Personalized E-Learning through Environment Design and Collaborative Activities

  • Felix Mödritscher
  • Fridolin Wild
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5298)

Abstract

Over the last century, many theoretical frameworks and technological solutions for personalized e-learning have emerged. The underlying models, however, are often based on the practice that domain experts develop an adaptation strategy to personalize content or parts of a learning platform, which leads to different problematic aspects decreasing the feasibility or utility of such approaches. After giving a brief overview of the historical development and basic concepts of personalized e-learning, we outline the shortcomings of the traditional ‘top-down, ex ante’ models and present an alternative approach which deals with personal learning environments, web application mashups, learning activities and learner interactions, as well as pattern-based best practice sharing. Furthermore, a prototypic implementation for our ‘learner-driven, bottom-up’ approach to personalized e-learning, namely the ‘Mash-UP Personal Learning Environment’ (MUPPLE), is presented and discussed on the basis of a concrete scenario.

Keywords

Personal Learning Environments Learning Environment Design Learner Interaction Scripting End-User Development 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Heller, J., Steiner, C., Hockemeyer, C., Albert, D.: Competence-Based Knowledge Structures for Personalised Learning. International Journal on E-Learning 5(1), 75–88 (2006)Google Scholar
  2. 2.
    Sampson, D., Karagiannidis, C.: Kinshuk: Personalised Learning: Educational, Technological and Standardisation Perspective. Interactive Educational Multimedia 4, 24–39 (2002)Google Scholar
  3. 3.
    Park, O., Lee., J.: Adaptive Instructional Systems. In: Jonassen, D.H. (ed.) Handbook of Research on Educational Communications and Technology, pp. 651–684. Lawrence Erlbaum Associates, Mahwah (2004)Google Scholar
  4. 4.
    Shute, V., Towle, B.: Adaptive E-Learning. Educational Psychologist 38(2), 105–114 (2003)CrossRefGoogle Scholar
  5. 5.
    Conlan, O.: The Multi-Model, Metadata Driven Approach to Personalised eLearning Services. University of Dublin, Dublin (2005)Google Scholar
  6. 6.
    Mödritscher, F.: Adaptive E-Learning Environments: Theory, Practice, and Experience. VDM, Saarbrücken (2008)Google Scholar
  7. 7.
    Brusilovsky, P.: KnowledgeTree: A Distributed Architecture for Adaptive E-Learning. In: Proceedings of the World Wide Web Conference (WWW), pp. 104–113 (2004)Google Scholar
  8. 8.
    Henze, N., Nejdl, W.: A Logical Characterization of Adaptive Educational Hypermedia. New Review of Hypermedia and Multimedia 10, 77–113 (2004)CrossRefGoogle Scholar
  9. 9.
    Specht, M., Burgos, D.: Modeling Adaptive Educational Methods with IMS Learning Design. Journal of Interactive Media in Education (Adaptation and IMS Learning Design), 1–13 (2007)Google Scholar
  10. 10.
    Henze, N., Dolog, P., Nejdl, W.: Reasoning and Ontologies for Personalized E-Learning in the Semantic Web. Educational Technology & Society 7(4), 82–97 (2004)Google Scholar
  11. 11.
    Chen, C.-M., Lee, H.-M., Chen, Y.-H.: Personalized e-learning system using Item Response Theory. Computers & Education 44(3), 237–255 (2005)CrossRefGoogle Scholar
  12. 12.
    Graf, S., Lin, T., Kinshuk, J.: The relationship between learning styles and cognitive traits - Getting additional information for improving student modelling. Computers in Human Behavior 24(2), 122–137 (2008)CrossRefGoogle Scholar
  13. 13.
    Helic, D.: A Didactics-Aware Approach to Management of Learning Scenarios in ELearning Systems. Graz University of Technology, Graz (2006)Google Scholar
  14. 14.
    Mödritscher, F., García-Barrios, V.M., Gütl, C.: Enhancement of SCORM to support adaptive E-Learning within the Scope of the Research Project AdeLE. In: Proceedings of the World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education (E-Learn), pp. 2499–2505 (2004)Google Scholar
  15. 15.
    Phillips, M.: Cognitive Style’s influence on Media Preference: Does it matter or do they know? In: Proceedings of the World Conference on Educational Multimedia, Hypermedia & Telecommunications (ED-MEDIA), pp. 1023–1028 (2005)Google Scholar
  16. 16.
    Holzinger, A., Kickmeier-Rust, M., Albert, D.: Dynamic Media in Computer Science Education; Content Complexity and Learning Performance: Is Less More? Educational Technology & Society 11(1), 279–290 (2008)Google Scholar
  17. 17.
    Rollett, H., Lux, M., Strohmaier, M., Dösinger, G., Tochtermann, K.: The Web 2.0 way of learning with technologies. International Journal of Learning Technology 3(1), 87–107 (2007)CrossRefGoogle Scholar
  18. 18.
    Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction (UMUAI) 6(2-3), 87–129 (1996)CrossRefMATHGoogle Scholar
