Combining ITS and eLearning Technologies: Opportunities and Challenges

  • Christopher Brooks
  • Jim Greer
  • Erica Melis
  • Carsten Ullrich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)

Abstract

The development of Intelligent Tutoring Systems (ITS) and eLearning systems has been progressing largely independently over the past several years. Both types of systems have strengths and weaknesses – ITSs are typically domain specific and rely on concise knowledge modeling and learner modeling, while eLearning systems are deployable in a wide range of circumstances and focus on connecting learners both to content and to one another. This paper provides possibilities for convergence of these two areas, and describes two of our experiences in providing an ITS-style approach to eLearning systems.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brusilovsky, P.: Adaptive hypermedia. User Modeling and User Adapted Interaction 11(1/2), 87–110 (2001)MATHCrossRefGoogle Scholar
  2. 2.
    Melis, E., Andrès, J.B.E., Frischauf, A., Goguadze, G., Libbrecht, P., Pollet, M., Ullrich, C.: Knowledge Representation and Management in ActiveMath. Annals of Mathematics and Artificial Intelligence 38(1-3), 47–64 (2003)MATHGoogle Scholar
  3. 3.
    Mohan, P., Greer, J.: E-learning specifications in the context of instructional planning. In: Hoppe, U., Verdejo, F., Kay, J. (eds.) AI in Education, AIED-2003, pp. 307–314. IOS Press, Amsterdam (2003)Google Scholar
  4. 4.
    Melis, E., Andrès, E., Büdenbender, J., Frischauf, A., Goguadze, G., Libbrecht, P., Pollet, M., Ullrich, C.: ActiveMath: A Generic and Adaptive Web-Based Learning Environment. International Journal of Artificial Intelligence in Education 12(4), 385–407 (2001)Google Scholar
  5. 5.
    Brooks, C., Kettel, L., Hansen, C., Greer, J.: Building a learning object content management system. In: Proceedings of World Conference on E-Learning in Corporate, Healthcare, & Higher Education (E-Learn 2005), Vancouver, Canada (2005)Google Scholar
  6. 6.
    Sampson, D., Karagiannidis, C.: Kinshuk: Personalized learning: Educational, technological and standardization perspectives. Interactive Educational Multimedia (2002)Google Scholar
  7. 7.
    IEEE Learning Technology Standards Committee: 1484.12.1-2002 IEEE standard for Learning Object Metadata (2002)Google Scholar
  8. 8.
    IMS Global Learning Consortium: IMS learning design specification (2003)Google Scholar
  9. 9.
    IMS Global Learning Consortium: IMS content packaging information model (2003)Google Scholar
  10. 10.
    IMS Global Learning Consortium: IMS simple sequencing specification (2003)Google Scholar
  11. 11.
    und Manfred Schweres, K.U.L.: E-Learning braucht Kontinuität. Mehr nicht? Telepolis (2004)Google Scholar
  12. 12.
    Martin, J.D., VanLehn, K.: OLAE: Progress toward a multi-activity, bayesian student modeler. In: Brna, P., Ohlsson, S., Pain, H. (eds.) Proceedings of the World Conference on Artificial Intelligence in Education, Edinburgh, Scotland, pp. 410–417. AACE (1993)Google Scholar
  13. 13.
    Shute, V.: SMART: Student modeling approach for responsive tutoring. User Modeling and User-Adapted Interaction 5, 1–44 (1995)CrossRefGoogle Scholar
  14. 14.
    Melis, E., Goguadze, G., Homik, M., Libbrecht, P., Ullrich, C., Winterstein, S.: Semantic-Aware Components and Services of ActiveMath. British Journal of Educational Technology 37(3), 405–423 (2006)CrossRefGoogle Scholar
  15. 15.
    Ullrich, C.: Course Generation Based on HTN Planning. In: Jedlitschka, A., Brandherm, B. (eds.) Proceedings of 13th Annual Workshop of the SIG Adaptivity and User Modeling in Interactive Systems, Saarbrücken, Germany, pp. 74–79 (2005)Google Scholar
  16. 16.
    Goguadze, G., Palomo, A.G., Melis, E.: Interactivity of Exercises in ActiveMath. In: Proceedings of the 13th International Conference on Computers in Education (ICCE 2005), Singapore, pp. 107–113 (2005)Google Scholar
  17. 17.
    Winter, M., Brooks, C., Greer, J.: Towards best practices for semantic web student modeling. In: Looi, C.K., McCalla, G. (eds.) Proceedings of the 12th International Conference on Artificial Intelligence in Education AIED 2005. IOS Press, Amsterdam (2005)Google Scholar
  18. 18.
    Brooks, C., McCalla, G.: Flexible learning object metadata. International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) (to appear, 2006)Google Scholar
  19. 19.
    Friesen, N.: Final report on the international lom survey. Technical Report 36C087, Canadian Avisory Commiittee for ISO/IEC JTC1/SC36 (2004)Google Scholar
  20. 20.
    Ullrich, C.: Tutorial Planning: Adapting Course Generation to Today’s Needs. In: Grandbastian, M. (ed.) Young Researcher Track Proceedings of 12th International Conference on Artificial Intelligence in Education, Amsterdam, The Netherlands, pp. 155–160 (2005)Google Scholar
  21. 21.
    Brusilovsky, P.: A component-based distributed architecture for adaptive web-based education. In: Hoppe, U., Verdejo, F., Kay, J. (eds.) AI in Education, AIED 2003, pp. 386–388. IOS Press, Amsterdam (2003)Google Scholar
  22. 22.
    Brooks, C.H., Winter, M., Greer, J.E., McCalla, G.I.: The Massive User Modelling System (MUMS). In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 635–645. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christopher Brooks
    • 1
  • Jim Greer
    • 1
  • Erica Melis
    • 2
  • Carsten Ullrich
    • 2
  1. 1.Advanced Research in Intelligent Educational Systems (ARIES)University of SaskatchewanSaskatoonCanada
  2. 2.German Research Center for Artificial Intelligence (DFKI)SaarbrückenGermany

Personalised recommendations