Skip to main content
SpringerLink
Log in

The Potential of the Internet of Things for Supporting Learning and Training in the Digital Age

  • Chapter
  • First Online:
Positive Learning in the Age of Information

Abstract

The rapid progress in the development of information and communication technologies opens new opportunities in education, which go hand in hand with new risks that may be difficult to foresee. Our aim here is to focus mainly on the Internet of Things and related technologies, in order to investigate how they can improve this field. We claim a proper analysis and interpretation of the big educational data can enable more precise personalization and adaptation of learning and training experiences, in order to make them more effective, efficient and attractive. Nevertheless, it will require new approaches to implement novel tools and services for more effective knowledge acquisition, deeper learning and skill training, which can take place in authentic settings and stimulate motivation of learners.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  • Aroyo, L., Dolog, P., Houben, G. J., Kravčík, M., Naeve, A., Nilsson, M., & Wild, F. (2006). Interoperability in personalized adaptive learning. Journal of Educational Technology & Society, 9(2), 4–18.

    Google Scholar 

  • Atif, Y., Mathew, S. S., & Lakas, A. (2015) Building a smart campus to support ubiquitous learning. Journal of Ambient Intelligence and Humanized Computing, 6(2), 223–238.

    Google Scholar 

  • Broll, G., Rukzio, E., Paolucci, M., Wagner, M., Schmidt, A., & Hussmann, H. (2009). PERCI: Pervasive Service Interaction with the Internet of Things. IEEE Internet Computing, 13(6), 74–81.

    Google Scholar 

  • Bull, S., & Kay, J. (2010). Open Learner Models. In R. Nkambou, J. Bourdeau & R. Mizoguchi (Eds.), Advances in Intelligent Tutoring Systems (pp. 301–322). Berlin: Springer.

    Google Scholar 

  • Carr, N. (2011). The shallows: What the Internet is doing to our brains. New York: Norton.

    Google Scholar 

  • Colbeck, J. (2017). I’ll be Watching You: Policing the Line between Personalization and Privacy. Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (p. 2). ACM.

    Google Scholar 

  • De Bra, P. (2017). After Twenty-Five Years of User Modeling and Adaptation… What Makes us UMAP? Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (p. 1). ACM.

    Google Scholar 

  • Dias Pereira dos Santos, A., Yacef, K., & Martinez-Maldonado, R. (2017). Let’s Dance: How to Build a User Model for Dance Students Using Wearable Technology. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 183–191). ACM.

    Google Scholar 

  • Doidge, N. (2007). The brain that changes itself: Stories of personal triumph from the frontiers of brain science. New York: Penguin.

    Google Scholar 

  • Greenfield, P. M. (2009). Technology and informal education: What is taught, what is learned. Science, 323(5910), 69–71.

    Google Scholar 

  • Guest, W., Wild, F., Vovk, A., Fominykh, M., Limbu, B., Klemke, R., Sharma, P., Karjalainen, J., Smith, C., Rasool, J., Aswat, S., Helin, K., Di Mitri, D., & Schneider, J. (2017). Affordances for Capturing and Re-enacting Expert Performance with Wearables. In Proceedings of the 12th European Conference for Technology-Enhanced Learning (ECTEL). Springer.

    Google Scholar 

  • ITU (International Telecommunication Union). (2012). Internet of Things Global Standards Initiative. Retrieved from http://www.itu.int/en/ITU-T/gsi/iot/Pages/default.aspx 19 May 2017.

  • Kay, J., & Kummerfeld, B. (2012). Creating personalized systems that people can scrutinize and control: Drivers, principles and experience. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(4), 24.

    Google Scholar 

  • Koren, I., & Klamma, R. (2015). Smart Ambient Learning with Physical Artifacts Using Wearable Technologies. In D. Preuveneers (Ed.), Workshop Proceedings of the 11th International Conference on Intelligent Environments (pp. 325–332). IOS

    Google Scholar 

  • Kravčík, M., & Klamma, R. (2014). Self-Regulated Learning Nudges. In Proceedings of the First International Workshop on Decision Making and Recommender Systems (DMRS2014) (pp. 52–54). CEUR Vol. 1278.

