Developing Personal Learning Environments Based on Calm Technologies

  • Jinan Fiaidhi
Part of the Communications in Computer and Information Science book series (CCIS, volume 124)


Educational technology is constantly evolving and growing, and it is inevitable that this progression will continually offer new and interesting advances in our world. The instigation of calm technologies for the delivery of education is another new approach now emerging. Calm technology aims to reduce the "excitement" of information overload by letting the learner select what information is at the center of their attention and what information need to be at the peripheral. In this paper we report on the adaptation of calm technologies in an educational setting with emphasis on the needs to cater the preferences of the individual learner to respond to the challenge of providing truly learner-centered, accessible, personalized and flexible learning. Central to calm computing vision is the notion of representing learning objects as widgets, harvesting widgets from the periphery based on semantic wikis as well as widgets garbage collection from the virtual/central learning memory.


Garbage Collection Mashup Application Personal Learn Environment Enterprise Mashup Metadata Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Horizon Report, The New Media Consortium and EDUCAUSE, ISBN 0-9765087-6-1 (2008),
  2. 2.
    Hinchcliffe, D.: A bumper crop of new mashup platforms, ZDNet Online Magazine (July 23, 2007),
  3. 3.
    Tugui, A.: Calm Technologies in a Multimedia World. ACM Ubiquity 5(4) (2004),
  4. 4.
    Weiser, M.: The Computer for the 21st Century. Scientific American, 94–100 (September 1991),
  5. 5.
    Fiaidhi, J., Mohammed, S.: Design Issues Involved in Using Learning Objects for Teaching a Programming Language within a Collaborative eLearning Environment. International Journal of Instructional Technology and Distance Learning (USA) 1(3), 39–53 (2004)Google Scholar
  6. 6.
    Jung, Y., Lee, J., Kim, M.: Community Computing Model Supporting Community Situation Based Strict Cooperation and Conflict Resolution. In: Obermaisser, R., Nah, Y., Puschner, P., Rammig, F.J. (eds.) SEUS 2007. LNCS, vol. 4761, pp. 47–56. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Kumar, M., et al.: PICO: A Middleware framework for Pervasive Computing. In: Pervasive Computing 1268-1536, pp. 72–79 (2003)Google Scholar
  8. 8.
    Jennings, R.: Developing Multiagent Systems: The Gaia Methodology. ACM Transactions on Software Engineering and Methodology 12(3), 317–370 (2003)CrossRefGoogle Scholar
  9. 9.
    Mohammed, S., Servos, D., Fiaidhi, J.: HCX: A Distributed OSGi Based Web Interaction System for Sharing Health Records in the Cloud. In: 2010 International Workshop on Intelligent Web Interaction (IWI 2010), Affiliated with Web Intelligence 2010 (WI 2010) International Conference, August 31 (2010)Google Scholar
  10. 10.
    Fiaidhi, J., Mohammed, S., Chamarette, L., Thomas, D.: Identifying Middlewares for Mashup Personal Learning Environments. Future Internet Journal 1(1) (2009)Google Scholar
  11. 11.
    Fiaidhi, J., Chou, W., Williams, J.: Mobile Computing in the Context of Calm Technology. IEEE IT-PRO, Editorial Article (May-June, 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jinan Fiaidhi
    • 1
  1. 1.Department of Computer ScienceLakehead UniversityCanada

Personalised recommendations