Survey on Context-Aware Pervasive Learning Environments
Dublin Core
Title
Survey on Context-Aware Pervasive Learning Environments
Subject
context-aware
literature survey
mobile learning
pervasive learning environment
Description
Context-aware pervasive learning environments consist of interconnected, embedded computing devices such as portable computers, wireless sensors, auxiliary input/output devices and servers. Until this study there has been no survey that has evaluated and presented information regarding these environments. In this paper, we conducted a survey to identify the commonly used technologies, methods and models behind these systems, and evaluated the role of mobile devices in the reviewed papers. As a result, we made five observations: (i) RFID was the most common sensor technology; (ii) several learning models were suggested, but none was validated properly; (iii) client-server architectures are prevalent in the systems and mobile devices were used most commonly to represent information; (iv) most of the systems supported multiple simultaneous users, but few facilitated virtual communication; and (v) possible roles for physical environments in pervasive learning systems are: contexts for learning, content for learning, and system resources. Evidence indicates that suitable learning models have yet to be validated, and that more roles of mobile devices could be emphasised.
Creator
Laine, Teemu Henrikki
Joy, Mike
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 3 No. 1 (2009); 70-76
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2008-11-10
Rights
Copyright (c) 2017 Teemu Henrikki Laine, Mike Joy
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Teemu Laine Henrikki and Mike Joy, Survey on Context-Aware Pervasive Learning Environments, International Association of Online Engineering (IAOE), Vienna, Austria, 2008, accessed November 6, 2024, https://igi.indrastra.com/items/show/873