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Supporting Content Contextualization in Web Based Applications on Mobile Devices
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Media Technology. (CeLeKT)ORCID iD: 0000-0001-9062-1609
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), 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 computer science and media technology (CM), Department of Media Technology. (CeLeKT)ORCID iD: 0000-0002-6937-345X
2013 (English)In: Proceedings of the 9th International Conference on Web Information Systems and Technologies: Aachen, Germany, 8-10 May, 2013 / [ed] Karl-Heinz Krempels, Alexander Stocker, SciTePress, 2013, p. 501-504Conference paper, Published paper (Refereed)
Abstract [en]

Mobile devices, in the form of smartphones, are endowed with rich capabilities in terms of multimedia, sensors and connectivity. The wide adoption of these devices allows using them across different settings and situations. One area in which mobile devices become more and more prominent is within the field of mobile learning. Here, mobile devices provide rich possibilities for the contextualization of the learner, by using the set of sensors available in the device. On the one hand, the usage of mobile devices enables participation in learning activities independent of time and space. Nevertheless, developing mobile learning applications for the heterogeneity of mobile devices available in the market becomes a challenge. Not only this is a problem related to form factor aspects, but also the large number of different operating systems, platforms and app infrastructures (app stores) are aspects to be considered. In this paper we present our initial efforts with regard to the development of cross-platform mobile applications to support the contextualization of learning content. 

Place, publisher, year, edition, pages
SciTePress, 2013. p. 501-504
Keywords [en]
mobile learning applications, context detection, cross platform development, content adaptation.
National Category
Computer Sciences Interaction Technologies
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
URN: urn:nbn:se:lnu:diva-25996DOI: 10.5220/0004405005010504Scopus ID: 2-s2.0-84887093051ISBN: 978-989-8565-54-9 (print)OAI: oai:DiVA.org:lnu-25996DiVA, id: diva2:624540
Conference
9th International Conference on Web Information Systems and Technologies (WEBIST), May 8-10, 2013, Aachen
Available from: 2013-06-01 Created: 2013-06-01 Last updated: 2020-04-03Bibliographically approved
In thesis
1. A Computational Approach for Modelling Context across Different Application Domains
Open this publication in new window or tab >>A Computational Approach for Modelling Context across Different Application Domains
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

 Nowadays, people use a wide range of devices (e.g., mobile phones, smart watches, tablets, activity bands, laptops) to access different digital applications and services. The ubiquitous distribution of these devices allows them to be used across different settings, in different situations, and in a large number of different domains. These devices contain a variety of hardware features (e.g., sensors, Internet connectivity, camera, low energy Bluetooth connectivity) that allow for gathering diverse data types that can be used in many application domains. Among other areas, they could be utilized in mobile learning situations (e.g., for data collection in science education, field trips), to support mobile health (e.g., for health data collection, monitoring the health states of patients, monitoring for changes in health conditions and/or detection of emergency situations), and to provide personalised recommendations (e.g., for recommending services based on the user’s location and time). These devices help to capture the current contextual situation of the user, which could make applications more personalised in order to generate novel services and to deliver a better user experience. However, most applications lack capturing the user’s context situation or have been often limited to the user’s current location and time. Therefore, new ways of conceptualising and processing contextual information are necessary in order to support the development of personalised and contextualised applications and services. Substantial research in the field of contextualisation has explored aspects related to computational modelling of context focusing on just one specific application domain. Most of the existing context models do not address the issue of generalization as being a core feature of the model. Thus, the model is to a particular application domain or scenario. The main goal of this thesis is to conceptualise, design and validate an approach for a unified context model and to investigate its applicability in different application domains. This thesis presents the state of the art of recent approaches used for context modelling and it introduces a rich context model as an approach for modelling context in a domain-independent way. Reusability and flexibility of the proposed rich context model are illustrated by showing several applications domains (e.g., mobile learning, recommender systems, data analytics, eHealth) in which the model has been tested. This work explains the promising potential of using rich context models to support the personalization of services that are tied to the user’s current context. The results and outcomes of this work pave the way for new opportunities and further research related to the integration and combination of the proposed rich context model with machine learning techniques.

Place, publisher, year, edition, pages
Växjö: Linnaeus University Press, 2020. p. 130
Series
Linnaeus University Dissertations ; 382/2020
Keywords
context modelling, multidimensional vector space model, contextualisation, recommender systems, rich context model
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Media Technology; Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-93251 (URN)978-91-89081-52-9 (ISBN)978-91-89081-53-6 (ISBN)
Public defence
2020-04-21, Wicksell, Hus K, Växjö, 09:00 (English)
Opponent
Supervisors
Available from: 2020-04-03 Created: 2020-04-03 Last updated: 2020-05-13Bibliographically approved

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Sotsenko, AlisaJansen, MarcMilrad, Marcelo

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