Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Community Learning Analytics with Industry 4.0 and Wearable Sensor Data

  • Conference paper
  • First Online:
Immersive Learning Research Network (iLRN 2017)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://jsplumbtoolkit.com/ last accessed in April, 2017.

  2. 2.

    https://www.polymer-project.org/ last accessed in April, 2017.

  3. 3.

    https://material.io/ last accessed in April, 2017.

  4. 4.

    https://github.com/rwth-acis/SWeVA-Editor-Page/.

References

  1. Bosch: Bosch’s IoT platform (2017). https://www.bosch-si.com/de/iot-plattform/bosch-iot-suite/homepage-bosch-iot-suite.html

  2. Drachsler, H., Hummel, H.G.K., Koper, R.: Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model. Int. J. Learn. Technol. 3(4), 404–423 (2008)

    Article  Google Scholar 

  3. IBM: Watson IoT Platform (2017). https://www.ibm.com/internet-of-things/platform/watson-iot-platform/

  4. Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). doi:10.1007/978-3-540-70956-5_7

    Chapter  Google Scholar 

  5. Klamma, R.: Community learning analytics - challenges and opportunities. In: Wang, J.F., Lau, R.W.H. (eds.) ICWL 2013. LNCS, vol. 8167, pp. 284–293. Springer, Berlin (2013)

    Chapter  Google Scholar 

  6. Klamma, R., Renzel, D., de Lange, P., Janßen, H.: Las2peer - A Primer (2016)

    Google Scholar 

  7. Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)

    Article  Google Scholar 

  8. Mikusz, M., Clinch, S., Jones, R., Harding, M., Winstanley, C., Davies, N.: Repurposing web analytics to support the IoT. Computer 48(9), 42–49 (2015)

    Article  Google Scholar 

  9. Patni, H., Henson, C., Sheth, A.: Linked sensor data. In: 2010 International Symposium on Collaborative Technologies and Systems, pp. 362–370

    Google Scholar 

  10. Ruiz-Calleja, A., Dennerlein, S.M., Ley, T., Lex, E.: Visualizing workplace learning data with the SSS Dashboard. In: Learning Analytics Across Physical and Digital Spaces 2016, CEUR Workshop Proceedings, CrossLAK 2016, vol. 1601. CEUR (2016)

    Google Scholar 

  11. Wenger, E.: Communities of Practice: Learning, Meaning, and Identity. Learning in Doing. Cambridge University Press, Cambridge (1998)

    Book  Google Scholar 

  12. Worsley, M., Blikstein, P.: What’s an Expert? Using learning analytics to identify emergent markers of expertise through automated speech, sentiment and sketch analysis. In: Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero, C., Stamper, J. (eds.) 4th International Conference on Educational Data Mining, pp. 235–240 (2011)

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ralf Klamma
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60632-3

  • Online ISBN: 978-3-319-60633-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation