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Flexible and Contextualized Cloud Applications for Mobile Learning Scenarios
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0001-9062-1609
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0002-6893-0461
Linnaeus University, Faculty of Technology, Department of Media Technology. University of Applied Sciences Ruhr West, Germany. (CeLeKT)ORCID iD: 0000-0001-7072-1063
Linnaeus University, Faculty of Technology, Department of Media Technology. (CeLeKT)ORCID iD: 0000-0002-6937-345X
2016 (English)In: Mobile, Ubiquitous, and Pervasive Learning: Fundaments, Applications, and Trends / [ed] Alejandro Peña-Ayala, Springer Publishing Company, 2016, p. 167-192Chapter in book (Refereed)
Abstract [en]

This chapter describes our research efforts related to the design of mobile learning (m-learning) applications in cloud-computing (CC) environments. Many cloud-based services can be used/integrated in m-learning scenarios, hence, there is a rich source of applications that could easily be applied to design and deploy those within the context of cloud-based services. Here, we present two cloud-based approaches—a flexible framework for an easy generation and deployment of mobile learning applications for teachers, and a flexible contextualization service to support personalized learning environment for mobile learners. The framework provides a flexible approach that supports teachers in designing mobile applications and automatically deploys those in order to allow teachers to create their own m-learning activities supported by mobile devices. The contextualization service is proposed to improve the content delivery of learning objects (LOs). This service allows adapting the learning content and the mobile user interface (UI) to the current context of the user. Together, this leads to a powerful and flexible framework for the provisioning of potentially ad hoc mobile learning scenarios. We provide a description of the design and implementation of two proposed cloud-based approaches together with scenario examples. Furthermore, we discuss the benefits of using flexible and contextualized cloud applications in mobile learning scenarios. Hereby, we contribute to this growing field of research by exploring new ways for designing and using flexible and contextualized cloud-based applications that support m-learning.

Place, publisher, year, edition, pages
Springer Publishing Company, 2016. p. 167-192
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 406
Keywords [en]
Mobile learning, Contextualization, Contextualized service, Cloud computing, Cloud-based services, Context modeling
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
URN: urn:nbn:se:lnu:diva-49569DOI: 10.1007/978-3-319-26518-6_7Scopus ID: 2-s2.0-84966270917ISBN: 978-3-319-26516-2 (print)OAI: oai:DiVA.org:lnu-49569DiVA, id: diva2:900525
Available from: 2016-02-04 Created: 2016-02-04 Last updated: 2017-02-22Bibliographically approved
In thesis
1. A Rich Context Model: Design and Implementation
Open this publication in new window or tab >>A Rich Context Model: Design and Implementation
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The latest developments of mobile devices include a variety of hardware features that allow for more rich data collection and services. Numerous sensors, Internet connectivity, low energy Bluetooth connectivity to other devices (e.g., smart watches, activity tracker, health data monitoring devices) are just some examples of hardware that helps to provide additional information that can be beneficially used for many application domains. Among others, they could be utilized in mobile learning scenarios (for data collection in science education, field trips), in mobile health scenarios (for health data collection and monitoring the health state of patients, changes in health conditions and/or detection of emergency situations), and in personalized recommender systems. This information captures the current context situation of the user that could help to make mobile applications more personalized and deliver a better user experience. Moreover, the context related information collected by the mobile device and the different applications can be enriched by using additional external information sources (e.g., Web Service APIs), which help to describe the user’s context situation in more details.

The main challenge in context modeling is the lack of generalization at the core of the model, as most of the existing context models depend on particular application domains or scenarios. We tackle this challenge by conceptualizing and designing a rich generic context model. In this thesis, we present the state of the art of recent approaches used for context modeling and introduce a rich context model as an approach for modeling context in a domain-independent way. Additionally, we investigate whether context information can enhance existing mobile applications by making them sensible to the user’s current situation. We demonstrate the reusability and flexibility of the rich context model in a several case studies. The main contributions of this thesis are: (1) an overview of recent, existing research in context modeling for different application domains; (2) a theoretical foundation of the proposed approach for modeling context in a domain-independent way; (3) several case studies in different mobile application domains.

Place, publisher, year, edition, pages
Växjö: Faculty of Technology, Linnaeus University, 2017. p. 103
Series
Reports: Linnaeus University, Faculty of Technology ; 48
Keywords
Context modeling, rich context model, mobile users, current context of the user, mobile sensors, multidimensional vector space model, contextualization
National Category
Computer Systems
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
urn:nbn:se:lnu:diva-60850 (URN)978-91-88357-62-5 (ISBN)
Presentation
2017-02-17, C1202, Växjö, 09:15 (English)
Opponent
Supervisors
Available from: 2017-02-24 Created: 2017-02-22 Last updated: 2017-09-01Bibliographically approved

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Sotsenko, AlisaZbick, JanoschJansen, MarcMilrad, Marcelo

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Citation style
  • apa
  • harvard1
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Output format
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