 2017)
Publisher: Springer, Cham
Date: 26 June, 2017
Linkhttps://link.springer.com/chapter/10.1007%2F978-3-319-60633-0_12

Abstract: Learning analytics in formal learning contexts is often restricted to collect and analyze data from students following curricula through a learning management system. In informal learning, however, a deep understanding of learners and entities interacting with each other is needed. The practice of exploring these interactions is known as community learning analytics. Mobile devices, wearables and interconnected Industry 4.0 production machines equipped with a multitude of sensors collecting vast amounts of data are ideal candidates to capture the goals and activities of informal learning settings. What is missing is a methodological approach to collect, manage, analyze and exploit data coming from such an interconnected network of artifacts. In this paper, we present a concept and prototypical implementation of a framework that is able to gather, transform and visualize data coming from Industry 4.0 and wearable sensors and actuators. Our collaborative Web-based visual analytics platform is highly embeddable and extensible on various levels. Its open source availability fosters research on community learning analytics on a broad level.