    Google Scholar 

  • Kravčík, M., Mikroyannidis, A., Pammer, V., Prilla, M., & Ullmann, T. D. (2017). Editorial. In Awareness and Reflection in Technology Enhanced Learning [Special issue], International Journal of Technology Enhanced Learning, 9(2), (pp. 99–102). Inderscience.

    Google Scholar 

  • Kravčík, M., Neulinger, K., & Klamma, R. (2016a). Data analysis of workplace learning with BOOST. In Proceedings of the Workshop on Learning Analytics for Workplace and Professional Learning (LA for work). In conjunction with the 6th International Learning Analytics and Knowledge Conference, Edinburgh, UK (pp. 25–29).

    Google Scholar 

  • Kravčík, M., Nicolaescu P., Siddiqui A., & Klamma R. (2016b) Adaptive Video Techniques for Informal Learning Support in Workplace Environments. In T. T. Wu, R. Gennari, Y. M. Huang, H. Xie & Y. Cao (Eds.), Emerging Technologies for Education: First International Symposium, SETE 2016, Held in Conjunction with ICWL 2016, Rome, Italy, October 26-29, 2016, Revised Selected Papers (pp. 533–543). Cham: Springer.

    Google Scholar 

  • Kummerfeld, B., & Kay, J. (2017). User Modeling for the Internet of Things. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 367–368). ACM.

    Google Scholar 

  • Li, Y., Chang, M., Kravčík, M., Popescu, E., Huang, R., & Chen, N. S. (2016). State-of-the- Art and Future Directions of Smart Learning. Singapore: Springer.

    Google Scholar 

  • Miorandi, D., Sicari, S., Pellegrini, F. D., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10, 1497–1516.

    Google Scholar 

  • Naeve, A., Yli-Luoma, P., Kravcik, M., & Lytras, M. D. (2008). A modelling approach to study learning processes with a focus on knowledge creation. International Journal of Technology Enhanced Learning, 1(1-2), 1–34

    Google Scholar 

  • Nussbaumer, A., Scheffel, M., Niemann, K., Kravčík, M., & Albert, D. (2012). Detecting and reflecting learning activities in Personal Learning Environments. In Proc. of the 2 nd Workshop on Awareness and Reflection in Technology-Enhanced Learning (artel12) at European Conference for Technology-Enhanced Learning (pp. 125–131). CEUR Vol. 931.

    Google Scholar 

  • Nussbaumer, A., Kravčík, M., Renzel, D., Klamma, R., Berthold, M., & Albert, D. (2014). A Framework for Facilitating Self-Regulation in Responsive Open Learning Environments. arXiv preprint arXiv:1407.5891.

  • Pardo, A. (2014). Designing Learning Analytics Experiences. In J. Larusson & B. White (Eds.), Learning Analytics. New York, NY: Springer.

    Google Scholar 

  • Raptis, G. E., Katsini, C., Belk, M., Fidas, C., Samaras, G., & Avouris, N. (2017). Using Eye Gaze Data and Visual Activities to Infer Human Cognitive Styles: Method and Feasibility Studies. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 164–173). ACM.

    Google Scholar 

  • Romero, C., & Ventura, S. (2010) Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601–618.

    Google Scholar 

  • Santos, O. C., & Eddy, M. (2017). Modeling Psychomotor Activity: Current Approaches and Open Issues. In Enhanced Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 305–310). ACM.

    Google Scholar 

  • Santos, O. C., Kravčík, M., & Boticario, J. G. (2016). Preface to Special Issue on User Modelling to Support Personalization in Enhanced Educational Settings. International Journal of Artificial Intelligence in Education, 26(3), 809–820.

    Google Scholar 

  • Sawyer, R., Smith, A., Rowe, J., Azevedo, R., & Lester, J. (2017). Enhancing Student Models in Game-based Learning with Facial Expression Recognition. In Proceedings of the 25 th Conference on User Modeling, Adaptation and Personalization (pp. 192–201). ACM.

