Using Knowledge Space Theory to support Learner Modeling and Personalization PROCEEDINGS
Owen Conlan, Ian O'Keeffe, Cormac Hampson, Trinity College, Dublin, Ireland ; Jurgen Heller, 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), Chesapeake, VA
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 September 21, 2018 from https://www.learntechlib.org/primary/p/23994/.
© 2006 Association for the Advancement of Computing in Education (AACE)
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