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Using Device Detection Techniques in M-Learning Scenarios

Using Device Detection Techniques in M-Learning Scenarios

Ricardo Queirós, Mário Pinto
Copyright: © 2014 |Pages: 16
ISBN13: 9781466645028|ISBN10: 1466645024|EISBN13: 9781466645035
DOI: 10.4018/978-1-4666-4502-8.ch069
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MLA

Queirós, Ricardo, and Mário Pinto. "Using Device Detection Techniques in M-Learning Scenarios." K-12 Education: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2014, pp. 1188-1203. https://doi.org/10.4018/978-1-4666-4502-8.ch069

APA

Queirós, R. & Pinto, M. (2014). Using Device Detection Techniques in M-Learning Scenarios. In I. Management Association (Ed.), K-12 Education: Concepts, Methodologies, Tools, and Applications (pp. 1188-1203). IGI Global. https://doi.org/10.4018/978-1-4666-4502-8.ch069

Chicago

Queirós, Ricardo, and Mário Pinto. "Using Device Detection Techniques in M-Learning Scenarios." In K-12 Education: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1188-1203. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4502-8.ch069

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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.

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