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Community Learning Analytics with Industry 4.0 and Wearable Sensor Data

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Immersive Learning Research Network (iLRN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 725))

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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.

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Notes

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Acknowledgements

The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme project “Learning Layers” (grant no. 318209) and the European Union’s Horizon 2020 Programme through the project “WEKIT” (grant no. 687669).

Author information

Authors and Affiliations

  1. Advanced Community Information Systems (ACIS) Group, RWTH Aachen University, Ahornstr. 55, 52056, Aachen, Germany

    István Koren & Ralf Klamma

Authors
  1. István Koren
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  2. Ralf Klamma
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Corresponding authors

Correspondence to István Koren or Ralf Klamma .

Editor information

Editors and Affiliations

  1. University of Arkansas, Fayetteville, AR, USA

    Dennis Beck

  2. University of St Andrews, St Andrews, UK

    Colin Allison

  3. Universidade Aberta, Coimbra, Portugal

    Leonel Morgado

  4. Institute of Information Systems and Computer Media, Graz University of Technology, Graz, Austria

    Johanna Pirker

  5. Department of Computer Science, California Polytechnic State University, San Luis Obispo, CA, USA

    Foaad Khosmood

  6. Salish Kootenai College, Pablo, MT, USA

    Jonathon Richter

  7. Graz University of Technology, Graz, Austria

    Christian Gütl

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Koren, I., Klamma, R. (2017). Community Learning Analytics with Industry 4.0 and Wearable Sensor Data. In: Beck, D., et al. Immersive Learning Research Network. iLRN 2017. Communications in Computer and Information Science, vol 725. Springer, Cham. https://doi.org/10.1007/978-3-319-60633-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-60633-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60632-3

  • Online ISBN: 978-3-319-60633-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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