  19. 19.
    Gütl, C., Mödritscher, F.: Towards a Generic Adaptive System applicable for Web-based Learning Management Environments. In: Proceedings of the Annual Workshop of the SIG Adaptivity and User Modeling in Interactive Systems (ABIS), pp. 26–31 (2005)Google Scholar
  20. 20.
    Van Harmelen, M.: Personal Learning Environments. In: Proceedings of the International Conference on Advanced Learning Technologies (ICALT), pp. 815–816 (2006)Google Scholar
  21. 21.
    Wild, F., Sigurdarson, S.E.: Distributed Feed Networks for Learning. European Journal for the Informatics Professional (UPGRADE) 9(3) (2008)Google Scholar
  22. 22.
    Santos, O.C., Boticario, J.G.: Meaningful pedagogy via covering the entire life cycle of adaptive eLearning in terms of a pervasive use of educational standards: The aLFanet experience. In: Tomadaki, E., Scott, P. (eds.) EC-TEL 2006. LNCS, vol. 4227, pp. 691–696. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  23. 23.
    Mödritscher, F.: e-Learning Theories in Practice: A Comparison of three Methods. Journal of Universal Science and Technology of Learning (JUSTL) 0(0), 3–18 (2006)Google Scholar
  24. 24.
    O’Reilly, T.: What is Web 2.0. O’Reilly Media Inc. (2005) (2008-07-21), http://www.oreilly.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html
  25. 25.
    Ebner, M., Holzinger, A., Maurer, H.: Web 2.0 Technology: Future Interfaces for Technology Enhanced Learning? In: Stephanidis, C. (ed.) HCI 2007. LNCS, vol. 4556, pp. 559–568. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  26. 26.
    Lathem, J., Gomadam, K., Sheth, A.: SA-REST and (S)mashups: Adding Semantics to RESTful Services. In: Proceedings of the International Conference on Semantic Computing (ICSC 2007), pp. 469–476 (2007)Google Scholar
  27. 27.
    Jones, S., Maiden, N.A.M.: RESCUE: An Integrated Method for Specifying Requirements for Complex Socio-Technical Systems. In: Mate, J.L., Silva, A. (eds.) Requirements Engineering for Socio-Technical Systems, pp. 245–265. Idea Publishing (2004)Google Scholar
  28. 28.
    Kraus, J.: The long tail of software: Millions of Markets of Dozens. Bnoopy: An entrepreneurship blog (2005) (2008-07-21), http://bnoopy.typepad.com/bnoopy/2005/03/the_long_tail_o.html
  29. 29.
    Mödritscher, F., Neumann, G., García-Barrios, V.M., Wild, F.: A Web Application Mashup Approach for eLearning. In: Proceedings of the OpenACS and.LRN Conference, pp. 105–110 (2008)Google Scholar
  30. 30.
    Spoerri, A.: Visual Mashup of Text and Media Search Results. In: Proceedings of the International Conference Information Visualization, pp. 216–222 (2007)Google Scholar
  31. 31.
    García-Barrios, V.M., Mödritscher, F., Gütl, C.: Personalisation versus Adaptation? A User-centred Model Approach and its Application. In: Proceedings of the International Conference on Knowledge Management (I-KNOW), pp. 120–127 (2005)Google Scholar
  32. 32.
    Akhras, F.N., Self, J.A.: System Intelligence in Constructivist Learning. International Journal of Artificial Intelligence in Education 11(4), 344–376 (2000)Google Scholar
  33. 33.
    Kay, J.: Stereotypes, Student Models and Scrutability. In: Proceedings of the International Conference on Intelligent Tutoring Systems (ITS), pp. 19–30 (2000)Google Scholar
  34. 34.
    Boud, D., Keogh, R., Walker, D. (eds.): Reflection: Turning Experience Into Learning. Routledge, Abingdon (1985)Google Scholar
  35. 35.
    Koper, R., Olivier, B.: Representing the Learning Design of Units of Learning. Educational Technology & Society 7(3), 97–111 (2004)Google Scholar
  36. 36.
    Mödritscher, F., García-Barrios, V.M., Gütl, C.: The Past, the Present and the Future of adaptive E-Learning: An Approach within the Scope of the Research Project AdeLE. In: Proceedings of the International Conference on Interactive Computer Aided Learning, ICL (2004)Google Scholar
  37. 37.
    Dillenbourg, P., Jermann, P.: Designing integrative scripts. In: Fischer, F., Mandl, H., Haake, J., Kollar, I. (eds.) Scripting computer-supported collaborative learning: Cognitive, computational and educational perspectives, pp. 277–302. Springer, New York (2007)Google Scholar
  38. 38.
    Wild, F., Mödritscher, F., Sigurdarson, S.E.: Designing for Change: Mash-Up Personal Learning Environments. eLearning Papers 9 (2008)Google Scholar
  39. 39.
    Jackson, C., Wang, H.J.: Subspace: Secure Cross-Domain Communication for Web Mashups. In: Proceedings of the International Conference on World Wide Web (WWW), pp. 611–620 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Felix Mödritscher
    • 1
  • Fridolin Wild
    • 1
  1. 1.Institute for Information Systems and New MediaVienna University of Economics and Business AdministrationViennaAustria

Personalised recommendations