Recommending in Inclusive Lifelong Learning Scenarios: Identifying and Managing Runtime Situations

Author

Santos, Olga C.

Author_Institution

aDeNu Res. Group, Artificial Intell. Dept., Comput. Sci. Sch., Madrid

Volume

3

fYear

2008

fDate

9-12 Dec. 2008

Firstpage

651

Lastpage

654

Abstract

Supporting learners in inclusive lifelong learning scenarios requires a dynamic support that takes into account their learning needs and preferences. This research is focused on an open standard-based recommender system, covering the full life cycle of eLearning. The recommending system I am developing is supported by a multi-agent architecture and its ultimate goal is to improve the learning efficiency and the learnerspsila satisfaction during the execution of the course tasks.

Keywords

continuing professional development; information filters; intelligent tutoring systems; multi-agent systems; inclusive lifelong e-learning scenario; multiagent architecture; open standard-based recommender system; runtime situation identification; runtime situation management; Artificial intelligence; Computer science; Conference management; Educational institutions; Electronic learning; Intelligent agent; Performance evaluation; Recommender systems; Runtime; Technology management; kiviat figures; learning efficiency; lifelong learning; recommending systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on

Conference_Location

Sydney, NSW

Print_ISBN

978-0-7695-3496-1

Type

conf

DOI

10.1109/WIIAT.2008.251

Filename

4740863