Translation of overlay models of student knowledge for relative domains based on domain ontology mapping

Sergey Sosnovsky, Peter Dolog, Nicola Henze, Peter Brusilovsky, Wolfgang Nejdl

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


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.
Original languageEnglish
Title of host publicationArtificial Intelligence in Education - Building Technology Rich Learning Contexts That Work
EditorsRosemary Luckin, Kenneth R. Koedinger, Jim Greer
Number of pages8
PublisherIOS Press
Publication date2007
ISBN (Print)978-1-58603-764-2
Publication statusPublished - 2007
EventAIED 2007: Artificial Intelligence in Education - Marina Del Rey, CA, United States
Duration: 9 Jul 200713 Jul 2007
Conference number: 13


ConferenceAIED 2007: Artificial Intelligence in Education
Country/TerritoryUnited States
CityMarina Del Rey, CA


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