Interoperability in Personalized Adaptive Learning
Lora Aroyo
Technische Universiteit Eindhoven, Computer Science – Information Systems, PO Box 513, 5600 MB Eindhoven, the Netherlands, l.m.aroyo@tue.nl
Peter Dolog
L3S Research Center, University of Hannover, Expo Plaza 1, 30539 Hannover, Germany, dolog@l3s.de
Geert-Jan Houben
Vrije Universiteit Brussel, Computer Science, Pleinlaan 2, 1050 Brussels, Belgium, Geert-Jan.Houben@vub.ac.be
Milos Kravcik
Fraunhofer FIT, Institute for Applied Information Technology, Schloss Birlinghoven, 53754 Sankt Augustin, Germany, Milos.Kravcik@fit.fraunhofer.de
Ambjörn Naeve
Knowledge Management Research (KMR) Group, Centre for user oriented IT-Design (CID), Royal Institute of Technology (KTH), 100 44 Stockholm, Sweden, amb@nada.kth.se
Mikael Nilsson
Knowledge Management Research (KMR) Group, Centre for user oriented IT-Design (CID), Royal Institute of Technology (KTH), 100 44 Stockholm, Sweden, mini@nada.kth.se
Fridolin Wild
Vienna University of Economics, and Business Administration, Institute of Information Systems and New Media, Augasse 2-6, 1090 Vienna, Austria, Fridolin.Wild@wu-wien.ac.at
ABSTRACT: Personalized adaptive learning requires semantic-based and context-aware systems to manage the Web knowledge efficiently as well as to achieve semantic interoperability between heterogeneous information resources and services. The technological and conceptual differences can be bridged either by means of standards or via approaches based on the Semantic Web. This article deals with the issue of semantic interoperability of educational contents on the Web by considering the integration of learning standards, Semantic Web, and adaptive technologies to meet the requirements of learners. Discussion is m ade on the state of the art and the main challenges in this field, including metadata access and design issues relating to adaptive learning. Additionally, a way how to integrate several original approaches is proposed.
Keywords: Semantic Interoperability, Learning Standards, Personalized Adaptive Learning, Meta-Data
[406 Kb]