Abstract
The effectiveness of an adaptive educational system in many respects depends on the precision of modeling assumptions it makes about a student. One of the well-known challenges in student modeling is to adequately assess the initial level of student's knowledge when s/he starts working with a system. Sometimes potentially handful data are available as a part of user model from a system used by the student before. The usage of external user modeling information is troublesome because of differences in system architecture, knowledge representation, modeling constraints, etc. In this paper, we argue that the implementation of underlying knowledge models in a sharable format, as domain ontologies - along with application of automatic ontology mapping techniques for model alignment - can help to overcome the .new-user. problem and will greatly widen opportunities for student model translation. Moreover, it then becomes possible for systems from relevant domains to rely on knowledge transfer and reuse those portions of the student models that are related to overlapping concepts.

URL:
http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2007-016

BibTeX:

@inproceedings{REWERSE-RP-2007-016,
	author = {Sergey Sosnovsky and Peter Dolog and Nicola Henze and Peter Brusilovsky and Wolfgang Nejdl},
	title = {Translation of Overlay Models of Student Knowledge for Relative Domains Based on Domain Ontology Mapping},
	booktitle = {Proceedings of 13th International Conference on Artificial Intelligence in Education, Marina Del Rey, CA, USA (9th--13th July 2007)},
	year = {2007},
	volume = {158},
	series = {Frontiers in Artificial Intelligence and Applications},
	pages = {289--296},
	url = {http://rewerse.net/publications/rewerse-publications.html#REWERSE-RP-2007-016}
}