    Google Scholar 

  • Schneider, J., Börner, D., van Rosmalen, P., & Specht, M. (2016). Can you help me with my pitch? Studying a tool for real-time automated feedback. IEEE Transactions on Learning Technologies, 9(4), 318–327.

    Google Scholar 

  • Tapscott, D., & Tapscott, A. (2016). Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World. New York: Penguin.

    Google Scholar 

  • Tapscott, D., & Tapscott, A. (2017). The Blockchain Revolution & Higher Education. Educause Review, 52(2), 11–24.

    Google Scholar 

  • Thomas, A., Shah, H., Moore, P., Rayson, P., Wilcox, A., Osman, K., Evans, C., Chapman, C., Athwal, C., While, D., Pham, H., & Mount, S. (2012). E-Education 3.0: Challenges and Opportunities for the Future of iCampuses. In Proceedings of the Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (pp. 953–958). IEEE.

    Google Scholar 

  • Ullrich, C., Aust, M., Dietrich, M., Herbig, N., Igel, C., Kreggenfeld, N., Prinz, C., Raber, F., Schwantzer, S., & Sulzmann, F. (2016). APPsist Statusbericht: Realisierung einer Plattform für Assistenz-und Wissensdienste für die Industrie 4.0. In Proceedings of DeLFI Workshop (pp. 174–180). CEUR Vol. 1669.

    Google Scholar 

  • Ullrich, C., Aust, M., Kreggenfeld, N., Kahl, D., Prinz, C., & Schwantzer, S. (2015). Assistance- and knowledge-services for smart production. In Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business (pp. 40). ACM.

    Google Scholar 

  • Unger, M., Shapira, B., Rokach, L., & Bar, A. (2017). Inferring Contextual Preferences Using Deep Auto-Encoding. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 221–229). ACM.

    Google Scholar 

  • Wahlster, W. (2014). Semantic Technologies for Mass Customization. In W. Wahlster, H.-J. Grallert, S. Wess, H. Friedrich & T. Widenka, T. (Eds.), Towards the internet of ser-vices. The THESEUS research program (pp. 3–13). Cham: Springer.

    Google Scholar 

  • Yu, Z., Liang, Y., Xu, B., Yang, Y., & Guo, B. (2011). Towards a Smart Campus with Mobile Social Networking. In Proceedings of the International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing (pp. 162–169). IEEE.

    Google Scholar 

  • Zlatkin-Troitschanskaia et al. (2017). Positive Learning in the Age of Information. Unpublished Manuscript, Draft Proposal Cluster of Excellence, Johannes Gutenberg University Mainz.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. German Research Center for Artificial Intelligence Berlin, Berlin, Germany

    Miloš Kravčík, Carsten Ullrich & Christoph Igel

Authors
  1. Miloš Kravčík
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carsten Ullrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph Igel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Miloš Kravčík or Carsten Ullrich .

Editor information

Editors and Affiliations

  1. Lehrstuhl für Wirtschaftspädagogik, Johannes Gutenberg-University Mainz, Mainz, Rheinland-Pfalz, Germany

    Olga Zlatkin-Troitschanskaia

  2. Goethe-University, Frankfurt am Main, Germany

    Gabriel Wittum

  3. German Research Center for Artificial Intelligence, Kaiserslautern, Germany

    Andreas Dengel

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Fachmedien Wiesbaden GmbH

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kravčík, M., Ullrich, C., Igel, C. (2018). The Potential of the Internet of Things for Supporting Learning and Training in the Digital Age. In: Zlatkin-Troitschanskaia, O., Wittum, G., Dengel, A. (eds) Positive Learning in the Age of Information. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-19567-0_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-19567-0_24

  • Published:

  • Publisher Name: Springer VS, Wiesbaden

  • Print ISBN: 978-3-658-19566-3

  • Online ISBN: 978-3-658-19567-0

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation