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

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

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

Abstract

Originalsprog	Engelsk
Titel	Artificial Intelligence in Education - Building Technology Rich Learning Contexts That Work
Redaktører	Rosemary Luckin, Kenneth R. Koedinger, Jim Greer
Antal sider	8
Vol/bind	158
Forlag	IOS Press
Publikationsdato	2007
ISBN (Trykt)	978-1-58603-764-2
Status	Udgivet - 2007
Begivenhed	AIED 2007: Artificial Intelligence in Education - Marina Del Rey, CA, USA Varighed: 9 jul. 2007 → 13 jul. 2007 Konferencens nummer: 13

Konference

Konference	AIED 2007: Artificial Intelligence in Education
Nummer	13
Land/Område	USA
By	Marina Del Rey, CA
Periode	09/07/2007 → 13/07/2007

Adgang til dokumentet

http://www.cs.aau.dk/~dolog/pub/aied2007.pdf

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

EU/IST FP6 NoE Prolearn
Dolog, P. (Projektdeltager)
Information Sociaty Technologies
01/09/2006 → 31/12/2007
Projekter: Projekt › Forskning

Citationsformater

Sosnovsky, S., Dolog, P., Henze, N., Brusilovsky, P., & Nejdl, W. (2007). Translation of overlay models of student knowledge for relative domains based on domain ontology mapping. I R. Luckin, K. R. Koedinger, & J. Greer (red.), Artificial Intelligence in Education - Building Technology Rich Learning Contexts That Work (Bind 158). IOS Press. http://www.cs.aau.dk/~dolog/pub/aied2007.pdf

@inproceedings{5fa654707eec11dc8139000ea68e967b,

title = "Translation of overlay models of student knowledge for relative domains based on domain ontology mapping",

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.",

author = "Sergey Sosnovsky and Peter Dolog and Nicola Henze and Peter Brusilovsky and Wolfgang Nejdl",

year = "2007",

language = "English",

isbn = "978-1-58603-764-2",

volume = "158",

editor = "Rosemary Luckin and Koedinger, \{Kenneth R.\} and Jim Greer",

booktitle = "Artificial Intelligence in Education - Building Technology Rich Learning Contexts That Work",

publisher = "IOS Press",

address = "Netherlands",

note = "AIED 2007: Artificial Intelligence in Education ; Conference date: 09-07-2007 Through 13-07-2007",

}

Sosnovsky, S, Dolog, P, Henze, N, Brusilovsky, P & Nejdl, W 2007, Translation of overlay models of student knowledge for relative domains based on domain ontology mapping. i R Luckin, KR Koedinger & J Greer (red), Artificial Intelligence in Education - Building Technology Rich Learning Contexts That Work. bind 158, IOS Press, AIED 2007: Artificial Intelligence in Education, Marina Del Rey, CA, USA, 09/07/2007. <http://www.cs.aau.dk/~dolog/pub/aied2007.pdf>

Translation of overlay models of student knowledge for relative domains based on domain ontology mapping. / Sosnovsky, Sergey; Dolog, Peter; Henze, Nicola et al.
Artificial Intelligence in Education - Building Technology Rich Learning Contexts That Work. red. / Rosemary Luckin; Kenneth R. Koedinger; Jim Greer. Bind 158 IOS Press, 2007.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Translation of overlay models of student knowledge for relative domains based on domain ontology mapping

AU - Sosnovsky, Sergey

AU - Dolog, Peter

AU - Henze, Nicola

AU - Brusilovsky, Peter

AU - Nejdl, Wolfgang

N1 - Conference code: 13

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

M3 - Article in proceeding

SN - 978-1-58603-764-2

VL - 158

BT - Artificial Intelligence in Education - Building Technology Rich Learning Contexts That Work

A2 - Luckin, Rosemary

A2 - Koedinger, Kenneth R.

A2 - Greer, Jim

PB - IOS Press

T2 - AIED 2007: Artificial Intelligence in Education

Y2 - 9 July 2007 through 13 July 2007

ER -

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

Abstract

Konference

Adgang til dokumentet

AUB Link

Fingeraftryk

Projekter

EU/IST FP6 NoE Prolearn

Citationsformater