Organizing Learning Objects for Personalized eLearning Services

  • Naimdjon Takhirov
  • Ingeborg T. Sølvberg
  • Trond Aalberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5714)


In this paper we present a way to organize Learning Objects to achieve personalized eLearning. PEDAL-NG is a system that supports personalization based on the user’s prior knowledge and the learning style in an existing and operational eLearning environment. The prior knowledge assessment and the learning style questionnaire proved to be simple and useful tools to gather necessary information about the user in order to deliver personalized eLearning experience.


Learn Object Digital Library Learning Style Metadata Schema Learn Style Model 
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.


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  1. 1.
    Gu, Q., Chica, S., Ahmad1, F., Khan, H., Sumner, T., Martin, J.H., Butcher, K.: Personalizing the Selection of Digital Library Resources to Support Intentional Learning. In: Christensen-Dalsgaard, B., Castelli, D., Ammitzbøll Jurik, B., Lippincott, J. (eds.) ECDL 2008. LNCS, vol. 5173, pp. 244–255. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Tyler-Smith, K.: Early attrition among first time elearners: A review of factors that contribute to drop-out, withdrawal and non-completion rates of adult learners undertaking elearning programmes. Journal of Online Learning and Teaching 2(2) (2006)Google Scholar
  3. 3.
    Kyung-Sun, K., Moore, J.L.: Web-based learning: Factors affecting students’ satisfaction and learning experience. First Monday 10(11) (2005)Google Scholar
  4. 4.
    Bchner, A., Patterson, D.: Personalised e-learning opportunities - call for a pedagogical domain knowledge model. In: Proc. of 15th International Workshop on Database and Expert Systems Applications, Zaragoza, Spain (2004)Google Scholar
  5. 5.
    Boticario, J.G., Santos, O., Rosmalen, P.: Issues developing standard-based adaptive learning management systems. Paper for the EADTU 2005 Working Conference: Towards Lisbon 2010: Collaboration for Innovative Content in Lifelong Open and Learning (2005)Google Scholar
  6. 6.
    Maurice, D.M., Anand, S.S., Bchner, A.G.: Personalization on the net using web mining: introduction. Communications of the ACM 43(8) (2000)Google Scholar
  7. 7.
    Conlan, O., Wade, V., Bruen, C., Gargan, M.: Multi-model, Metadata Driven Approach to Adaptive Hypermedia Services for Personalized eLearning. In: Proc. of AH and Adaptive Web-Based Systems (2002)Google Scholar
  8. 8.
    Fleming, N.: VARK. A guide to learning styles (2008),
  9. 9.
    Takhirov, N., Sølvberg, I.: Adaptive personalized eLearning on top of existing LCMS. In: Proc. of Joint Conference on Digital Libraries, JCDL 2009, Austin, TX, USA (2009)Google Scholar
  10. 10.
    Takhirov, N.: Adaptive personalized eLearning. Master thesis, Norwegian University of Science and Technology, Trondheim, Norway (2008),
  11. 11.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles. Engineering Education, 78(7) (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Naimdjon Takhirov
    • 1
  • Ingeborg T. Sølvberg
    • 1
  • Trond Aalberg
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway

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