The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Using Device Detection Techniques in M-Learning Scenarios
|
|
Author(s): Ricardo Queirós (CRACS and ESEIG/IPP, Porto, Portugal)and Mário Pinto (ESEIG/IPP, Porto, Portugal)
Copyright: 2013
Pages: 17
Source title:
Innovations in XML Applications and Metadata Management: Advancing Technologies
Source Author(s)/Editor(s): José Carlos Ramalho (Universidade do Minho, Portugal), Alberto Simões (Universidade do Minho, Portugal)and Ricardo Queirós (CRACS & INESC-Porto LA, Faculdade de Ciências, Universidade do Porto, Portugal)
DOI: 10.4018/978-1-4666-2669-0.ch007
Purchase
|
Abstract
Recent studies of mobile Web trends show the continued explosion of mobile-friend content. However, the wide number and heterogeneity of mobile devices poses several challenges for Web programmers, who want automatic delivery of context and adaptation of the content to mobile devices. Hence, the device detection phase assumes an important role in this process. In this chapter, the authors compare the most used approaches for mobile device detection. Based on this study, they present an architecture for detecting and delivering uniform m-Learning content to students in a Higher School. The authors focus mainly on the XML device capabilities repository and on the REST API Web Service for dealing with device data. In the former, the authors detail the respective capabilities schema and present a new caching approach. In the latter, they present an extension of the current API for dealing with it. Finally, the authors validate their approach by presenting the overall data and statistics collected through the Google Analytics service, in order to better understand the adherence to the mobile Web interface, its evolution over time, and the main weaknesses.
Related Content
|
Ruizhe Ma, Azim Ahmadzadeh, Soukaina Filali Boubrahimi, Rafal A Angryk.
© 2019.
19 pages.
|
|
Zhen Hua Liu.
© 2019.
25 pages.
|
|
Lubna Irshad, Zongmin Ma, Li Yan.
© 2019.
25 pages.
|
|
Hao Jiang, Ahmed Bouabdallah.
© 2019.
22 pages.
|
|
Gbéboumé Crédo Charles Adjallah-Kondo, Zongmin Ma.
© 2019.
22 pages.
|
|
Safa Brahmia, Zouhaier Brahmia, Fabio Grandi, Rafik Bouaziz.
© 2019.
20 pages.
|
|
Zhangbing Hu, Li Yan.
© 2019.
20 pages.
|
|
|