The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
An Adaptive and Context-Aware Scenario Model Based on a Web Service Architecture for Pervasive Learning Systems
|
|
Author(s): Cuong Pham-Nguyen (TELECOM, France), Serge Garlatti (TELECOM, France), B.-Y.-Simon Lau (Multimedia University, Malaysia), Benjamin Barbry (University of Sciences and Technologies of Lille, France)and Thomas Vantroys (University of Sciences and Technologies of Lille, France)
Copyright: 2009
Volume: 1
Issue: 3
Pages: 29
Source title:
International Journal of Mobile and Blended Learning (IJMBL)
Editor(s)-in-Chief: David Parsons (The Mind Lab by Unitec, New Zealand)and Kathryn Mac Callum (University of Canterbury, Christchurch, New Zealand)
DOI: 10.4018/jmbl.2009092203
Purchase
|
Abstract
Pervasive learning will become increasingly important in technology-enhanced learning (TEL). In this context, development efforts focus on features such as context-awareness, adaptation, services retrieval and orchestration mechanisms. This paper proposes a process to assist the development of such systems, from conception through to execution. This paper focuses mainly on pervasive TEL systems in a learning situation at the workplace. We introduce a context-aware scenario model of corporate learning and working scenarios in e-retail environments such as shops and hypermarkets. This model enables us to integrate contextual information into scenarios and to select how to perform activities according to the current situation. Our pervasive learning system is based on a service oriented architecture that consists of an infrastructure for service management and execution that is flexible enough to reuse learning components and to deal with context changes that are not known in advance and discovered on the fly.
Related Content
|
W. A. Piyumi Udeshinee, Ola Knutsson, Sirkku Männikkö-Barbutiu.
© 2024.
21 pages.
|
|
Shatha Mohammed Almalki.
© 2024.
19 pages.
|
|
Lin Wang, Muhd Khaizer Omar, Noor Syamilah Zakaria, Nurul Nadwa Zulkifli.
© 2024.
19 pages.
|
|
Daniel Biedermann, Patrick Oliver Schwarz, Jane Yau, Hendrik Drachsler.
© 2023.
12 pages.
|
|
Almed Hamzah, Sergey Sosnovsky.
© 2023.
16 pages.
|
|
Qiwei Men, Belinda Gimbert, Dean Cristol.
© 2023.
17 pages.
|
|
Olga Viberg, Agnes Kukulska-Hulme, Ward Peeters.
© 2023.
15 pages.
|
|
|