Advertisement

Sensor Based Interaction Mechanisms in Mobile Learning

  • Kai-Uwe Martin
  • Madlen Wuttke
  • Wolfram Hardt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8524)

Abstract

This contribution discusses the possibilities for mobile interaction and learning, facilitated by the increasing use of sensors in mobile devices. Each sensor provides information which is useful in certain learning contexts and allows for distinct interaction mechanisms. However a model is required how to collect the sensor data and connect it to the learning environment and content. A suitable architecture is described and the steps of the information flow are explained. Future prospects to enhance mobile interaction with more natural ways of communication supported by sensors are given.

Keywords

Collaboration technology and informal learning Mobile and/or ubiquitous learning Personalization user modeling and adaptation in learning technologies Technology enhanced learning sensors context information architecture m-learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Franklin, T.: Mobile Learning: At The Tipping Point. The Turkish Online Journal of Educational Technology 10(4) (2011)Google Scholar
  2. 2.
    Roschelle, J.: Keynote paper: Unlocking the learning value of wireless mobile devices. Journal of Computer Assisted Learning 19(3), 260–272 (2003)CrossRefGoogle Scholar
  3. 3.
    Rensing, C.: Szenarien und Erfahrungen mobilen situierten Lernens an Hochschulen. DeLFI (2012)Google Scholar
  4. 4.
    Sharples, M., Taylor, J., Vavoula, G.: Towards a theory of mobile learning. In: Proceedings of mLearn 2005, vol. 1(1), pp. 1–9 (2005)Google Scholar
  5. 5.
    Gellersen, H.W., Schmidt, A., Beigl, M.: Multi-sensor context-awareness in mobile devices and smart artifacts. Mobile Networks and Applications 7(5), 341–351 (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Taylor, J.: A theory of learning for the mobile age. In: Medienbildung in Neuen Kulturräumen, pp. 87–99. VS Verlag für Sozialwissenschaften (2010)Google Scholar
  7. 7.
    Sarker, S., Wells, J.D.: Understanding mobile handheld device use and adoption. Communications of the ACM 46(12), 35–40 (2003)CrossRefGoogle Scholar
  8. 8.
    Chia, Y., et al.: Context-aware mobile learning with a semantic service-oriented infrastructure. In: 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA), pp. S.896–S.901. IEEE (2011)Google Scholar
  9. 9.
    da Silva, L.C.N., Neto, F.M.M., Júnior, L.J., de Carvalho Muniz, R.: Recommendation of Learning Objects in an Ubiquitous Learning Environment through an Agent-Based Approach. In: Putnik, G.D., Cruz-Cunha, M.M. (eds.) ViNOrg 2011. CCIS, vol. 248, pp. 101–110. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Lester, J.C., Converse, S.A., Kahler, S.E., Barlow, S.T., Stone, B.A., Bhogal, R.S.: The persona effect: affective impact of animated pedagogical agents. In: Pemberton, S. (ed.) Human Factors in Computing Systems: CHI 1997 Conference Proceedings, pp. 359–366. ACM Press, New York (1997)Google Scholar
  11. 11.
    Wuttke, M.: Pro-Active Pedagogical Agents. In: Fakultät für Informatik (ed.) Proceedings of International Summer Workshop Computer Science, pp. 59–62 (July 2013)Google Scholar
  12. 12.
    Wang, S.-L., Wu, C.-Y.: Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with Applications 38(9), S.10831–S.10838 (2011)Google Scholar
  13. 13.
    Hong, J., et al.: Context-aware system for proactive personalized service based on context history. Expert Systems with Applications 36(4), S.7448–S.7457 (2009)Google Scholar
  14. 14.
    Martin, K.-U.: Delivering complex learning content on mobile devices. In: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. S.161–S.166 (2013)Google Scholar
  15. 15.
    Ally, M. (Hg.): Mobile learning: Transforming the delivery of education and training. Athabasca University Press (2009)Google Scholar
  16. 16.
    Eckardt, D., Hettich, G., Schmid, H.-D.: Sensor for measuring physical dimensions and process for balancing the sensor. U.S. Patent Nr. 4,845,649 (1989)Google Scholar
  17. 17.
    Martin, K.-U., Hardt, W.: Adaptive agent supported mobile learning. In: International Summer Workshop Computer Science 2013, vol. 17, p. S.28 (2013)Google Scholar
  18. 18.
    Hinckley, K., et al.: Sensing techniques for mobile interaction. In: Proceedings of the 13th Annual ACM Symposium on User Interface Software and Technology, pp. S.91–S.100. ACM (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kai-Uwe Martin
    • 1
  • Madlen Wuttke
    • 2
  • Wolfram Hardt
    • 1
  1. 1.Computer EngineeringChemnitz University of TechnologyChemnitzGermany
  2. 2.Institute for Media ResearchChemnitz University of TechnologyChemnitzGermany

Personalised recommendations