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Using Knowledge Space Theory to support Learner Modeling and Personalization

, , , Trinity College, Dublin, Ireland ; , University of Graz, Austria

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Honolulu, Hawaii, USA ISBN 978-1-880094-60-0 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA


A learner's knowledge is often the key aspect towards which personalized eLearning systems attempt to adapt. However, the assessment of their knowledge usually involves tedious and time consuming questionnaires or making stereotypical assumptions about what they know. The Knowledge Space Theory (KST) [Doignon and Falmagne, 1985; Albert and Held, 1999] offers a means of efficiently and effectively determining the current knowledge of a learner. By applying this theory to the analysis and determination of a learner's knowledge, highly configurable adaptive systems, such as APeLS [Conlan et al. 2002], can provide highly dynamic event driven personalized adaptations based on up-to-date information about the learner. This paper describes the culmination of collaborative research that has been carried out by Knowledge and Data Engineering Group of Trinity College, Dublin and the Cognitive Science Section of the University of Graz under the auspices of several European Commission funded projects.


Conlan, O., O'Keeffe, I., Hampson, C. & Heller, J. (2006). Using Knowledge Space Theory to support Learner Modeling and Personalization. In T. Reeves & S. Yamashita (Eds.), Proceedings of E-Learn 2006--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1912-1919). Honolulu, Hawaii, USA: Association for the Advancement of Computing in Education (AACE). Retrieved February 17, 2019 from .


View References & Citations Map


  1. Albert, D. & Held, T. (1994). Establishing knowledge spaces by systematical problem construction. In D. Albert (Ed.), Knowledge Structures (pp. 78–112). New York: Springer Verlag.
  2. Albert, D., & Held, T. (1999). Component Based Knowledge Spaces in Problem Solving and Inductive Reasoning. In D. Albert& J. Lukas (Eds.), Knowledge Spaces: Theories, Empirical Research, and Applications (pp. 15–40).
  3. [Doignon and Falmagne, 1999] Doignon, J.-P., & Falmagne, J.-C. (1999). Knowledge spaces. Berlin; Heidelberg ; New York: Springer.
  4. [Dün tsch and Gediga 1995] Düntsch, I., & Gediga, G. (1995). Skills and Knowledge Structures. British Journal of Mathematical and Statistical Psychology, 48, 9–27.
  5. [Leclercq and Poumay, 2003] Leclercq D. & Poumay, M. (2003). Analyses édumétriques et indices métacognitifs appliqués aux questios des 10 check-up MOHICAN. In D. Leclercq (2003), Diagnostic cognitive et métacogntif au seuil de l’université. Editions de l’université de Liège.
  6. [Muehlenbrock et al., 2005] Muehlenbrock, M., Winterstein, S., Andres, E., & Meier, A. (2005). Continuous Learner Modelling in iClass. In Proceedings of the World Conference on Educational Multimedia Hypermedia& Telecommunications ED-MEDIA 2005.
  7. [O’Keeffe et al., 06] O'Keeffe, I., Brady, A., Conlan, O., Wade, V. (2006) "Just-in-time Generation of Pedagogically Sound, Context Sensitive Personalized Learning Experiences", International Journal on E-Learning (IJeL), Special Issue: Learning Objects in Context, Volume 5, Issue 1, pp 113-127. Chesapeake, VA: AACE. (2006)

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